Review of Biomass Gasification Technologies with a Particular Focus on a Downdraft Gasifier

Abstract

The utilization of biomass as a renewable energy source has the potential to play a role in mitigating climate change. Furthermore, biomass gasification represents a sustainable solution for the management of lignocellulosic waste. Topics related to the different types of gasification reactors, biomass, and economic feasibility, along with tar formation and its removal in the product gas, are discussed as general aspects in the gasification. A detailed analysis of capital and operational expenditures, the net present value, the payback period, and the internal rate of return of downdraft gasifiers has been conducted. A bibliometric analysis has been conducted; the results are presented in the form of visual maps based on keywords, and likely future trends in gasification modeling were identified. Since modeling is crucial to optimize the production or quality of the syngas, this paper discloses some important aspects related to biomass gasification carried out on downdraft gasifiers. The modeling section encompasses a range of approaches, including those based on chemical equilibrium, both stoichiometric and non-stoichiometric, kinetic models, and computational fluid dynamics. A substantial section is devoted to the modeling of the downdraft reactor, incorporating the primary conservation equations for mass, energy, and momentum. The modeling framework aims to provide a comprehensive overview for researchers seeking to simulate downdraft gasifiers. This enables researchers to utilize a summary of equations and conditions that are pertinent to their own modeling and simulations.

1. Introduction

Residual biomass is constituted by solid waste materials derived from a variety of sources, including agriculture, forestry, industry, and urban areas. Among the materials that can be used as biomass for power generation are bagasse, cotton, stalks, rice and soya husks, sawdust, coconut shells, and coffee waste. Lignocellulosic biomass is composed primarily of agricultural and forestry residues and is constituted by cellulose, hemicellulose, and lignin [1].

The utilization of biomass and waste gasification technologies has emerged as a pivotal approach in the conversion of a diverse array of biomass feedstocks, municipal solid waste, sewage sludge, and industrial waste into syngas. Gasification technology has been employed in the synthesis of a plethora of chemical compounds, including methane, methanol, and ammonia. The use of hydrogen and sustainable aviation fuels has the potential to reduce the reliance on fossil fuels in the context of chemical production. The integration of this approach with carbon capture and storage technology has the potential to result in a substantial reduction in greenhouse gas emissions [2].

The source and sustainability of the biomass feedstock are critical to the economics and success of biomass-based power generation. A wide variety of biomass feedstocks are available, which can be categorized based on their geographical origin as either urban or rural. Urban biomass feedstocks can be classified into five distinct groups: (1) urban wood waste (packing crates and pallets); (2) wastewater and sewage biogas; (3) landfill gas; (4) municipal solid waste; and (5) food processing residues. In contrast, rural biomass encompasses a wider range of materials, including forest residues, wood waste, agricultural residues (corn stoves, wheat stalks), energy crops (trees and grasses), and biogas derived from livestock effluents. A critical issue for the biomass feedstock is its energy content. It is necessary that due consideration be given to the ash and moisture contents in addition to the homogeneity of the material. It is widely acknowledged that these elements will exert a substantial influence on the cost of biomass feedstock. In order to ensure a comprehensive analysis, both the financial implications of storage and the relevance of its appropriateness must be taken into consideration [3].

A thermochemical process is a method of converting lignocellulosic or other types of biomasses into gas molecules such as hydrogen, carbon monoxide, carbon dioxide, methane, etc. These molecules have the potential to be converted into Fischer–Tropsch fuels, alcohols, or other chemical compounds [1]. Thermochemical methods include a range of processes, such as gasification, direct combustion, and pyrolysis, which are used to transform biomass into gaseous, liquid, or solid fuels, depending on the temperature and amount of oxygen used during the reaction [4]. In addition, thermochemical conversion is a more efficient process than biochemical transformation due to the high conversion efficiency and relatively short residence time. Figure 1 shows the different pathways of biomass conversion.

Gasification is part of the thermochemical processes, and it consists of drying (water evaporation), pyrolysis (removal of volatiles such as CO, CO₂, light hydrocarbons, and tar, mostly formed by long-chain liquid hydrocarbons), oxidation and reduction (or reduction and oxidation zones, depending on the type of reactor). Partial combustion takes place in the oxidation zone, while char decomposition and producer gas formation occurs. Gasification is a method of producing synthesis gas, which is composed of carbon monoxide and hydrogen, in addition to methane, carbon dioxide, light hydrocarbons, and tar. The pyrolysis step is the process by which biomass is decomposed by thermal processes into non-condensable gases, bio-oil (condensable liquids), and biochar under an inert atmosphere at high temperatures (350 to 600 °C). The reactions that occur during gasification can be divided into homogeneous and heterogeneous, as summarized in

Table 1. Homogeneous and heterogeneous reactions occurring during gasification.

Reaction Type	Reaction Example	Enthalpy of Reaction, MJ kmol−1
Homogeneous reactions
Hydrogen oxidation	H2 + ½ O2 → H2O	−242
CO oxidation	CO + ½ O2 → CO2	−283
Steam methane reforming	CH4 + H2O ↔ CO + 3H2	206
Water–gas shift	CO + H2O ↔ H2 + CO2	−41
Combustion	C + O2 → CO2	−394
Partial combustion	C + ½O2 → CO	−111
Methanation	CO + 3H2 ↔ CH4 + H2O	−227
Heterogeneous reactions
Water–gas	C + H2O ↔ H2 + CO	131
Boudouard	C + CO2 ↔ 2CO	172
Hydrogasification	C + 2H2 ↔ CH4	−75

[5,6]. In addition, the enthalpy changes have been reported at different temperatures in the interval of 298 to 1500 K for some of the reactions carried out in the gasification [5].

Reactions are mostly exothermic, except for Boudouard and water–gas reactions, where the heat for these two reactions comes from combustion or external heat sources. Depending on the set of reactions, carbon monoxide is typically produced at high temperature and normal pressure, while methane from methanation reactions is produced at lower temperature and high hydrogen pressure. The Boudouard reaction is decreased when the moisture content in the biomass is high, and the amount of CO in the producer gas decreases. On the other hand, high moisture content increases the amount of hydrogen in the producer gas [7]. The appropriate scale of operation must be determined based on the type and amount of biomass, energy demand, and other factors. In addition, environmental protection is mandatory [8].

A number of reviews have been published on this topic. In their study, Buragohain et al. [9] carried out an economic and technical analysis of decentralized power generation through biomass gasification; Ma et al. [10] conducted an analysis of lignocellulosic conversion through hydrolysis and gasification as an integrated process, while other authors [11] examined the small-scale production of heat and electricity using a downdraft gasifier. Additionally, Shahabuddin [12] discussed the production of hydrogen through gasification of municipal solid waste and biomass. A review of the modified equilibrium models focused on the non-stoichiometric methods is widely described as well [13].

This work can be divided into two parts. First, it provides an overview of the gasification process using biomass as a feedstock, including the influence of the variables involved during the reactions, such as equivalence ratio and gasifying medium, being largely discussed as well as the influence of pressure drop affecting the gasification behavior. The cleanup of the producer gas (including the tar removal) is deeply discussed. In addition, economic feasibility of biomass gasification and a bibliometric analysis are also included as well as the syngas applications. Second, this review emphasizes the necessity of disclosing the stoichiometric and non-stoichiometric equilibrium models separately, incorporating some of the most recent models of each type. Third, the kinetic models are discussed in detail, and a summary of the main reactions with an emphasis on downdraft gasifiers is provided. Furthermore, this review enumerates a range of downdraft reactor models, which, to the best of the author’s knowledge, have not been fully discussed in other reviews, particularly in the context of kinetics and reactor models. The incorporation of an extensive analysis on modeling based on computational fluid dynamics serves to complement this review, with the principal objective being to update existing information and new trends in gasification modeling. A summary of the mass, energy, and momentum equations associated with a downdraft reactor is presented here to serve as a reference for authors in developing their own models and assumptions to be used during simulation and modeling.

2. Biomass Types Used as Feed

The thermochemical conversion of biomass results in the production of gaseous, liquid, and/or solid products that can be utilized as fuels or for thermal and power generation. The efficiency of the conversion process is enhanced when the biomass possesses a higher calorific value. In a compilation by Alauddin et al. [14], the authors reported that the LHV of some biomass wastes was higher for coffee grounds, peach pits, olive residue oil, pine sawdust, cedar, and beech wood, among others. The authors summarized some LHVs of biomass wastes in the range of 13 to 22 (MJ kg⁻¹).

2.1. Particle Size

Reducing the particle size will increase the gasification rate. When pine bark is used in a downdraft gasifier, the particle size is in the range of 2 to 6 mm [15] due to the heat transfer being hindered by larger particles. In addition, the particle size of the biomass has a considerable effect on the composition of the producer gas. To illustrate, the production of CO₂ was reduced and that of CO, CH₄ and C₂H₄ was increased when smaller biomass particles were used. The reduction in particle size results in an increase in gas yield and a decrease in the lower heating value (LHV) due to the influence of pyrolysis reactions, which are kinetically controlled. Conversely, as the particle size increases, the reaction is controlled by gas diffusion, rather than by the kinetic control observed at smaller particle sizes [16]. It is widely accepted that gas yield and composition are related to the heating rate of the biomass particles. In fact, high heating rates have been shown to produce more light gases and less char and condensate. It can be hypothesized that the size of biomass particles will have a significant impact on product gas composition and yield because smaller particles possess a larger surface area, and consequently, a faster heating rate [16].

The pyrolysis of lignocellulosic biomass is a two-step process. The initial process is referred to as ‘primary pyrolysis’, and the subsequent process is known as ‘secondary charring’. The solid residue of pyrolysis is a carbonaceous and porous product known as char. The composition of char is mainly determined by the final pyrolysis temperature and the initial biomass material. It has been demonstrated that the carbon content of char increases in proportion to elevated pyrolysis temperatures. During the pyrolysis process, an increase in aromaticity and a concomitant reduction of oxygen-containing functional groups are observed. The following volatile compounds are produced during biomass pyrolysis: (1) Gases (e.g., CO, CO₂, CH₄, H₂) and light hydrocarbons (e.g., ethane, propene, N-compounds); (2) Water vapor released from pyrolysis rather than by drying; (3) Carbonyls and alcohols; (4) Heterocyclic compounds containing mostly oxygen, such as furans; (5) Phenolics, e.g., guaiacol, syringol, and phenols; and (6) Polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, and xylene (BTX) [17]. Firstly, a rapid decrease in mass was observed, which could be attributed to the decomposition of cellulose. Next, lignin was decomposed, and its char was burned for combustion. For biomass with a higher cellulose content, the pyrolysis rate increased. It was established that the biomass with a higher lignin content exhibited a reduced pyrolysis rate. The cellulose and lignin content of the biomasses was a pivotal parameter in evaluating the pyrolysis characteristics. This characteristic may be influenced by the morphology of the biomass. In the case of having biomasses with fibrous and porous morphology, the diffusion of oxygen within the particle during combustion is unobstructed [18].

It was found that, in a downdraft gasifier using wood as feed, larger particle sizes require an increase in the length of the gasification zone to increase the conversion. Furthermore, the low moisture content of biomass provides an additional advantage by eliminating the need for energy consumption during water evaporation [19]. The presence of excessive moisture in biomass has a detrimental effect on the quality of the produced gas and the overall performance of the system. Moreover, the CO content in the syngas is higher in the case of dry fuels, while the CO₂ content increases with the moisture content of the feedstock [20].

The correlation between biomass particle size and the limitations in the gasification process is well known. Given that smaller biomass particle sizes are associated with an increased surface area per unit mass, resulting in enhanced heat and mass transfer rates between phases, it can be deduced that biomass size has the potential to enhance gasification performance [21]. However, it has been demonstrated that reducing biomass particle size below 1 mm results in an exponential increase in energy consumption, accounting for approximately 10% of the output energy obtained in the gasification process [22].

The mechanical pre-processing of biomass, namely grinding, resulted in additional costs within the overall process. These additional costs arise from comminution of the biomass to be further processed. It has been reported that the specific energy consumption for grinding using a hammer mill can reach 50–65 (kJ kg⁻¹) for harvested Miscanthus biomass and 35–50 (kJ kg⁻¹) for dried energetic willow, using a 10 mm orifice sieve. Thus, for processing substantial quantities of biomass, the cost of grinding (or mechanical pre-processing) is comparatively high [23]. Other reports [24] demonstrated that a reduction in particle size enhanced the dry gas and hydrogen yield, whilst concomitantly leading to a decrease in tar content. Furthermore, other findings have reported that the particle size exhibited a marginal impact on gasifier performance, with a modest decline in tar content observed as the biomass particle size diminished [25]. Erkiaga et al. [26] further observed that the gas composition and tar yields were only minimally influenced by the diameter of the biomass particles because of the high rate of heat transfer within the bed.

In summary, the dimensions of the biomass are subject to considerable variation, contingent on the specific reactor employed, the moisture content of the biomass feedstock, and the extent of pre-processing of the biomass to yield briquettes, pellets, etc. The size and density of the biomass are important parameters because they affect the rate of heating and drying during the process. It has been demonstrated that larger particles exhibit a greater propensity for slow heating in comparison to smaller particles, resulting in a greater production of char and a reduced yield of tar. In fixed bed gasifiers, the presence of fine-grained and/or fluffy feedstock has been observed to potentially induce flow impediments within the bunker section. This phenomenon is characterized by a decline in pressure within the reduction zone, accompanied by an elevated proportion of dust particles in the gas. In downdraft gasifiers, the substantial pressure drop can also diminish the gas load, leading to reduced temperatures and heightened tar production. The type of handling equipment employed is contingent upon the size, shape, density, moisture content and composition of the biomass [3].

2.2. Cellulose, Hemicellulose, and Lignin Content

Lignin and cellulose have a significant influence on pyrolysis and gasification. The higher the cellulose content, the higher the pyrolysis rate. Conversely, the higher the lignin content, the lower the pyrolysis rate, and the more tar is produced. Physical treatment of biomass prior to gasification includes grinding, briquetting, pelleting, and even torrefaction. The process of torrefaction has the effect of improving the physical, chemical, and rheological characteristics of biomass materials. The term “torrefied biomass” refers to a group of products resulting from the partial control and isothermal pyrolysis of biomass, occurring at temperatures between 200 and 300 °C and including the devolatilization and carbonization of hemicellulose in the initial stages, followed by the depolymerization and devolatilization of lignin and cellulose in subsequent steps. This process maintains minimal moisture in the biomass and enhances the energy density, homogeneity, grindability, and formation of pellets that can be used in gasification plants. In addition, the typical calorific value of torrefied biomass is within the range of 18–22 (MJ kg⁻¹) [27].

Torrefaction is a mild pyrolysis process that enhance the quality of biomass by effectively removing moisture from the material and improve its energy density. Moreover, it can be integrated with gasification as a pre-treatment since the efficiency of gasification is improved particularly when dry biomass is used. In certain instances, volatile compounds that are produced due of torrefaction can be combusted with air, thereby resulting in the generation of hot flue gases, which facilitates the recovery of thermal energy. Furthermore, torrefied biomass has been demonstrated to engender a reduced emission of carbon dioxide, concomitant with an augmented yield of hydrogen and methane. Nevertheless, the energy consumption inherent to the torrefaction process renders it less energy efficient. The thermal energy of gasification can be converted into electrical energy with a steam turbine generator, which improves the overall sustainability performance. As demonstrated in the literature [28], it is feasible to enhance energy efficiency through the implementation of a multi-generation process that integrates a pre-treatment torrefaction step, a gasification process, electric power generation through syngas utilization in solid oxide fuel cells, a carbon dioxide liquefaction process, and thermal energy to electricity generation in CHP units, for example, could constitute a route towards a carbon-neutral usage of biomass.

In the case of wood, it has been reported that materials with higher raw densities are more reactive due to their high volatile content. This causes significant changes in the pore structure compared to low-density wood, by which gasification is then limited by pore diffusion [29]. Components such as cellulose, hemicellulose, and lignin are mainly contained in different amounts in biomass. Cellulose resulted from polymerization of glucose by β-1,4-glycosidic bonds. Hemicellulose is a polysaccharide composed of glucose, galactose, mannose, xylose, arabinose, and uronic acids. The contents of cellulose, hemicellulose, and lignin in biomasses are contingent on the source. Typically, cellulose, hemicellulose, and lignin account for 40–60 wt%, 20–40 wt%, and 10–25 wt% of dry biomass materials, respectively [30]. It has been demonstrated that an elevated cellulose and hemicellulose/lignin ratio is indicative of a greater syngas volume. Furthermore, lignin has been shown to result in a higher tar yield in comparison to cellulose and hemicellulose [31].

For example, rape straw is composed of 27.9 wt%, 16.9 wt%, and 21.1 wt% of cellulose, hemicellulose, and lignin, respectively, while woods, i.e., black locust wood, is formed of 34.2 wt% (cellulose), 16.4 wt% (hemicellulose), and 25.8 wt% (lignin). In a similar way, pine sawdust is composed by 59 wt% (cellulose), 19 wt% (hemicellulose), and 22 wt% (lignin). The composition is strongly affected by the origin of the biomass, such as woods, straws, shells, etc. In addition, the composition for macro algae and corn stalks is also reported [32], as outlined:

• Sargassum horneri: 28.29–39.88%; 16.75–22.64%; 22.10–27.20%.
• Corn stalk: 30.60–33.10%; 25.80–27.65%; 14.60–15.90%.

Both the cellulose and hemicellulose are embedded in lignin, which provides mechanical resistance to wood due to its highly cross-linked phenolic units [33]. The mean molecular weight of cellulose is approximately 100,000, in comparison to less than 30,000 for hemicellulose and approximately 20,000 for lignin [34]. In

Table 2. Chemical composition for raw biomass in dry basis.

Biomass Type	Hemicellulose, wt%	Cellulose, wt%	Lignin, wt%	Others, wt%	Reference
Black locust wood	16.40	34.20	25.80	23.60	[35]
Corn straw	29.72 ± 0.83	34.03 ± 1.27	22.00 ± 0.58	14.25 ± 0.37	[36]
Pine bark	18.30 (22.62) a	21.90 (27.07) a	40.70 (50.31) a	18.00	[37]
Poplar	21.70 (23.77) a	42.70 (46.77) a	26.90 (29.46) a	7.20	[37]
Rape straw	16.90	27.90	21.1	34.10	[35]
Spruce bark	13.90 (15.67) a	29.70 (33.48) a	45.10 (50.84) a	10.12	[37]
Walnut shell	19.29 ± 0.76	18.32 ± 0.65	41.50 ± 1.35	20.89 ± 0.63	[36]
Wheat straw	23.80 (29.10) a	37.50 (45.84) a	20.50 (25.06) a		[37]
Wheat straw	22.90	33.90	19.10	24.10	[35]
Willow	22.60 (24.57) a	44.30 (48.15) a	25.10 (27.28) a	10.30	[37]
Bamboo	17.23 ± 0.43	44.45 ± 0.07	18.53 ± 0.32		[38]
Pine sawdust	19.0	59.0	22.0		[39]
Wheat straw	45.0	22.0	33.0		[39]

^a Values in parentheses correspond to ash and extractives-free basis.

, the composition in terms of cellulose, hemicellulos, lignin, and other compounds of some biomasses is depicted.

The devolatilization process of the biomass is understood to occur through a combination of water evaporation and the decomposition of hemicellulose, cellulose, and lignin [40]. After the removal of moisture at temperatures up to 150 °C, hemicellulose begins to decompose between 200–300 °C, followed by cellulose between 250–380 °C. The lignin, which is the component with the most complex structure, decomposes at temperatures ranging from 200 to 1000 °C [41,42]. The activation energy for hemicellulose, cellulose, and lignin decomposition can be described by a combined kinetic method during the primary pyrolysis stage [43]:

• Hemicellulose: 95.39 (kJ mol⁻¹); cellulose: 199.66 (kJ mol⁻¹); lignin: 174.40 (kJ mol⁻¹).

A comparative analysis was conducted on the gasification of three distinct biomass samples, namely pine sawdust, used ground coffee, and wheat straw, under uniform experimental conditions. The results indicated that pine sawdust exhibited a higher cellulose content, while used ground coffee and wheat straw were found to be abundant in hemicellulose and lignin. The gasification process was also carried out on model compounds using cellulose, xylan (as a representative of hemicellulose), and lignin, individually. Gasification experiments were conducted within a quartz tube gasifier, wherein the sample was subjected to a heating rate of 20 °C/min until the gasification temperature was attained, after which it was maintained for a duration of 90 min. The results of the study indicate that cellulose and cellulose-rich biomass (e.g., pine sawdust) yield a greater quantity of tar and a lower amount of hydrogen in comparison to lignin and lignin-rich biomass (e.g., used ground coffee and wheat straw). An elevated level of lignin and hemicellulose results in a greater quantity of char. The gasification of pine sawdust, used ground coffee, and wheat straw yielded 12.7, 15.7, and 17.2 vol% hydrogen, respectively. Furthermore, the gasification of cellulose, hemicellulose, and lignin resulted in hydrogen yields of 10.5%, 22.1%, and 30.4 vol%, respectively. Interactions among components can be substantial due to the presence of an overlapping degradation region [39].

2.3. Proximate and Ultimate Analysis for Different Types of Biomasses

As the proximate and ultimate analyzes are also necessary to obtain a complete picture of the biomass composition,

Table 3. Proximate and ultimate analyzes for different types of biomasses.

Source	Proximate Analysis, wt%				Ultimate Analysis, wt%						Ref.
	Moisture	Volatiles	Fixed Carbon	Ash	C	H	N	O	S	LHV, (MJ kg−1)
Alfalfa stem		78.92	15.81	5.27	47.17	5.99	2.68	38.19	0.20	16.8	[45]
Almond hulls		73.80	20.07	6.13	47.53	5.97	1.13	39.16	0.06	17.0	[45]
Bamboo wood	11.50 a	86.80	11.24	1.95	48.76	6.32	0.20	42.77		18.5	[44]
Coconut shell	8.20 a	77.19	22.10	0.71	50.22	5.64	0.41	42.94		18.0	[44]
Coffee husk		78.50	19.10	2.40	47.50	6.40		43.70		17.8	[46]
Corn cobs	10.1 a	80.06	17.82	2.12	47.60	6.10	0.52	45.78		17.1	[47]
Corn stover		66.58	26.65	6.73	45.48	5.52	0.69	41.52	0.04	16.2	[48]
Cotton stalk		76.10	18.80	5.10	47.07	4.58	1.15	42.10		15.7	[49]
Douglas fir bark					56.2	5.9		36.7		19.9	[50]
Empty fruits bunch	5.5	69.0	18.5	7.0	42.33	5.28	1.46	50.84	0.08	14.0	[51]
Eucalyptus wood	16.40 a	75.35	21.30	3.35	46.04	5.82	0.30	44.49		16.8	[44]
Hazelnut shell		68.90	30.00	1.10	50.90	5.90	0.40	42.80		17.9	[52]
Larch sawdust	2.6	76.7	19.9	0.8	48.5	6.4	0.1	44.7	0.3	17.9	[53]
Mango wood (Mangifera indica)		82.30	16.34	1.36	50.18	6.35	0.13	43.34		17.2	[54]
Mangrove	5.3	36.26	56.4	2.04	66.46	4.37	0.03	29.14		22.8	[55]
Maritime pinewood (wood stem) b		85.7	14.0	0.22	45.60	6.70	0.13	46.40	0.03	19.5	[56]
Microalgae (Nannochloropsis oculata)	6.71	78.94	7.95	6.4	47.50	6.15		46.35		13.6	[57]
Neem wood (Azadirachta indica)		82.35	16.35	1.35	50.20	6.38	0.14	43.28		18.5	[54]
Peanut shell		84.90	13.40	1.70	47.40	6.10	2.10	44.40		16.8	[52]
Pine sawdust (Pinus elliotti)	10.66	73.74	15.30	0.30	50.91	6.13	0.23	42.14		18.1	[58]
Pinewood pellet	10.8	69.8	18.3	1.1	49.8	6.03	0.21	42.86			[59]
Rice husk	9.95	55.54	14.99	19.52	49.07	3.79	0.63	46.42	0.09		[60]
Rice husk	3.6	60.0	20.1	16.3	38.5	5.5	0.4	36.6	0.2	14.9	[53]
Rice husk	9.5	67.6	6.3	16.6	49.2	2.2	0.44	48.02	0.06	13.2	[61]
Rice husk pellet	9.2	65.1	16.4	9.30	46.60	6.20	0.70	37.40	0.10	15.6	[53]
Rice straw		65.47	15.83	18.67	38.24	5.20	0.87	36.26	0.18	13.6	[45]
Rubber wood		80.1	19.2	0.7	50.6	6.5	0.2	42.0		17.7	[19]
Sawdust pellets	9.50	80.63	17.27	2.10	48.91	5.80	0.18	45.11		18.4	[62]
Sewage sludge	6.48	50.09	4.39	39.04	29.50	4.67	5.27	20.21	1.31	11.5	[63]
Sewage sludge	13.25	45.50	10.65	30.60	21.56	4.11	2.89	21.43	1.81		[64]
Soya residue				3.44	43.59	5.60	0.35	50.46		19.6	[65]
Sugarcane bagasse	51.01 a	83.66	13.15	3.20	45.48	5.96	45.21	0.15		16.9	[44]
Sunflower residue				9.6	42.60	5.47	0.19	51.74		20.3	[65]
Switch grass		76.69	14.34	8.97	46.68	5.82	0.77	37.38	0.19	16.3	[45]
Tunisian olive pomace	23.45	75.28 c		1.27	53.60	6.93	1.48	37.98		21.3	[66]
Vine pruning	17.6 a	80.84	16.54	2.62	50.84	5.82	0.88	42.46		18.2	[47]
Wheat straw	8.87 a	82.12	10.98	6.90	42.95	5.35		46.99		16.2	[44]

^a On wet basis. ^b Average values. ^c Denoted as organic material in the proximal analysis (moisture, ash, and organic material) by the authors.

displays various types of biomasses frequently utilized for gasification. The lower heating values (LHV) are also reported. The biomass parts commonly used are stems, stalks, husks, or straws. Additionally, the composition of sewage sludge, which some authors have reported using as feed, is provided. If HHV is not provided, it can be calculated from the ultimate or proximate analysis. According to Channiwala and Parikh [44], a correlation has been established to predict HHV from the ultimate analysis data. The correlation has an average absolute error of 1.45% and a bias error of 0%:

$$\mathrm{H}\mathrm{H}\mathrm{V}=0.3491\times \mathrm{C}+1.1783\times \mathrm{H}+0.1005\times \mathrm{S}-0.1034\times \mathrm{O}-0.0151\times \mathrm{N}-0.0211\times \mathrm{A}\mathrm{s}\mathrm{h}$$

(1)

where C, H, S, O, N, and Ash is the wt% of carbon, hydrogen, sulfur, oxygen, nitrogen, and ash content of the biomass on dry basis, respectively. The above-mentioned equation is valid for the following intervals given in wt%: 0 ≤ C ≤ 92.25; 0.43 ≤ H ≤ 25.15; 0 ≤ O ≤ 50.0; 0 ≤ N ≤ 5.60; 0 ≤ S ≤ 94.08; 0 ≤ Ash ≤ 71.4.

In addition, the correlation is only valid for the interval of HHV, in (MJ kg⁻¹), of 4.745 ≤ HHV ≤ 55.345. The correlation is not accurate for predicting the values for highly unsaturated hydrocarbons such as ethylene and acetylene. The oxygen content is obtained commonly by difference as follows:

$$\mathrm{O}\mathrm{x}\mathrm{y}\mathrm{g}\mathrm{e}\mathrm{n} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}, \mathrm{w}\mathrm{t}\%=100 \mathrm{w}\mathrm{t}\%-\left(\mathrm{C}+\mathrm{H}+\mathrm{N}+\mathrm{S}\right) \mathrm{i}\mathrm{n} \mathrm{w}\mathrm{t}\%-\mathrm{A}\mathrm{s}\mathrm{h} \mathrm{i}\mathrm{n} \mathrm{w}\mathrm{t}\%$$

(2)

Furthermore, a correlation based on the proximate analysis to calculate the HHV is also reported as follows [67]:

$$\mathrm{H}\mathrm{H}\mathrm{V}=0.1905\times \mathrm{V}\mathrm{o}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{e}\mathrm{s}+0.2521\times \mathrm{F}\mathrm{i}\mathrm{x}\mathrm{e}\mathrm{d} \mathrm{c}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n}$$

(3)

where volatiles and fixed carbon are expressed in wt% on dry basis and have a low bias error.

3. Reactors Used for Gasification

Gasifiers can be classified as autothermal (or direct gasifiers) and allothermal (or indirect gasifiers). The heat necessary for the reactions is provided by autothermal gasifiers through the addition of an oxidant inside the reactor, which is partially consumed and affects the quality of the gas. When air is used as an oxidant, the presence of nitrogen reduces the lower heating value of the producer gas, which ranges from 4 to 6 (MJ Nm⁻³). In contrast, this value reaches 10–12 (MJ Nm⁻³) when using oxygen and steam. In allothermal gasification, an external heat source is employed to provide the requisite thermal energy for the reactions. This may take the form of a solid carrier, such as sand, or a heat-integrated gasifier. Subsequently, the heat is transferred primarily by radiation and convection. The principal benefit of this type of gasifier is its capacity to produce nitrogen-free producer gas, thereby circumventing the necessity for the use of pure oxygen as a gasifying agent. Reactors for gasification can be grouped into three main categories: (1) fixed bed; (2) moving bed; and (3) fluidized bed reactor.

Extensive research has been carried out on fixed bed gasifiers, which are widely considered to be the most suitable reactor for biomass gasification. This is mainly due to the simple operational and design characteristics of these reactors, and they are mainly classified according to their mode of operation, which includes updraft, downdraft, and cross-draft gasifiers. In a fixed-bed updraft reactor, the gas is introduced at the base of the grate zone. This results in the formation of a countercurrent flow between the biomass and the gasification medium. Subsequently, the biomass is subjected to a drying process, which is followed by pyrolysis, reduction, and oxidation. The drying process is facilitated by the utilization of the sensitive heat of the producer gas, which ascends prior to its departure from the reactor. Concurrently, the gases produced during pyrolysis do not fully reach the oxidation zone, where a considerable quantity of tar is present in the producer gas. The moisture content of the biomass does not present a limiting factor for this reactor; however, the cold gas efficiency is relatively low [68]. The updraft gasifiers have the disadvantage of producing larger amounts of tar, with concentrations ranging from 10 to 150 (g Nm⁻³) [69,70]. Figure 2 shows a schematic view of an updraft gasifier.

In contrast, downdraft gasifiers produce up to 6 (g Nm⁻³) of tars [71]. The downdraft gasifiers are commonly utilized due to the minimal production of tar and the superior quality of the syngas. The downdraft gasifier is also referred as a co-current gasifier. There are two principal categories of downdraft gasifiers: (1) stratified or throatless gasifiers, and (2) Imbert or throated gasifiers. In the case of Imbert gasifiers, the flow and temperature are not uniform in the throat section, which may give rise to issues when scaling up to greater flows and biomass amounts. The throat angle is very important in this type of gasifier because the conversion decreases as the throat angle increases. On the other hand, the length of the gasification zone increases as the throat angle decreases [19].

In a downdraft reactor, the gasification medium is introduced below the drying and pyrolysis zones. In the oxidation stage, carbon dioxide and water are produced; however, these gases are reduced to carbon monoxide and hydrogen through the Boudouard and the water–gas reactions in the reduction zone, resulting in the generation of a minimal amount of tar. It is essential to maintain a low moisture content in the biomass in order to achieve the requisite high temperatures during the oxidation stage. The formation of tar in this gasifier is minimal due to its transformation into compounds with lower molecular weights through cracking reactions in the oxidation zone. However, the temperature of the producer gas is currently high, necessitating the use of heat exchangers to recover thermal energy [69]. In contrast, the current evidence suggests that fluidized bed and entrained flow gasifiers have the potential to be developed for fuel production. Figure 3 depicts both types of downdraft gasifiers.

In fluidized bed reactors, the shredded biomass is fed and mixed with a substantial quantity of bed material, which may be either inert, such as silica-quartz sand, or catalytic, including dolomite, olivine, and other materials. The gasifying agent is introduced into the reactor via nozzles at the base, where it fluidizes the contents of the reactor. The classification of the reactor depends on the speed at which it operates, with the distinction between a bubbling and a circulating fluidized bed reactor being made accordingly. At low fluidization speeds, the reactor behaves as a bubbling fluidized bed, whereby the bed material is retained within the reactor chamber. Conversely, at high fluidization speeds, the bed material is expelled from the reactor and collected in a cyclone separator, where it is recycled to the gasifier, which behaves as a circulating fluidized bed. The uniform heating and gas production is contingent upon the presence of high turbulence, as this facilitates the homogeneous occurrence of all steps involved in the process, including drying, pyrolysis, oxidation, and reduction [72]. The tar amount formed in a bubbling and in a circulating gasifier is up to 23 (g Nm⁻³) and 30 (g Nm⁻³), respectively [73].

In the case of the circulating fluidized bed, different biomass fuels can be processed, and the moisture content of the fuel is an important property to consider. At higher moisture contents, a greater amount of air or oxygen is required to maintain the temperature in the reactor chamber. Consequently, a greater quantity of carbon is transformed, thereby diminishing the quantity of tar; however, the calorific value of the producer gas is also diminished [74]. One type of circulating fluidized bed gasifier is the dual fluidized bed gasifier. In this reactor, the char reacts in the combustion zone, while gasification occurs in the first reactor, which is currently utilizing steam. The resulting char is then transported to the second reactor, where it is oxidized with air. The use of sand particles serves two purposes: firstly, to enhance the fluidization process, and secondly, to facilitate the transfer of thermal energy between the reactors [75]. Figure 4 shows a schematic view of a bubbling fluidized bed gasifier and a circulating fluidized bed gasifier.

Spouted beds are a diverse class of apparatus, with the most common variety being the conventional conical spouted bed, which is composed of the nozzle, the bottom inverted cone, and the cylindrical main body. The spouted bed is filled with particles, and gas is injected from the inlet located at the bed base. This process creates a powerful jet in the middle of the bed. When the velocity of the gas exceeds a critical threshold, it creates a path through the particles, leading to the formation of a dilute-phase spout region in the center of the bed, where particles move upward. Once the particles reach a certain height, their velocity is known to decrease under the action of gravity, and they fall back down, forming a region that resembles a fountain. The gas flow-induced particle circulation around the spout region gives rise to an annular region characterized by densely packed particles. The classification of spouted beds is determined by the number of sections of the beds [76]. In the case of spout-fluidized bed, it aims to prevent particle agglomeration, and a distributor is incorporated at the side wall of the conical area. The sparging gas entering from the bottom of the spouted bed and the fluidizing gas entering the side wall jointly fluidize the particles to form a spout-fluidized bed. The addition of the distributor promotes the mixing of particles and gas in the annulus region, reduces the local aggregation of particles, and improves stratification or throttling in the bed. The spout-fluidized bed enhances the bed thermal efficiency [77].

In entrained flow reactors, both the finely pulverized biomass and the gasification medium are fed concurrently to the reactor. The pyrolysis products are subjected to combustion in the oxidation zone at a higher temperature, resulting in low tar production (0.2 g Nm⁻³), as documented elsewhere [78]. Depending on the gasification medium used, i.e., air or oxygen, the reactor is typically operated at elevated temperatures (1200 to 1500 °C), which increases the conversion rate [70]. The reactor is utilized primarily for coal gasification due to its brief residence time, elevated temperatures, and high feed throughput [76]. However, when biomass is used as a feed, this reactor requires the use of smaller particles. As illustrated in Figure 5, a schematic representation of an entrained flow reactor is provided.

A summary of the main characteristics of the different types of gasifiers is shown in the

Table 4. Main characteristics of the different types of fixed bed, fluidized bed, and entrained flow gasifiers.

Reactor Characteristics		Commonly Used Feedstock	Particle Size, mm	Moisture Content (Wet Basis), wt%
Fixed bed
Updraft	The fuel is preheated, resulting in high efficiency. This gasifier requires a high flame temperature. The producer gas may contain some dust and impurities.	Chipped wood, dried sewage sludge, rice husks [3]	6–100 [3]	<20 [3]
Downdraft	The producer gas reaches high temperatures, resulting in a reduced quantity of tars produced.	Wood chips, nut shells, pellets [3]	Up to 50 [3]	<15 [3]
Cross-draft	The temperature in the combustion zone exceeds 1500 °C. This gasifier is primarily used for processing charcoal.	Briquettes (eucalyptus, bamboo, etc.) [79]	Variable	<15 [79]
Fluidized bed
Stationary fluidized bed	During fast pyrolysis, silica sand or olivine is used as bed material. Increasing the head space can reduce the amount of tar produced. Ash and char can be separated from the producer gas by a cyclone.	Bagasse, low alkali content fuels, mostly wood residues with high moisture content [3]	Up to 50 [3]	<60 [3]
Circulating fluidized bed	The producer gas is separated from the bed material and ash through a cyclone and returned to the gasifier. The gasifier can be scaled up due to the increased cross-sectional area.	Wood and chipped agricultural residues [3]	6–50 [3]	15–50 [3]
Circulating fluidized two-bed	Gasification takes place in one fluidized bed, while the bed material is circulated to the second bed and then to the gasification reactor.	Wood and chipped agricultural residues [3]	6–50 [3]	15–50 [3]
Entrained flow
Entrained flow	Gasification occurs quickly with a low amount of tar. It is possible to operate at high temperatures (up to 1200 °C) and pressures (up to 100 bar). Biomass with a low ash melting point can be used as feed. Design requires materials that can withstand harsh operating conditions.	Coal and pet coke finely pulverized [80]. The adaptation of the entrained flow gasifier to biomass is still under development	Distinct values on micrometers scale [81]

.

The utilization of gasifiers that produce elevated levels of tars is predominantly oriented towards the maximization of product yield, particularly electricity or heat. The operation of downdraft gasifiers is observed in small and medium-scale applications. It is a common practice to couple them with combined heat and power (CHP) systems and use them in this manner. These systems are not limited in their use to rural areas only. The capacity ranges of these gasifiers are limited to a few hundred kW of electricity, whereas other gasifier types, such as fluidized beds, are capable of producing electricity or heat in the order of MW. In the context of chemical and fuel production, such as synthetic natural gas, the usage of fluidized bed gasifiers is favored, attributable to their augmented capacity for biomass processing. A further salient characteristic of gasifiers pertains to the operational cost that is associated with their use. For instance, the operational expenditure (OpEx) for fixed bed gasifiers ranges from €0.01 to €0.16/kWh, whereas for fluidized bed and entrained flow gasifiers, these figures are €0.09 to €0.15/kWh and €0.02 to €0.05/kWh, respectively. It is thus observed that fluidized and fixed bed gasifiers have similar OpEx, while the cheapest one corresponds to entrained flow gasifier [2].

4. Gasifying Medium

The heating value of the producer gas is dependent on the selected gasifying medium, which may be air, oxygen, carbon dioxide, or steam. The composition of the producer gas is subject to variations as a consequence of the gasifying agent that is employed. For instance, the utilization of steam results in a higher concentration of hydrogen in the final product, whereas the employment of oxygen gives rise to an elevated production of CO₂ and H₂O. The composition (vol%) of CO, CO₂, H₂, CH₄, and N₂ in a fixed bed gasifier was determined to be 13–18, 12–16, 11–16, 2–6, and 45–60, respectively, using air as the gasifying agent. In the case of an entrained flow gasifier with oxygen as gasifying medium, the compositions were found to be 45–55, 10–15, 23–28, 0–1, and 0–1, respectively, whereas in a fluidized bed gasifier with steam gasification, the compositions were reported to be 25–30, 20–25, 35–40, 9–11, and 0–5, respectively [82].

The utilization of air results in a reduction in LHV of the gas due to the presence of nitrogen. Accordingly, LHV of the synthesis gas is within the range of 4–6 (MJ Nm⁻³). Conversely, the utilization of steam as a gasifying medium has been observed to enhance the heating value of the syngas to a range of 12–14 (MJ Nm⁻³). Furthermore, the use of oxygen results in higher heating values, reaching 10–12 (MJ Nm⁻³), although this is accompanied by increased operational costs [82]. Additionally, carbon dioxide is employed in the conversion of char, methane, and tar to hydrogen and carbon monoxide, facilitated by Ni-based catalysts [68,83,84,85]. Moreover, the use of CO₂ or steam in gasification requires an external or indirect heat supply.

The equivalence ratio (ER) is commonly used when oxygen or air is employed in gasification. It represents the ratio of oxygen required for gasification to the stoichiometric oxygen needed for the complete combustion of biomass. A higher ER value indicates a lower tar amount, as more oxygen can be consumed during the reaction. Conversely, a higher ER value results in a lower amount of hydrogen and carbon monoxide in the producer gas, as the biomass is primarily combusted, with the production of carbon dioxide and water being highly prevalent [86,87]. At elevated excess ratios, the majority of the hydrogen is consumed by oxidation reactions [16]. Consequently, the value of the equivalence ratio is typically situated between 0.2 and 0.4 [68].

In the context of gasification, the steam-to-biomass ratio (S/B) is introduced as an operational parameter of the gasifier when steam is employed as the oxidant [88]. Consequently, the utilization of steam results in the generation of a greater quantity of hydrogen. A number of studies have been conducted to examine the S/B ratio. For instance, a maximum hydrogen content of 52.7% was achieved with an S/B ratio of 2.10, whereby the ratio was varied from 0 to 2.80 [89]. Variations in the S/B ratio between 1.35 and 2.70 resulted in a slight increase in CO₂ and H₂ due to enhanced steam reforming reactions involving CO, CH₄, and C₂H₄, which decreased in the producer gas [16].

The influence of the atmospheric and reduced pressure (below atmospheric) in a fixed bed gasifier in the composition of the producer gas as a function of the ER was studied [81]. The examination of the impact of ER on the composition of producer gas is contingent on atmospheric pressure [90]. In the context of atmospheric pressure (0.1 MPa); the ER value exerts a substantial influence on the composition of syngas and its calorific value. It is evident from the data that an increase in the ER value results in a significant rise in the concentration of CO. The volume fraction of H₂ exhibited a comparable trend to that of CO, while CO₂ demonstrated an opposing trend to that of CO. This is particularly evident until an ER value of 0.28 at atmospheric pressure, where the gas heat value attains its maximum value and then gradually declines. In instances where the equivalence ratio is low, there is insufficient oxygen supply [90,91]. In the case of reduced pressure (0.04 MPa), the composition of the syngas exhibited a comparable trend to that observed under atmospheric pressure conditions, as Figure 6 shows, which is obtained using data published in several other locations [90]. From Figure 6, the optimal equivalence ratio is determined to be 0.33 under these conditions, where the calorific value attains its maximum value. This is attributable to the lower oxygen concentration observed at higher altitudes, in which the dilution of gas components plays an important role.

How producer gas as a function of ER from 0.21 to 0.41 varies while maintaining a constant gasification temperature of 950 °C under air + steam was studied [92]. The authors found that increasing the ER is associated with a decline in the yield of hydrogen and carbon monoxide that was converse to the carbon dioxide concentration, which is attributed to the enhanced oxidation reactions for both hydrogen and carbon monoxide as the oxygen enters the gasifier at higher ER. Consequently, elevated ER are more conducive to the formation of carbon dioxide at the expense of hydrogen and carbon monoxide. Furthermore, an increase in ER by oxidation results in a decrease in methane concentration, leading to the formation of carbon dioxide and water. This relationship is further supported by the observation that an increase in ER leads to a corresponding decline in the HHV. In a separate case study, the range of ER values was from 0.17 to 0.65, as Figure 7 shows; these values were plotted from distinct data published by the authors [92]. Initially, a decrease in the concentration of carbon monoxide and carbon dioxide was observed, with an increase in ER from 0.17 to 0.24. Conversely, an increase in hydrogen was observed, whilst methane decreased as the dry reforming reaction occurred. Furthermore, when ER in the dry reforming reaction exceeds 0.24, there is an increase in the concentration of carbon dioxide, whilst the concentration of carbon monoxide, hydrogen, and methane decrease due to the oxidation reactions.

The utilization of CO₂ may result in a reduction in pollution through the recycling of the gas and an improvement in the H₂/CO ratios. Moreover, the gasification process gives rise to the formation of reactive char. The utilization of pure CO₂ at atmospheric pressure and adiabatic operation results in incomplete carbon conversion, as evidenced by a low value of cold gas efficiency (CGE). In order to enhance the performance of the gasifier operating at atmospheric pressure, it is essential to ensure a consistent supply of heat to the reactor, either through the provision of additional heat or an increase in the CO₂ flow rate. The use of a CO₂/O₂ mixture allows gasification to occur without the need for a heat supply over a range of temperatures. The utilization of steam as a co-gasifying agent also serves to reduce the conversion of CO₂ into other compounds [93].

In addition, Ni and Al-based catalysts were used when the gasification medium was CO₂ at atmospheric pressure and 700 °C [94]. In the same line, the influence of sodium, potassium, calcium, magnesium, and iron nitrates on CO₂ gasification of biomass char was studied using thermogravimetric analysis. It was found that biomass reactivity decreased in the following order: Na > Ca > Fe > K > Mg [95]. Other authors reported that the CO₂ gasification of biomass char reactivity decreased when using metal catalysts as follows: K > Na > Ca > Fe > Mg [96]. CO₂ reforming typically uses Ni-based catalysts and high water content in the feed to reduce the carbon formation. However, in more severe operating conditions, a sulfur-passivated reforming process can be used. This process involves sulfur poisoning the catalytic sites and a stream of H₂S/H₂ being fed to prevent carbon deposition on Ni catalysts [97]. The carbon deposition on the catalyst surface is prevented with addition of an excess of CO₂ [85]; the following reactions are expected to occur involving CO₂.

$$\mathrm{C}{\mathrm{H}}_4+\mathrm{C}{\mathrm{O}}_2\leftrightarrow 2{\mathrm{H}}_2+2\mathrm{C}\mathrm{O}$$

(4)

$$\mathrm{C}{\mathrm{O}}_2+{\mathrm{H}}_2\leftrightarrow \mathrm{C}\mathrm{O}+{\mathrm{H}}_2\mathrm{O}$$

(5)

$$\mathrm{C}{\mathrm{O}}_2+\mathrm{C}\leftrightarrow 2\mathrm{C}\mathrm{O}$$

(6)

The reaction rate of gasification with carbon dioxide is slower compared with steam. For example, for wood gasification, the activation energy and pre-exponential factor values were 151 (kJ mol⁻¹) and 6993 s⁻¹ with CO₂, and 138 (kJ mol⁻¹) and 3729 s⁻¹ with steam, in the range of 1073–1173 K [29]. The kinetics of coal and wheat straw chars were investigated in a fixed-bed reactor. The findings suggest that the rate of reaction increased with rising CO₂ partial pressure, with reaction orders in the range from 0.3 to 0.7, and minimal temperature dependence was exhibited during the process of biomass gasification. Nevertheless, elevated temperatures were observed to increase the reaction order for coal gasification [98]. In other reports, the activation energy varied from 140 to 160 (kJ mol⁻¹), while the reaction orders ranged from 0.4 to 0.6 for spruce and birch wood at different pressures of CO₂ [99]. The studies conducted at elevated CO₂ pressures (20 bar at 1000 °C) have demonstrated an important enhancement in the reaction rate, which can be effectively described by the Langmuir–Hinshelwood model. This indicates that the gasification kinetics are primarily driven by a CO₂ chemisorption-limited reaction mechanism [100].

The process of CO₂ gasification has recently attracted considerable attention, serving two primary functions: the production of CO-rich syngas and the advancement of CO₂ utilization within the scope of carbon capture, utilization, and storage strategies [101]. Carbon capture and storage (CCS)/carbon capture, utilization, and storage (CCUS) systems are widely recognized as having the potential to reduce CO₂ emissions. Particularly, the pivotal role of CCUS technologies in achieving a global reduction of CO₂ emissions by 15–20% by 2050 has been emphasized [102,103]. Carbon dioxide is a by-product of a variety of industrial processes, emanating from flue gases discharged from power plants, cement factories, and steel mills. Furthermore, the gaseous by-products of combustible solid waste gasification have been found to consist of 10–30 vol% CO₂ [101].

There are several technological pathways available for the capture of carbon dioxide, namely: industrial separation, post-combustion, pre-combustion, oxy-fuel combustion, chemical looping combustion, and direct air capture systems. The financial implications of CO₂ capture (USD/tonne CO₂) for diverse technologies are subject to variation depending on their respective stages of technological maturity. For instance, the costs associated with industrial separation, post-combustion, pre-combustion, oxy-fuel combustion, chemical looping combustion, and direct air capture range from 34.8 to 60.9, 46 to 74, 34 to 63, 52, <59.2, and 140 to 340, respectively. Industrial separation and post-combustion capture technologies are utilized extensively, with pre-combustion, oxy-fuel combustion, and direct air capture, following in sequence. Chemical looping combustion, conversely, remains in the development stage [102].

The process of CO₂ gasification has been identified as a key method for the conversion of CO₂ into a valuable product. This approach is in alignment with global strategies that are focused on the reduction of carbon footprints. However, it should be noted that CO₂ gasification necessitates elevated operational temperatures. Moreover, the kinetics of CO₂ gasification reactions are, in general, slower in comparison to those of other gasifying agents. Catalysts or enhanced reactor designs are therefore required in order to achieve comparable conversion rates. The cost-effectiveness of the entire CO₂ gasification process is critically dependent on the availability of inexpensive and sustainable sources of CO₂, such as those from industrial flue gases. However, the separation and purification of CO₂ from these sources is a complex and often energy-intensive process, which can negate the environmental benefits of the technology. It is also possible to develop modular CO₂ gasification units that can be integrated into existing industrial infrastructure, utilizing waste CO₂ emissions directly from the site, thereby reducing the necessity for CO₂ transportation and storage infrastructure. Such modular systems could be of particular advantage to small to medium-scale industries that are looking to enhance their sustainability profiles. However, to comprehensively evaluate the economic viability of CO₂ gasification, it is imperative to consider capital expenditures associated with energy consumption, maintenance, and catalysts. The sale of syngas, used for electricity, fuels, or chemicals, and potentially from carbon credits due to the process’s carbon capture features, has the potential to generate revenue. A thorough financial examination incorporating Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PBP) is essential for evaluating the economic viability of a project. It is evident that, despite the clear promise of CO₂ gasification in terms of sustainable energy production and carbon management, further research and development is required in order to overcome the challenges currently faced and to realize the full potential of this technology [104].

5. Syngas Cleaning

Tar is a mixture of varying molecular weight hydrocarbon molecules formed during the thermochemical conversion of organic materials. It has the potential to condense at low temperatures, which can result in clogging or blockage in devices such as internal combustion engines, gas turbine systems, combined heat and power (CHP) units, and solid oxide fuel cells, as well as filters and fuel lines. This underscores the necessity to convert the tar present in the producer gas, thereby facilitating the utilization of biomass gasification systems for power generation purposes [105].

In the field of gasification technologies, a prevailing issue has been identified as the presence of tar, which has been shown to be the most problematic group of compounds. The condensation of water vapor in the gas that is in the presence of tar has been demonstrated to result in the generation of contaminated water requiring management. Additionally, the gas contains solid particles, such as unreacted char or mineral components (ash), in varying quantities, which can form agglomerates within the system. In the context of catalytic applications of the producer gas, such as tar reforming, fuel cell utilization, or gas synthesis, it is imperative that the gas undergoes further purification from trace components containing sulfur, nitrogen, or chlorine [106].

The chemical structure of the tars is dependent upon the specific composition of the type of biomass. The quantity of tar produced may be diminished if the conditions of gasification, including pressure, temperature, the use of a gasifying agent, equivalence ratio, and other relevant parameters, are optimized [22]. The temperature of the process is a key factor in the quality of producer gas and tar content. At lower temperatures, a high tar concentration is obtained, while at higher temperatures, there is a significant reduction in tar content due to the acceleration of tar cracking and reforming reactions. At elevated temperatures, there is an enhancement in the H₂ content of the syngas, alongside a reduction in the levels of CO and CH₄. However, it is imperative to acknowledge that, for industrial-scale gasifiers, elevated temperatures can lead to fouling and a decline in cold gas efficiency of the producer gas. Thus, an inverse correlation is evident between the temperature and the tar content, irrespective of the gasification agent employed. In general, an increase in gasification temperature results in the formation of tar from highly stable species, such as light and heavy polycyclic aromatic compounds, as a consequence of the removal of monoaromatics and heterocyclic compounds [107].

The literature contains a discrepancy regarding the effect of pressure on tar concentration. While some authors have reported a reduction in tar concentration with elevated pressures, others have observed an increase in tar content under similar conditions. It has been highlighted that the pressure exerted a significant influence on the tar concentration, a factor that is contingent on the configuration of the gasifier design [22]. The tar content in the producer gas has been observed to decrease as temperature and pressure (1–5 bar) increase. In the majority of cases, an increase in temperature and/or pressure has been observed to result in a decrease in the primary and secondary aromatic tar components (phenols) [108]. In this study, the gasification of biomass was conducted in a 100 kW steam-blown dual fluidized bed operating within the temperature range of 800–870 °C, coupled to a 70 kW air-blown pressurized unit operating at temperatures between 800–900 °C and pressures ranging from 1 to 5 bar. Olivine has been used as a catalyst for tar conversion; consequently, it has been utilized as bed material in steam gasification and pressurized air gasification. Pressurized operation is deemed suitable for large-scale processes as it facilitates enhanced heat transfer in the bed, thereby optimizing gasification performance and concomitantly reducing tar formation [109].

Research has demonstrated that an increase in pressure results in a minor reduction in tar content. The experimental procedure involved pressurized circulating fluidized-bed gasification by steam-oxygen. In this process, the raw gas obtained in the gasification is subsequently filtered at approximately 600 °C prior to the catalytic reforming of tars and hydrocarbon gases. The feedstocks included wood pellets, crushed pellets, bark, and forest residue, with bed materials such as dolomite, magnesium oxide, and sand being utilized. The gasification pressure was in the range of 2.5–6 bar, with the operating temperature ranging from 860 to 930 °C [110].

Conversely, an escalation in pressure was observed to augment tar concentration [111]. The study investigates the effects of torrefaction level and pressure on product yields and composition during fluidized bed O₂/steam gasification of two different raw biomasses. As the pressure increased, there was an increase in the methane and carbon dioxide content, and a decrease in the hydrogen and carbon monoxide levels. Furthermore, analysis of the tars produced during gasification revealed that higher pressures led to increased tar yields. Conversely, torrefaction level exhibited an antithetical effect, characterized by diminished tar yields and a more pronounced decrease in the molecular weight distribution of tars with increasing torrefaction level.

A similar trend was observed when increasing the pressure from 1 to 2.5 bar [112]. Authors investigated the influence of temperature (750–840 °C), steam-to-biomass, and (S/B) ratio (0.8–1.2) with varying pressure in an allothermal bubbling fluidized bed steam gasifier. A marked decrease in the total tar content is observed, particularly in heterocyclic and light aromatic compounds. It has been demonstrated that, at atmospheric pressure, there is an increase in the naphthalene content in proportion to rising temperature. Conversely, in pressurized gasification, a decrease in naphthalene is observed as the temperature rises. Authors demonstrated that a greater reduction in tar is observed at higher temperatures in the presence of a higher steam content. Conversely, an increase in pressure results in an increase in the total tar content when the S/B ratio is low.

The three primary components of wood, i.e., cellulose, hemicellulose, and lignin, have been identified as the source for the primary tar. Cellulose and hemicellulose, which contain a significant amount of oxygen, are the primary contributors to oxygen-rich primary tar products, including alcohols, ketones, aldehydes, and carboxylic acids. In contrast, the occurrence of bi- and trifunctional mono-aromatics is predominantly associated with lignin. The resultant residual primary tar then forms secondary tar, which is composed of alkylated mono- and diaromatics, including heteroaromatics such as pyridine, furan, dioxin, and thiophene. The predominant reaction that leads to the transformation of primary tar into secondary tar is the elimination of small gaseous molecules. The presence of over 800 °C tertiary tar has been identified. These tars are also referred to as recombination or high-temperature tars. However, it is noteworthy that tertiary tar can also be formed at lower temperatures by cycloaddition according to Diels–Alder. This process involves the formation of additional cyclohexene rings, which undergo aromatization through hydrogenation or dehydration [108].

A systematic classification of pyrolysis as primary, secondary, and tertiary products has been reported, which can be used to compare the derivatives of the various reactors utilized for pyrolysis and gasification based on the gas-phase thermal cracking reactions. This yielded four major product classes, which can be used to facilitate comparison between different reactors [71]. The primary products are characterized by cellulose-derived products, including levoglucosan, hydroxyacetaldehyde, and furfurals. Additionally, analogous hemicellulose-derived products and lignin-derived methoxyphenols are present. The secondary products are conformed by phenolic and olefin compounds. The tertiary products are methyl acenaphthylene, methylnaphthalene, toluene, and indene, which are methyl derivatives of aromatics. The fourth category of products comprises PAHs that lack alkyl substituents, such as benzene, naphthalene, acenaphthylene, anthracene, phenanthrene, and pyrene. These are collectively referred to as condensed tertiary products. There is a consensus on the relative levels of tar production, with updraft gasifiers producing the most tar, downdraft reactors producing the least, and fluidized bed gasifiers in the middle. It is also recognized that updraft gasifiers produce primary tars with some secondary character. In contrast, downdraft gasifiers produce mainly tertiary tars, while fluidized beds produce a mixture of secondary and tertiary tars. In updraft gasifiers, the pyrolysis reaction occurring in the fresh feed forms primary tars, whereas in a downdraft gasifier, the high temperature of the final tar cracking achieves a significant degree of char gasification [71].

In accordance with the molecular weight of tar compounds, a categorization of tar components into five groups has been reported elsewhere [113]:

Class I: Undetectable compounds by GC (more than eight aromatic rings and calculated by subtraction of total gravimetric tar minus detectable compounds by GC).

Class II: Heterocyclic compound (water-soluble single aromatic rings containing heteroatoms), such as pyridine, quinoline, isoquinoline, phenol, and cresol.

Class III: Light aromatic compounds (single aromatic rings, which are non-condensable), such as toluene, xylenes, ethylbenzene, and styrene.

Class IV: Light PAHs (condensable at low temperatures and even at low concentrations, 2–3 rings aromatic compounds), such as naphthalene, methylnaphthalene, indene, byphenyl.

Class V: Heavy PAHs (condensable at very low temperatures and low concentrations, aromatic compounds larger than three rings, usually between four and seven aromatic rings), such as pyrene, chrysene, coronene, fluoranthene, perylene.

Nevertheless, when model compounds are employed to represent the tar structure, benzene is frequently the predominant structure utilized to represent tars [114,115,116]. A description of the tar growth mechanisms has been reported elsewhere [117], while a description of the PAHs formation from polyphenolic compounds is also discussed [118]. In this approach, phenol is converted to cyclopentadiene by CO-abstraction and undergoes H-abstraction to form cyclopentadienyl radicals [118]. Phenol is converted to cyclopentadiene via keto/enol tautomerization, initially forming cyclohexadienone, and subsequently undergoing decarbonylation. The cyclopentadienyl radical is formed by the loss of a hydrogen atom from cyclopentadiene [119]. Cyclopentadienyl radicals combine to form naphthyl, which in turn can lose a hydrogen atom to produce a naphthyl radical. Following the combinations between naphthyl and cyclopentadienyl radicals, polycyclic aromatic compounds are formed. It is anticipated that these mechanisms will occur in the formation of PAHs, which will then undergo condensation to produce naphthalene and subsequently chrysene. When tar is formed, it will undergo several reactions in the presence of a gasifying agent.

Table 5. Chemical reactions of tar.

Reaction	−ΔH01173 a, kJ mol−1
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}+\mathrm{n}{\mathrm{H}}_2\mathrm{O}\to \left(\mathrm{n}+{\displaystyle \frac{\mathrm{m}}{2}}\right){\mathrm{H}}_2+\mathrm{n}\mathrm{C}\mathrm{O}$	−876 b
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}+\mathrm{x}{\mathrm{H}}_2\mathrm{O}\to {\mathrm{C}}_{\mathrm{x}}{\mathrm{H}}_{\mathrm{y}}+\mathrm{p}{\mathrm{H}}_2+\mathrm{q}\mathrm{C}\mathrm{O}$	−123 c
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}+\left(2\mathrm{n}-{\displaystyle \frac{\mathrm{m}}{2}}\right){\mathrm{H}}_2\to \mathrm{n}\mathrm{C}{\mathrm{H}}_4$	713
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}+\mathrm{x}{\mathrm{H}}_2\to {\mathrm{C}}_{\mathrm{x}}{\mathrm{H}}_{\mathrm{y}}+\mathrm{q}\mathrm{C}{\mathrm{H}}_4$	104 c
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}+\mathrm{n}{\mathrm{C}\mathrm{O}}_2\to \left({\displaystyle \frac{\mathrm{m}}{2}}\right){\mathrm{H}}_2+2\mathrm{n}\mathrm{C}\mathrm{O}$	−1105 b
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{2\mathrm{n}+2}\to \mathrm{n}\mathrm{C}+\left(\mathrm{n}+1\right){\mathrm{H}}_2$	73
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{2\mathrm{n}+2}\to {\mathrm{C}}_{\mathrm{n}-1}{\mathrm{H}}_{2\mathrm{n}-2}+\mathrm{C}{\mathrm{H}}_4$
${\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\mathrm{m}}\to \mathrm{C}+{\mathrm{C}}_{\mathrm{x}}{\mathrm{H}}_{\mathrm{y}}+\mathrm{gases}$

^a Tabulated enthalpy values (at 1173 K) are for toluene (C₇H₈) used as a tar model. ^b If n is 7. ^c If x is 1.

summarizes different reactions involved in the catalytic reforming of tar to gaseous compounds [120,121].

5.1. Tar Mitigation Techniques

Consequently, a number of strategies for the removal of tar have been developed. Figure 8 provides a schematic representation of these methods [22,122]. A categorization of these strategies is provided as follows: (i) primary, for the removal of tar in the gasifier, and (ii) secondary, for the cleaning of the syngas produced. About the primary methods, the mitigation techniques generally comprise the design of improved gasification process, the utilization of in situ catalysts, and the effective control of operating conditions (temperature, equivalence ratio, and gasification agent), and selection of the biomass type. Secondary methods do not interfere with gasification operations and may be classified into physical (cyclones, cooling towers/wash columns, and electrostatic precipitators) and chemical treatments (thermal and catalytic processes). Although primary tar elimination methods significantly reduce the process costs, they may not be efficient enough to remove all tar. In such instances, a combination of in situ and ex situ methods may be a viable option.

Secondary strategies employed for the mitigation of tar are based on wet and hot gas cleaning, depending on the range of temperature. Cold gas cleanup is carried out at conditions near the ambient whereas the hot gas cleanup is carried out at temperatures commonly higher than 400 °C. In the wet gas cleaning processes, water scrubbing and Venturi scrubbers are used to condense the tar compounds. The exit gas temperature from wet cleaning methods ranged between 35 and 60 °C, which resulted in a loss of heat, and the water condensate therefore required treatment before disposal [123]. The removal of tar involves wet separation with the use of washing agents. Historically, water was utilized for this purpose; however, this approach resulted in significant contamination from water-soluble components, such as phenol or ammonia, as well as the predominantly insoluble PAHs. Consequently, this mixture necessitates separation and further treatment prior to its disposal in the environment. Other approaches in the gas washing have also incorporated organic solvents or washing agents, such as plant oil or biodiesel (fatty acid methyl esters) [105].

Regarding the chemical treatments comprised in the secondary methods, these may include both the thermal and the catalytic processes. Thermal cracking entails the utilization of elevated temperatures, typically ranging from 1100 to 1300 °C, for the purpose of decomposing substantial organic compounds. Conversely, if lower temperatures are employed, then extended residence times are required for effective cracking [105]. The catalytic tar cracking method has been identified as one of the most effective techniques for reducing tar in producer gas. Consequently, the quality of the producer gas can be enhanced without the need for wastewater or matter disposal [124]. A substantial body of research has been dedicated to investigating the catalytic tar cracking problem, with numerous studies concluding that a Ni–based catalyst exhibits superior performance in terms of tar mitigation when compared to other transition-metal-based catalysts [125]. Nevertheless, the tar concentration in the producer gas remains at a negligible level, because of numerous factors, including gas flow rate, temperature, catalyst content, and feedstock size [123]. However, the utilization of catalysts in tar cracking processes may lead to deactivation through poisoning or carbon deposition, attrition, and other mechanisms.

5.2. Physical/Mechanical Processes

The removal of tar and dust after gasification includes various processes, such as the use of cyclones, filters (bag, baffle, ceramic, and fabric), rotary separators, electrostatic precipitators, and wet scrubbers. In a biomass gasification plant, the system components are employed for the purpose of cleaning gases. This process is integral to achieving the requisite quality parameters for the producer gas, which is then made available for subsequent utilization. The gas cleaning systems currently in use are based on the application of processes adapted from dedusting and washing, which interact to meet the requirements of biomass gasification plants (dust and tar loading). In addition, there are systems that use either a single–stage or a two-stage tar or particulate separation process. In a two-stage separation process, the separation of particles and tars takes place in separate units due to the different temperatures at which the processes are carried out to prevent tar condensation, which occurs between 150 and 250 °C, depending on the type of gasifier and operating conditions. This divides the treatment of dust and tar into hot and cold gas processes. For temperature ranges between 20 and 60 °C, the process can be carried out using packed columns and Venturi scrubbers, among others, to remove tar and other solid particles. For temperature ranges between 140 and 800 °C, filtration or dust electrostatic precipitators are effective options to remove solid particles and tars [72]. Thus, the physical tar removal processes include dry and wet gas scrubbing. The dry process is applied to gas streams at high temperatures before cooling, whereas the wet process is applied to gases after cooling where the producer gas temperature is below 60 °C [121].

The use of filters is a common method employed to eliminate a range of undesired compounds from the producer gas encompassing tar, sulfur, nitrogen, and/or halides. Once cooled, the tar is extracted from the producer gas using solvents such as biodiesel. Wet scrubbing is necessary to remove the nitrogen and halides. It is also crucial to eliminate the sulfur compounds as they can poison the catalysts employed in other processes. Nevertheless, it is possible that some tars may remain in the producer gas, which can be removed through various methods, including catalytic and non-catalytic (physical/mechanical) processes.

Dust separators can be classified into three main categories: impact separators, deflection separators, and cyclone separators. Gas-permeable fabrics and gas-permeable porous sintered materials are considered filtration deduster. If the separation takes place within the medium, it is classified as deep-bed filtration. If sufficient dust is deposited to form a layer on the filter media, it acts as a surface filtration as it is capable of separating very small particles, depending on the particle size distribution of the filter cake formed, as reported by Lettner et al. [72]. Of course, pressure drop occurs, and maintenance of filters is required.

The filtration deduster utilizes a filtration system to collect dust particles, and a different filter can be used. For example, baghouse filters represent a key component of the filtration process, wherein the gas flows through a network of tubes or bags. The particles are retained on the external surface of the filter while the clean gas flows upwards. Subsequently, the accumulated dust is dislodged from the bags or tubes through the application of vibration. In the case of temperatures below 100 °C, the use of materials such as cotton, polyester, or PVC is recommended. For temperatures between 150 and 300 °C, polytetrafluoroethylene (PTFE) and glass fiber felt are appropriate materials for use. If temperatures exceed 300 °C, it is recommended that fabric, quartz, glass, graphite, and felts composed of ceramic, mineral, or metallic materials be utilized. For temperatures exceeding 600 °C, the use of felts comprising ceramic or quartz fibers, aluminosilicate ceramics, and silicon carbide is recommended [72]. Multistage filter candles or cartridges are composed of metal or ceramic materials and are capable of withstanding temperatures more than 500 °C. The filtration process entails the passage of gas through the filter, whereby dust particles are retained on the external surface. Regeneration is achieved through the injection of pressurized gas, whereby the dust is collected at the base of the apparatus. The ceramic crossflow filter is composed of permeable ceramic sheets that facilitate the passage of gas through channels, thereby achieving a high surface area that is dependent on the geometry. The electrostatic precipitator is a device that employs electrostatic forces to separate electrically charged particles. The precipitator allows for the flow of gas, whereby particles become charged under the influence of an electrical field. As the gas flows, the charged particles are attracted to the walls, resulting in their separation from the gas [72]. The efficiency of this separation is dependent on the gas flow rate.

Figure 9 presents a summary of the separators employed in the gas cleaning process.

5.3. Tar Removal

The removal or conversion of tars may be achieved through the application of a range of processes, including wet scrubbers, wet electrostatic precipitators, thermal treatment, and adsorption by fixed beds. The utilization of catalysts has also been demonstrated to enhance the conversion of tar. Both naturally occurring dolomite and nickel-based catalysts have been employed in the tar reforming reaction. It is also advantageous to remove CO₂ from the syngas for commercial applications, and the use of activated carbon, zeolites, and metal oxides as sorbents is recommended [71].

5.3.1. Wet Scrubbers

The removal of small particles (0.05 µm) from the gaseous medium can be achieved through the utilization of wet scrubbers, which can be categorized into four principal types: packed column (flow rate of 1 (m s⁻¹) and particle sizes ranging from 0.7 μm to 1.5 μm), jet (flow rate from 10 to 25 (m s⁻¹) with particle sizes between 0.8 μm to 0.9 μm), dip (particle size from 0.6 μm to 0.9 μm and gas flows of 8–20 (m s⁻¹)), and Venturi scrubber (particle size from 0.6 μm to 0.9 μm and gas flows of 8–20 (m s⁻¹)) [71].

A series of experiments was conducted with the objective of assessing the efficacy of various scrubbing agents in the removal of light PAHs considered as tars. The agents subjected to testing included water, diesel and biodiesel fuels, vegetable oil, and engine oil. The results demonstrated that the efficiency of these agents decreased in the following order: diesel fuel > vegetable oil > biodiesel > engine oil > water. However, due to the cost and evaporation of diesel fuel, vegetable oil was found to be the most efficient scrubbing liquid [126]. As previously reported [127], the utilization of vegetable oil in conjunction with biomass char as an adsorbent bed resulted in the removal of tar by more than 95%. The deployment of scrubbing agents is contingent upon the specific type of pollutant to be extracted from the gas. All wet gas cleaning systems produce wastewater that is contaminated with inorganic and organic pollutants. The concentration of these pollutants is significant, even in the case of gasifiers with low tar production. The wastewater contaminants encompass a diverse range of organic compounds, acids, ammonia, and metals. A variety of technologies are employed for the treatment of wastewater prior to its final disposal. These include extraction with organic solvents, distillation, adsorption on activated carbon, wet oxidation, oxidation with ozone or hydrogen peroxide, incineration, and biological treatment [71].

5.3.2. Wet Electrostatic Precipitators (Wet ESPs)

Furthermore, the utilization of wet electrostatic precipitators (wet ESP) is advised in instances where the objective is to eradicate fine solids and liquid droplets from the gas stream. This is due to the fact that the dry ESP may become increasingly impeded by the progressive condensation of tar on the precipitation electrode, which would consequently restrict the removal of particles [72].

5.3.3. Fixed Bed Adsorber

These systems are particularly suited to analytical procedures within a laboratory setting. The fixed bed adsorber employs an adsorbent material, such as activated carbon with a grain size of up to 1.0 mm. Such devices are employed for not only the separation of tar, but also the processing of wastewater [72].

5.3.4. Thermal Treatment

The prevailing conclusion is that temperatures more than 1000 °C and a sufficient residence time are required to thermally destroy the refractory unsubstituted aromatics. However, thermal decomposition of such compounds may result in the formation of soot. It is also observed that benzene has the lowest reactivity to thermal treatment of the light aromatics. The addition of excess steam reduces the formation of refractory tars and improves the formation of phenol as an oxygenate compound with minimal conversion of aromatics, making the tar suitable for catalytic reforming. Partial oxidation by the addition of oxygen or air to steam forms refractory tars at lower levels by which the selective oxygen addition allows preferential oxidation of tars [71]. This type of tar treatment is more likely to be seen as a possible process step to reduce the presence of tar in gas production [72].

5.3.5. Catalytic Tar Treatment

This treatment of tar is based on reactions that occur in catalysts as a result of the reduction in LHV and cold gas efficiency. Three types of additives used as catalysts have been tested to improve tar conversion: natural materials, alkali and salts, and metal-based catalysts. Taking these possibilities into account, two catalytic processes have been developed to convert tar to lighter products. The initial process involves reacting with the tar directly inside the gasifier (in situ). In contrast, the second process (ex situ) involves treating the stream, which contains the hot syngas and tar, in an external unit [128].

Naturally occurring materials like dolomite, olivine, magnesite, and limestone exhibit catalytic activity during the tar conversion. Natural dolomite is the most widely used catalyst, primarily due to its ease of availability, cost-effectiveness, and disposable nature. It has been shown to have a substantial capacity to reduce the tar content of syngas. However, a significant challenge associated with the utilization of dolomite is its deactivation, which is a consequence of its rapid calcination within the gasifier. Additionally, calcined dolomite (CaO·MgO) or olivine ((Mg,Fe)₂SiO₄) have demonstrated that they are active during biomass gasification with air [20]. However, dolomite undergoes attrition, resulting in more particulate matter in the producer gas compared to olivine [129]. Thermal pre-treatment at 1200 °C enhanced the catalytic of olivine, resulting in increased hydrogen production [130]. Additionally, an increased H₂/CO ratio and greater tar reduction were observed [131].

Group IA alkali metals and salts are difficult to recover and tend to agglomerate with increasing temperature. Although these alkalis result in tar reduction, they also result in a higher amount of hydrogen in the producer gas [68,132].

Transition metal-based catalysts are commonly used due to their ability to efficiently convert tar at high rates. Among them, both the monometallic and bimetallic Ni-based catalysts are widely employed. In a study conducted by Xu et al. [133], alumina-supported Ni catalysts were tested in the co-gasification of biomass and polyethylene using steam as a gasifying agent to obtain higher yields of syngas. The hydrogen production during the gasification of low-density polyethylene and pine sawdust was improved by using Ni/CaO/C catalysts. The nickel load and support ratio (CaO:C) were varied to achieve this, as reported by Chai et al. [134]. In the decomposition of tar using toluene as a model compound, alumina-supported nickel catalysts were tested and reacted mainly through steam reforming. Ni/Al₂O₃ catalyst decomposed ammonia, reaching an equilibrium-limited conversion of 85%. Metal-based catalysts improved producer gas quality due to high tar conversion, but the Ni-based catalysts deactivated due to carbon deposition [120,131]. Most studies that have reported on the use of nickel as an active metal have focused on increasing hydrogen production rather than reducing tar while keeping Ni content in the range of 10–20 wt% [128]. Additionally, the catalysts were used outside of the gasifier, in a second reactor operating between 250 and 800 °C. Nickel catalysts commonly undergo deactivation over longer periods of time due to the presence of H₂S derived from biomass gasification [128].

The Ni/CeO₂/α–Al₂O₃ catalysts synthesized by the co-impregnation method exhibited excellent performance in tar conversion and reduced the coke formation when using cedar wood as a biomass source. Additionally, the catalyst enhanced hydrogen formation. Tomishige et al. [135] reported that Ni/CeO₂/Al₂O₃ catalysts could replace Rh/CeO₂/SiO₂ catalysts due to the synergy between Ni and CeO₂. The H₂/CO ratio and coke deposition in the product were found to be similar in both cases.

Fe–based catalysts decorated with Ni dispersed on MgAl₂O₄ spinel support were used to catalyze the upgrading of surrogate biomass, resulting in improved production of syngas. The presence of hematite was noticeable in the Fe-based catalysts. This was due to the promotion of the reverse water–gas shift and dry reforming of methane reactions by the Ni–Fe/MgAl₂O₄ catalyst. The catalyst with 2 wt% Ni content showed higher CO yields compared to that with 5 wt% Ni when a low volume of H₂ was fed [128]. Although the material was not tested for tar conversion, it is a suitable candidate for use in biomass gasification [136]. Dolomite loaded with perovskite oxides can be used in biomass gasification to convert tar more efficiently than natural dolomite due to the presence of oxygen sites. This enhances the production of hydrogen and improves catalytic activity when using La_0.8Ce_0.2FeO₃/dolomite as a catalyst [137]. La_0.8Sr_0.2Ni_0.8Fe_0.2O₃ perovskite has also been shown to effectively convert tar at 700 °C [138].

The use of Rh/CeO₂/SiO₂ catalysts improved the efficiency of cold gas when compared to commercial catalyst, dolomite, and non-catalyzed reaction. Additionally, hydrogen production was increased, and catalyst deactivation by coke was negligible despite a slight reduction in specific surface area [139]. NiFe–NiFe₂O₄ catalysts were synthesized using char as a support. The biomass (pine sawdust) was impregnated with metal precursors and then subjected to fast pyrolysis at 600 °C for 1 h. The catalyst was tested for biomass gasification and tar catalytic cracking in a two–stage fixed bed reactor. The catalyst promoted a tar conversion rate of over 92%. The unconverted tar consisted mainly of phenolic compounds, PAHs, oxygen–containing heterocyclic compounds, and other oxygen–containing compounds [140]. Rönkkönen et al. [141] tested platinum, rhodium, palladium, ruthenium, iridium, and nickel as catalysts with modified zirconia as support during the decomposition of tar and ammonia. Tar was modeled as a mixture of naphthalene and toluene. At temperatures above 850 °C, all catalysts achieved around 90% conversion of tar model compounds, with the order of activity being Rh ≈ Ni > Pd > Ir > Ru > Pt. For ammonia, the Ni catalyst exhibited the highest conversion rate, followed by the Ru and Ir catalysts, while the bare zirconia support promoted some degree of conversion, with other catalysts being inactive during the reaction. Previous studies stated that the order of catalyst activity during ammonia conversion is Ru > Rh ≈ Ni > Pt ≈ Pd [142]. It has been reported that the Rh/CeO₂/SiO₂ catalyst exhibits superior performance in the gasification of cellulose at temperatures ranging from 550 to 650 °C and has tolerance against carbon deposition and sulfur compared to the Ni/CeO₂/SiO₂ catalyst [141].

Cobalt–based catalysts have been demonstrated to exhibit comparable behavior to nickel–based catalysts. During the gasification of cedar wood, CoFe/α–Al₂O₃ catalysts were evaluated, and it was observed that iron exerted a suppressive effect on coke deposition. In the steam reforming process, toluene was employed as a tar model compound, and its conversion was found to be influenced by the partial pressure of hydrogen [143]. Tang et al. [144] reported the effectiveness and high stability of a dispersed cobalt–based catalyst in toluene conversion during 100 h of time on stream at 400 °C. Monolithic biochar–supported catalysts impregnated with Co, Co/Fe, and Co/Ni were synthesized. The addition of a second metal, such as Fe and Ni, improved the stability of the catalysts and enhanced the decomposition of tar. The CoNi/monolithic biochar catalyst demonstrated exceptional tar conversion rates of over 90%, even after multiple cycles. Additionally, the catalyst facilitated the production of hydrogen due to the synergistic effect of Ni and Co. The presence of Fe and Ni on the catalyst inhibited carbon deposition in a two–stage fixed bed reactor where biomass pyrolysis and tar cracking occur [145]. Figure 10 shows the treatments commonly used to reduce the presence of tar in the gas, while

Table 6. Catalysts commonly used in the tar conversion and/or biomass gasification.

Ref.	Catalyst and Composition	Main Results/Observations
[138]	La0.8Sr0.2Ni0.8Fe0.2O3 Preparation method: Sol-gel synthesis	Bamboo sawdust was used as feed. Pyrolysis of biomass was conducted at 700 °C using approximately 0.6 g of catalyst. The syngas yield was 475 (mL g−1), and the total gas yield was 606.7 (mL g−1)
[140]	NiFe-NiFe2O4/char Preparation method: in situ carbothermal reduction of Ni/Fe metal chloride impregnated sawdust. Ni: 0.42 wt%; Fe: 0.11 wt%; C: 89.20 wt%; O: 10.27 wt%	Tar conversion: 92.54% and 81.8% after three cycles of reuse (at T = 600 °C).
[144]	Co0.15-C2H3O2− Preparation method: Ion exchange method Co: 16 wt%	Tar model: toluene. Toluene conversion: 20.6% and 15.6% after 100 h of time on stream (0.1 g of catalyst at T = 400 °C, GHSV = 12,000 (mL h−1 g−1), and steam–to–carbon ratio = 0.68).
[145]	CoNi supported on monolithic biochar. Preparation method: Impregnation and carbonization. Co: 2.4 wt%; Ni: 2.49 wt%; C: 93.92 wt%; O: 1.19 wt%	Pyrolysis and cracking of pinewood at 600 °C and 700 °C, respectively, carried out in a two-stage fixed bed reactor. Tar conversion: 91% (after five cycles of reuse).
[136]	Ni-Fe/MgAl2O4 Preparation method: Successive wet impregnation. Ni: 2–10 wt%; Fe: 30 wt%; Mg: 10 wt%	Biomass gasification carried out with 200 mg of catalyst, volume ratio of CO2 (15%):H2 (0–60%):CH4 (0–15%), WHSV of 30 (L g−1 h−1), temperature between 400 and 700 °C.
[137]	La0.8Ce0.2FeO3/dolomite Preparation method: Sol-gel synthesis La0.8Ce0.2FeO3 represents the 10 wt% of the dolomite mass	Biomass gasification carried out with 8 g of pine wood and 4 g of catalyst at 850 °C.
[143]	CoFe/α-Al2O3 Preparation method: Co-impregnation Co: 12 wt%; Fe/Co molar ratio: 0.25	Tar model: toluene. H2/CO ratio of 4.6 (for steam reforming of toluene). Toluene conversion: 62.7% [H2 flow of 3.8 (mmol min−1)].
[141]	Precious metal (Rh, Pd, Ir, Ru, Pt) and Ni over modified ZrO2 Preparation method: Incipient wetness Rh, Pd, Ir, Ru, or Pt: 0.5 wt%; Ni: 8 wt%	Tar model: toluene/naphthalene (90/10). Tar conversion: ~95% (with Rh and Ni catalysts above 850 °C). Ammonia conversion: 75% with Ni catalysts.
[136]	Ni/CeO2/α-Al2O3 Preparation method: Co-impregnation Ni: 4 wt%; CeO2: 30 wt%	Tar model: cedar wood gasified. H2/CO ratio of 2.8 (for cedar wood steam gasification), low tar and coke amounts.
[120]	Ni/α–Al2O3 Preparation method: Impregnation Ni: 13.3 wt%	Tar model: toluene. Toluene conversion: ~100% (at P = 2 MPa and T = 900 °C).

summarizes some of the materials typically used in the catalytic tar conversion.

5.4. Recent Tar Mitigation Techniques

Further measures involving redesign of the gasifier or development of innovative designs are also essential to promote tar abatement. The aim of those reactor modifications usually pursues the improvement of residence time, temperature profiles in the gasifier, and the gas–solid contact. The existing research efforts have been focusing on the fluidized beds, with the most common approaches including the secondary air injections or candle filters and the optimization of the feeding location or hydrodynamic regime [22].

The two–stage gasification system, comprising separate pyrolysis and reduction zones, and the two-stage air entry, has attracted significant attention [146]. The gasification design developed at the Indian Institute of Science was found to be more promising from technical and economic standpoints. The configuration of the gasification system enables a dual air entry, with air entering from both the nozzle and the top of the reactor. The presence of an open top facilitates the movement of the reaction front in an upward direction, thereby creating a high–temperature zone. This, in turn, ensures a high residence time for the gases at elevated temperatures. The reactor design is capable of processing both woody and non–woody biomass with an average composition of the syngas with air as the oxidizing agent as follows: H₂ (19 ± 1%), CO (19 ± 1%), CH₄ (1.5%), CO₂ (12 ± 1%), H₂O (2 ± 0.5%), and the mean calorific value is 4.6 ± 0.2 (MJ kg⁻¹) [100].

The tar cleaning system (OLGA) was designed to separate heavy and light tars, ensuring they remain as distanced from the condensing water as possible. In early 2001, the Energy Research Centre of the Netherlands initiated the development of a new technology called “OLGA”. The development of this patented technology began with a mechanistic study of tar removal using a scrubbing liquid that differs from water. The mechanistic study indicated a strong possibility that an alternative scrubbing liquid could result in effective deep tar removal. Experiments demonstrated the efficacy of the OLGA process in achieving negligible levels of tars, i.e., tar dewpoints below −15 °C. This process yields gases that are tar–free, and as such, they are suitable for use in gas engines. Furthermore, they are also suitable for use in Fischer–Tropsch and synthetic natural gas. In comparison with alternative conventional tar removal systems, the specific investment costs for relatively small OLGA units are relatively high. However, in an economy of scale, the process is easily scalable and does not become more complex upon scaling–up. It has been demonstrated that, at sizes above 4000 (Nm³ h⁻¹), which corresponds to ~10 MW biomass input, the specific investment costs appear to stabilize [147]. The technology was launched to the market in 2007. The system is comparatively complex and is therefore primarily suited to larger–scale applications. It is intended to be combined with a two-stage fluidized bed gasification system called MILENA, where steam gasification and combustion of solid residues are combined for syngas production. The collected heavy tar can be recycled in conjunction with the organic washing liquid to the combustor section of the reactor, ensuring that hazardous material is not disposed of [106].

Other advancements in the field of gas washing have also incorporated the utilization of organic solvents or washing agents, such as vegetable oil or biodiesel. The fast internally circulating fluidized bed (FICFB) process utilizes rape seed oil methyl esters to wash the gas [106]. The fundamental principle underlying the FICFB concept is to compartmentalize the fluidized bed into two distinct zones: a gasification zone and a combustion zone. A circulation loop of bed material is established between these two zones, whilst the gases remain segregated. The bed material, which is in constant circulation, functions as a medium for heat transfer from the combustion zone to the gasification zone. The utilization of steam as a gasification agent enables the FICFB process to generate producer gas containing negligible quantities of nitrogen [148]. This advanced design of gasifier has the capacity to generate producer gas with LHV of 11.5–13.4 (MJ Nm⁻³), which is two to three times higher than that yielded by conventional gasification systems. This objective is completed through the implementation of a dual–reactor configuration, encompassing gasification and combustion stages, respectively. The quantity of tars in the producer gas was found to range between 0.9 and 4.7 (g Nm⁻³), with naphthalene and acenaphthylene being the most prevalent compounds. It has been demonstrated that gasification temperatures more than 750 °C have a positive effect on both the producer gas yield and cold gas efficiency. Furthermore, utilization of olivine as a catalytic bed material has been shown to increase the producer gas yield by 20% when compared with a non–catalytic bed material [149].

The technology known as Quench Coupled with Absorption Technology (QCABT) for the removal of tar is also another approach to remove tars. The process is based on a quench scrubber and an absorber, in which the producer gas is cleaned by heavy tar itself and the absorbent in an orderly manner. In the initial section of the QCABT, the producer gas undergoes a process using heavy tar. The heavy tar, having been separated from the fine solids, is condensed and recycled as the scrubbing oil or utilized as a product. In the subsequent stage, lighter gaseous tars are absorbed, and the absorbent is saturated with the lighter tars [105]. QCABT exhibit both cooling and absorption effects in the quench scrubber, providing a total removal efficiency of 98%. Preliminary testing has revealed that the tar has been successfully removed using the QCABT method, yielding a residual tar concentration of less than 10 (mg Nm⁻³) [150].

Plasma gasification has been demonstrated to heat the biomass feedstock to temperatures higher than 3000 °C (and, in some cases, up to 15,000 °C). Furthermore, it has been reported that the gasification of biomass in the plasma occurs very quickly, without any intermediate reactions; syngas with elevated levels of hydrogen and carbon monoxide, reduced carbon dioxide content, minimal tar amount, and a high heating value is generated [105].

Achieving economically and environmentally efficient energy recovery from biomass gasification is contingent on overcoming tar-associated problems. Indeed, tar formation has been shown to waste 5–15% of the effective energy from biomass gasification, thereby reducing the process efficiency [151]. The polymerization of tar compounds within downstream pipelines, heat exchangers, or filters can lead to corrosion, fouling, and clogging, resulting in a decline in process efficiency, an increase in emissions, and elevated operational costs [22]. Consequently, the necessity for the removal of tar becomes evident, and the employment of conventional and contemporary techniques for mitigation is paramount.

Another technique of mitigation, distinct from tar, involves the capture of CO₂. The integration of gasification with CO₂ capture has been identified as a highly effective strategy for mitigating greenhouse gas emissions, whilst concurrently utilizing energy derived from diverse feedstocks [152]. The process of capturing CO₂ involves the isolation of this gas from the stream of gases produced during the gasification of MSW [152]. For example, in situ CO₂ capture via Ca–based sorbent has been reported [153].

6. Influence of Pressure Drop and Heat Transfer in Gasification

Pressure drop is a pivotal operating parameter in process control as well as for downstream equipment and excessive pressure drop can result in suboptimal system performance and elevated energy consumption. The pressure drop between the top and bottom of the gasifier exhibited a global increase, attributable to two phenomena. Firstly, solid particles are consumed by chemical reactions, resulting in a decrease in their size and porosity of the bed, as well as the formation of solid residues. Secondly, a minor flow disturbance has occurred. When the pellets are mechanically stable, and their thermal decomposition did not generate strong resistance to the gas flow, low pressure drop values are attained. This outcome is favorable from an industrial applications perspective [154]. It is advantageous that the pressure drops are minimal, with their determination across the gasifier, packed–bed scrubber and the filter box, as this facilitates the operation of an internal combustion engine [155].

In order to achieve steady-state operation, it is imperative that any variation in the energy flux over distance, designated as (Δz), is equivalent to the rate of energy released by reactions within the volume, minus the rate of work completed by the gas. The pressure gradient (dP/dz) is a quantity that depends on the surface velocity of the gas. Ergun equation for flow through a bed of randomly packed particles of given diameter relates the pressure gradient of the fluid flowing through the bed of solid particles to the physical properties of the bed [156]. Ergun equation, as applied in its reduced form can be expressed as follows [157]:

$$-{\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{z}}}=1183\times \left({\displaystyle \frac{{\mathsf{\rho }}_{\mathrm{g}\mathrm{a}\mathrm{s}}}{{\mathsf{\rho }}_{\mathrm{a}\mathrm{i}\mathrm{r}}}}\right)\times {\mathrm{v}}_{\mathrm{g}\mathrm{a}\mathrm{s}}^2+388.19\times {\mathrm{v}}_{\mathrm{g}\mathrm{a}\mathrm{s}}-79.896$$

(7)

where v_gas is the superficial gas velocity (m s⁻¹), ρ_gas and ρ_air is the fluid mass density of the gas and air, respectively.

The phenomenon of pressure drop in Ergun correlation is indicative of two factors: firstly, viscosity, and secondly, kinetic energy loss. In circumstances where the Reynolds number is elevated, the viscosity factor becomes less significant in comparison to the kinetic energy factor. It is therefore imperative to take account of the loss of kinetic energy, whilst neglecting the viscosity factor. However, at intermediate Reynolds numbers (Re < 300–400), both factors must be considered. The relationship between the pressure drop (ΔP) and the bed height (h) and superficial gas velocity (${\mathrm{v}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$) can be approximated by Equation (7), which is a simplified model of the Ergun equation.

$$\mathsf{\Delta }\mathrm{P}={(\mathrm{C}}_1\times {\mathrm{h}}^{{\mathrm{C}}_2}\times {{\mathrm{v}}_{\mathrm{g}\mathrm{a}\mathrm{s}})}^{{\mathrm{C}}_3}$$

(8)

where C₁, C₂, and C₃ are parameters to be determined. These parameters are already reported elsewhere [158].

Experimental studies were conducted in order to examine the effect of biomass feed size and air flow rate on the pressure drop in a gasification reactor. The findings revealed that both the sphericity and the size of the particles present within the reactor exert a significant influence on the bed porosity. It was observed that a decrease in bed porosity resulted in an increase in pressure drop, and conversely, an increase in bed porosity led to a decrease in pressure drop. The height of the bed also influenced the pressure drop, and a direct correlation between the height of the bed and the pressure drop within the reactor can be seen, irrespective of the size of the particles [158]. Differential pressure measurements have usually been used to estimate axial profiles of cross–sectional average solids concentrations in gasifiers; this is completed by considering that the pressure drops due to gas–solid suspension to wall friction and particle acceleration are negligible. This approach is predicated on the assumption that the pressure drops due to gas–solid suspension to wall friction and particle acceleration are negligible, which is widely accepted due to its non-intrusive and cost-effective nature. However, experimental findings have demonstrated that the contributions of friction and acceleration to the total pressure drop cannot be disregarded under specific operating conditions [159].

In earlier studies, it was determined that the pressure drop resulting from friction accounted for 20–40% of the total measured pressure drops in dilute flows [160]. Furthermore, Hartge et al. [161] found that there was good agreement at lower gas velocities, but that particle–wall friction was significant at higher gas velocities. Other studies have demonstrated that the maximum contribution of frictional pressure loss to the total pressure drop was less than 20% [162]. Additionally, the particle diameter was found to have a substantial influence on the particle friction factor. The study of friction between co-current downflow gas–solid flow and column wall was reported by Qi et al. [159] and completed by measuring apparent and actual solids concentrations. The authors proposed a model to predict pressure drops due to friction between the gas–solid suspension and the downer wall and found variations in solids concentrations, particularly under conditions of higher superficial gas velocities. Furthermore, the influence of particle diameter on frictional pressure drops is found to vary with superficial gas velocity.

Regarding the energy transfer, the mechanisms and rates of heat transfer are influenced as the reaction front advances through the bed, encompassing the drying, pyrolysis, reduction, and combustion stages. In the context of a fixed bed downdraft gasifier, the incorporation of various coefficients pertaining to heat transfer becomes imperative. These include the solid–gas heat transfer coefficient, the radiation absorption coefficient, and the bed–wall heat transfer coefficient. The inclusion of these coefficients is essential in order to encompass the entirety of the potential transfer mechanisms that occur within the gasifier [163].

(a)

Solid–gas heat transfer coefficient.

The solid–gas heat transfer coefficient (h_sg) is a critical component in the calculation of the volumetric rate of energy transfer between the solid and gaseous phases (Q_sg). The estimation of the solid–gas heat transfer coefficient (h_sg) is typically made under the assumption of non–reactive conditions. However, it is important to note that this may result in significant discrepancies between the theoretical and experimental values by which an adjustment constant ($\mathsf{\zeta }$) is introduced, which takes values between 0.02 and 1.0, $\mathsf{\upsilon }$_p is the particle density number in (m⁻¹), as shown below [163]:

$${\mathrm{Q}}_{\mathrm{s}\mathrm{g}}=\mathsf{\zeta }\times {\mathrm{h}}_{\mathrm{s}\mathrm{g}}\times {\mathsf{\upsilon }}_{\mathrm{p}}\times \left({\mathrm{T}}_{\mathrm{s}}-{\mathrm{T}}_{\mathrm{g}}\right)$$

(9)

(b)

Radiation absorption coefficient.

Energy transfer by radiation in the combustion of solid fuels in fixed beds is attenuated by absorption. In the absence of dispersion effects, the absorption coefficient (k_a) is contingent on the bed void fraction (ε) and the diameter of the particles (d_p), as reported elsewhere [164]. The (k_ka) adjustment constant varied between 0.5 and 2.0 [163] according to the following equation:

$${\mathrm{k}}_{\mathrm{a}}=-{\mathrm{k}}_{\mathrm{k}\mathrm{a}}\times \left({\displaystyle \frac{1}{{\mathrm{d}}_{\mathrm{p}}}}\times \ln \mathsf{\epsilon }\right)$$

(10)

(c)

Bed–wall heat transfer coefficient.

The following equations facilitate the calculation of convective heat losses from the solid and gaseous phases through the walls during the combustion/gasification process.

$${\mathrm{Q}}_{\mathrm{s}\mathrm{w}}={\displaystyle \frac{4\times {\mathrm{h}}_{\mathrm{s}\mathrm{w}}}{{\mathrm{d}}_{\mathrm{t}}}}\times \left({\mathrm{T}}_{\mathrm{s}}-{\mathrm{T}}_{\mathrm{w}}\right)$$

(11)

$${\mathrm{Q}}_{\mathrm{g}\mathrm{w}}={\displaystyle \frac{4\times {\mathrm{h}}_{\mathrm{g}\mathrm{w}}}{{\mathrm{d}}_{\mathrm{t}}}}\times \left({\mathrm{T}}_{\mathrm{g}}-{\mathrm{T}}_{\mathrm{w}}\right)$$

(12)

where (d_t) is the gasifier diameter in (m); (T_s), (T_w), and (T_g) is the temperature of solids, wall, and gases in (K), (h_gw) and (h_sw) are the gas–wall and solid–wall heat transfer coefficient; (Q_sg), (Q_sw), and (Q_gw) is the solid–gas, solid–wall, and gas–wall heat transfer in (W m⁻³). As the oxidation of the gaseous fuel progresses, there is an increase in the heat transfer from the gas to the solid phase, which is accompanied by an increase in the heat transfer coefficient. The heat transfer coefficient profiles are depicted by Pérez et al. [163].

7. Uses and Applications of the Producer Gas

Gas upgrading is a crucial stage that depends on the final use of the product. Inert species, such as CO₂, methane, and longer hydrocarbons, must be removed as they increase volume and energy demand. It is important to note that synthesis reactions may require high pressure, necessitating compression of the producer gas. A further parameter that affects the quality of syngas is the superficial velocity (SV), which is defined as the apparent velocity of the gas (air or steam) passing through the reactor under the assumption that the space is empty. This is a crucial consideration to ensure accurate residence time and effective heat transfer through the gasifier. In a downdraft gasifier, the gas enters the combustion zone. At this stage, the contact between the gas and the solid is significant due to the substantial reduction in particle size that occurs as the combustion progresses. As the particle undergoes consumption by partial combustion, it transitions through the reduction zone.

A reduction of the SV value results in a slower pyrolysis process, which in turn produces a greater quantity of char and a considerable number of unburned tars. A low superficial velocity has been shown to result in relatively slow pyrolysis conditions, with an approximate temperature of 700 °C. This has been demonstrated to yield high levels of charcoal (20–30%), in addition to substantial quantities of unburned tars and a gas comprising hydrocarbons and volatile fuels. It has been established that a high SV results in accelerated pyrolysis, leading to the production of char–ash that constitutes less than 10% at 1050 °C. Concurrently, the flame pyrolysis zone is characterized by the generation of hot gases at temperatures ranging from 1200 to 1400 °C. Subsequently, these gases react with the residual char–ash, yielding tars with concentrations typically below 1000 ppm, char-ash levels of 5–7%, and a producer gas with reduced energy content [165]. Although high values of SV promote the formation of low–density char, such high SV values result in a reduction in residence time within the gasifier, which in turn diminishes the efficiency of tar cracking processes. An increase in surface velocity with airflow makes the velocity independent of gasifier size, allowing comparison of gasifiers of different dimensions [20].

A study was conducted on the changes in the amount and composition of tar according to superficial velocity in a downdraft biomass gasifier [166]. Research indicated that SV variations (0.3 to 0.7 m s⁻¹) have a significant impact on tar content. The gravimetric analysis of tar indicates that the lowest tar content is recorded at 0.4 (m s⁻¹). The presence of one–ring aromatics and naphthalene was mainly detected by gas chromatography in tar.

Producer gas possesses valuable properties that can be utilized for various purposes, including fuel synthesis, which is one of its most significant applications. The crucial processes involved in fuel synthesis are synthetic natural gas, hydrogen, Fischer–Tropsch, and alcohols [167]. However, methane is the least desirable product for the Fischer–Tropsch synthesis, and its amount must be reduced. The product selectivity is found to be strongly related to the process conditions. It has been demonstrated that an increase in temperature results in a shift in the distribution towards products with lower carbon numbers on iron, ruthenium, and cobalt. An increase in total pressure typically results in a shift of product selectivity towards heavier products. It has been observed that an increase in H₂/CO ratios results in a greater yield of light hydrocarbons and a reduced yield of olefins. Subsequently, a number of strategies have been posited to reduce methane selectivity, such as: (1) Increasing the pressure at low temperatures and (2) Achieving a relatively high conversion rate, but at a level that is low enough to limit the extent of the water–gas shift reaction [168].

The production of synthetic natural gas (SNG) requires a molar ratio of 3:1 of hydrogen to carbon monoxide, which is achieved through the water–gas shift reactor prior to methanation. Typically, Ni–based catalysts are employed for this reaction:

$$\mathrm{C}\mathrm{O}+3{\mathrm{H}}_2\to {\mathrm{C}\mathrm{H}}_4+{\mathrm{H}}_2\mathrm{O}$$

(13)

The presence of methane in the producer gas is advantageous because it is not converted during methanation, thus increasing the amount of methane.

Furthermore, the costs of hydrogen production by biomass gasification in very large scale may be competitive with natural gas reforming by biomass gasification [167]. The steam reforming and water–gas shift reactions are utilized for this purpose. A dual fluidized bed steam gasification process can yield 55–70 vol% of hydrogen on a dry basis [169]. However, there is a lack of infrastructure to produce hydrogen at high scale.

The Fischer–Tropsch (FT) synthesis is a gas–to–liquid technology used to produce fuels and chemicals. The process involves the following reactions:

$$\left(2\mathrm{n}+1\right){\mathrm{H}}_2+\mathrm{n}\mathrm{C}\mathrm{O}\to {\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{\left(2\mathrm{n}+2\right)}+\mathrm{n}{\mathrm{H}}_2\mathrm{O}$$

(14)

$$2\mathrm{n}{\mathrm{H}}_2+\mathrm{n}\mathrm{C}\mathrm{O}\to {\mathrm{C}}_{\mathrm{n}}{\mathrm{H}}_{2\mathrm{n}}+\mathrm{n}{\mathrm{H}}_2\mathrm{O}$$

(15)

Furthermore, the FT synthesis can be conducted at low temperatures (200–260 °C), using Co– or Fe–based catalysts to produce hydrocarbons with high boiling points. Conversely, at high temperatures (300–350 °C), Fe–based catalysts promote the formation of methane, olefins, and aromatics. Diesel can be obtained through this process with no sulfur content and a cetane number higher than 70 [167].

Methanol can be produced from synthesis gas through hydrogenation using oxide–based catalysts, such as copper or zinc. The reactions involved are as follows:

$$2{\mathrm{H}}_2+\mathrm{C}\mathrm{O}\leftrightarrow {\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}$$

(16)

$$3{\mathrm{H}}_2+{\mathrm{C}\mathrm{O}}_2\leftrightarrow {\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}+{\mathrm{H}}_2\mathrm{O}$$

(17)

The reaction is promoted by a low amount of CO₂, which also helps to maintain a constant level of activity. However, due to the reversibility of both reactions, the equilibrium is favored at low temperatures and high pressures. The stoichiometry for both reactions is achieved when the following relationship (r) is higher than 2.03, as shown below:

$$\mathrm{r}={\displaystyle \frac{{\mathrm{H}}_2-{\mathrm{C}\mathrm{O}}_2}{\mathrm{C}\mathrm{O}+{\mathrm{C}\mathrm{O}}_2}}$$

(18)

Methanol is a valuable fuel source as it can be transformed into gasoline or diesel through processes such as the methanol–to–diesel (MtD) process, where methanol is first converted to propylene and then oligomerized. The resulting product is distilled and hydrogenated to produce kerosene and diesel with traces of naphtha. The sulfur content in all cuts is negligible, and the diesel has a cetane number of at least 52 [167]. The most prevalent catalyst employed is ZSM-5 zeolite. The near-zero sulfur/polyaromatics diesel fuel resulting from this process differs from conventional FT diesel only in cetane number; the FT diesel has a cetane number of >70. The gasoline stream formed during cracking is characterized by a near–zero sulfur content, in addition to commercial octane ratings (92 RON, 80 MON) and a maximum aromatics content of 11% [167].

Methanol can be converted to olefins by a process called methanol–to–olefins (MtO) synthesis, which uses zeolite–based catalysts to produce mainly propylene and ethylene [167]. The conversion of methanol is essentially complete and stoichiometric, and the product selectivity is 56% (w/w) towards hydrocarbons and 44% to H₂O. The overall reaction is described as:

$${\mathrm{C}\mathrm{H}}_3\mathrm{O}\mathrm{H}\to \left[{\mathrm{C}\mathrm{H}}_2\right]+{\mathrm{H}}_2\mathrm{O}$$

(19)

It is noteworthy that [CH₂] represents the mean oligomeric branch of an olefin hydrocarbon. The reaction primarily occurred over a ZSM-5 catalyst in a fixed bed configuration; however, olefins in the C₂–C₃ range are selectively obtained by using a SAPO–34 catalyst, which has milder acidity and unique pore size and geometry compared to ZSM-5. The milder acidity of SAPO–34 has been demonstrated to reduce the extent of hydrogen transfer reactions. This, in turn, minimizes the yield of paraffinic products and increases the yield of olefin products slate. However, due to the occurrence of pore diffusional gradients, the SAPO–34 catalysts are also susceptible to severe deactivation. A variety of industrial processes are available across different scales (pilot, demonstration, or commercial). These include the UOP/hydro–advanced MTO process by UOP, which uses SAPO–34 as a catalyst to perform a near 100% conversion, achieving 80% selectivity to C₂–C₃ olefins. Honeywell UOP provides other MtO technology, utilizing SAPO–34 as the catalyst and achieving conversions of approximately 100%, with 85% selectivity towards C₂–C₃ olefins [170].

Methanol dehydration produces dimethyl ether when the H₂:CO ratio is near 1, which is commonly obtained when oxygen is used as a gasifying agent. Dimethyl ether has the potential to be used as a fuel in Otto engines and can be transported using the liquified petroleum gas (LPG) infrastructure due to its similar physical properties compared to LPG [167].

When produced in an appropriate ratio and under adequate pressure, syngas can be transformed into valuable chemical products such as alcohols, synthetic natural gas, and fuels. Currently, the most common technologies employed in this field are based on Fischer–Tropsch and methanation reactions. In a comprehensive report carried out by Ekbom et al. [171] based on a technological revision, the authors presented case studies of processes and their Technology Readiness Level (TRL). The technologies mainly explore gasification followed by other processes such as FT and methanation to obtain transportation fuels and/or chemical products.

The generation of electrical energy from gasified biomass has been extensively researched and developed, with notable advancements in gasification technology, particularly in adapting it to applications involving biomass, with the dual objective of curbing greenhouse gas emissions and providing electricity. This approach could prove beneficial if accompanied by sufficient financial incentives, subsidies, and infrastructure development. The optimal location and supply area for an electric generation plant must be considered when determining the viability of biomass–fueled electricity generation. This includes the local distribution of biomass resources, transportation costs, and the availability of electric lines [172].

8. Economic Feasibility of Biomass Gasification

In the context of small-scale electricity generation, fixed bed downdraft gasifiers are often regarded as a more favorable option due to their tendency to produce lower tar levels in the producer gas [173]. The fixed bed gasifier appears to be the most viable option for the production of a low calorific value gas for utilization in small–scale power generation schemes, for example, in gas turbine and diesel engines. The gasifier plant is notable for its simplicity of construction, its robustness, and its minimal or non-existent requirement for moving parts [174]. However, the economic feasibility is contingent on numerous factors, primarily the capital expenditures associated with the equipment (i.e., gasifier, engine-generator set, civil works, and local distribution network), the specific fuel consumption, the capacity utilization factor, the apparatuses useful lifespan, and the prevailing fuel prices. In order to assess the economic feasibility, several indicators must be taken into consideration, such as the levelized cost of electricity (LCOE) [175].

The capital costs may encompass a number of items. The specific costs will vary depending on the gasification reactor and its capacity, the biomass type, the gasifying agent, and syngas cleaning. The following items may be included: consultancy/design, civil works, fuel handling/preparation, electrical requirements, and the gasifier. In addition, the annual cost of operating the plant can be calculated by aggregating the costs of the primary fuel, plant operation and maintenance (O&M), and taxes. The O&M costs can be subdivided into personnel, power consumption, maintenance materials, consumables, waste disposal, and by-products management costs. The financial burden of electrical energy for a gasification facility can be calculated by including the feeding, reactor, and cleaning systems, the water treatment sections, auxiliary devices, the cost of electrical energy, and total yearly electric energy consumptions. The personnel expenditures can be categorized into three distinct types: annual operating, administrative and support labor. In some states or countries, revenues may apply. The primary income of the facility is derived from the sale of electrical energy generated by the combined heat and power (CHP) system. It is also necessary to make financial assumptions to include the plant operating life in years, the amortization period established, the bank loan and interest, and inflation rate, among other parameters [176].

The capital expenditure (CapEx) and operational expenditure (OpEx) for a fixed bed gasifier are contingent on the gasifying agent selected, which in turn impacts the gasifier construction, design and operation. The following sequence of costs is observed as a function of the gasifying agent: oxygen > steam > air. Furthermore, it is necessary for the syngas cleaning stages to encompass the financial implications of cyclones, filters, scrubbers, and adsorbents. In the context of tar cracking, it is mandatory to incorporate catalytic and thermal processes [2].

The utilization of gasification technology confers considerable environmental benefits, attributable to its capacity to generate lower levels of emissions when compared with direct combustion or landfill. Moreover, gasification prevents soil contamination due to leachate compared to conventional landfill. When processing municipal solid waste (MSW) by gasification, 31 g of NO_x and 9 g of SO₂ is formed per ton of waste converted, while landfill releases 68 g of NO_x and 53 g of SO₂ per ton. The direct combustion emits more than 192 g and more than 94 g, respectively. Furthermore, in terms of carbon dioxide emissions, MSW gasification generates approximately 1 kg of CO₂-eq/kWh of generated power, whereas landfill produces approximately 2.75 kg of CO₂-eq/kWh and direct combustion produces approximately 1.6 kg of CO₂-eq/kWh. Moreover, MSW gasification has the potential to reduce the volume of landfill by over 88%, which is comparable to the efficacy of direct combustion in reducing the original volume of solid waste by 90% [177].

Technological Maturity and Economical Parameters for Biomass Gasification in Downdraft Gasifiers

Renewable energy sources are gaining recognition as increasingly clean, reliable, and efficient options for decentralized electrification, with the inclusion of a wide range of small–scale, off-grid solutions that will become available [31]. Regarding gasification, the large–scale gasification processes are currently encountering difficulties in achieving market availability. This is primarily due to the substantial financial investment required and the uneconomic nature of their commercial operations, which are related to biomass availability and heterogeneity. As considerable amounts of homogeneous feedstock are required to support the operations in these plants, the economic viability of such investments is questionable. Furthermore, the financial burden associated with the transportation of feedstocks from distant locations to gasification facilities with high capacity could not be economically feasible. The substantial amounts of feedstock that are required in large–scale gasification plants can result in the displacement of food crops to unused lands. This, in turn, can lead to the conversion of forest areas to arable lands, which has a detrimental effect on biodiversity and greenhouse gas emissions. Certain solutions are predicated upon the exclusive utilization of waste as a feedstock (such as forestry, agricultural, and municipal solid wastes) or the co–gasification of different waste types [2].

Conversely, fixed bed gasifiers are regarded as the optimal solution for small– to medium–scale applications, offering enhanced efficiency. The application of exergy analysis to gasification has been documented in the extant literature. This analysis is defined as a quantitative measure of the maximum useful work that can be obtained from a given energy or material flow. The methodology employed has been demonstrated to be effective in addressing the limitations of conventional energy analysis by measuring the quality and quantity of energy flows concurrently. The primary function of this system is to facilitate the identification of thermodynamic inefficiencies with respect to their location, quantity, and origin. In consideration of the aforementioned concept, experimental evidence has demonstrated that the downdraft gasifier exhibits the highest exergy efficiency among biomass gasification systems. This observation is attributed to the optimal interaction between the biomass and the gasifying medium during the combustion stage, resulting in the generation of high–quality syngas with minimal tar content [178].

It is vital to consider the financial implications of the energy required for the project. Consequently, the LCOE is a useful metric that is defined as the price of electricity required for a project, in circumstances where revenues are expected to equal costs. This includes the return on capital invested, which is equivalent to the discount rate. It is necessary to acknowledge that a price of electricity that exceeds this threshold would result in a higher return on capital, whereas a price that falls short of this level would yield a lower return on capital or potentially even a loss. The value of the LCOE for renewable energy technologies is subject to variation due to a number of factors. These include the technology itself, geographical location, specific project characteristics, cost of the renewable energy resource, capital outlay, and operational expenses. It is also influenced by the efficiency and performance of the technology [3].

The formula employed for calculating the LCOE is:

$$LCOE={\displaystyle \frac{{\sum }_{t=1}^n\frac{I_t+M_t+F_t}{{\left(1+r\right)}^t}}{{\sum }_{t=1}^n\frac{E_t}{{\left(1+r\right)}^t}}}$$

(20)

where LCOE is the average lifetime levelized cost of electricity generation, I_t is the investment expenditures in the year “t”, M_t is the operation and maintenance expenditures in the year “t”, F_t is the fuel expenditures in the year “t”, r is the discount rate, and n is the life of the system. The investment expenditures consist of the equipment, fuel handling, and preparation machinery. The scope of the project may also encompass grid connection, roads, and any new infrastructure or improvements to existing infrastructure deemed necessary for the implementation. The aforementioned components vary significantly between projects. The O&M costs encompass a variety of expenses, including labor, scheduled maintenance, and routine component and equipment replacement.

A fundamental component of a gasification system pertains to the feedstock, given that its price and availability can be subject to variation over time. In instances where low-cost agricultural or forestry residues and waste are available, biomass can often compete with conventional power sources. This renders biomass an optimal solution for the provision of electricity in off–grid and mini-grid contexts. Research has demonstrated that feedstock costs can account for between 40% and 50% of the total cost of electricity produced. It is an established fact that the most economical feedstock is frequently agricultural residues, such as straw and bagasse from sugar cane, as these can be collected at harvest [179]. In the case of forestry residues, the financial burden is predominantly ascribed to the costs associated with collection and transportation. The low energy density of biomass feedstocks imposes limitations on the transport distance from a biomass power plant that is economically viable for feedstock transport, resulting in biomass struggling to capitalize on economies of scale due to the unavailability of large quantities of low–cost feedstock. In the context of non–traded biomass, the financial outlay typically encompasses expenses associated with transport, handling, and storage. The financial burden of these expenditures is attributable to the transportation of biomass waste or residue to the power plant. The issue with low–cost feedstocks in the context of agricultural production is that, in the case of an independent power producer, the quantity of bagasse available is contingent on the dynamics of the ethanol and sugar markets. This complicates the negotiation of long–term contracts, which are designed to mitigate price risk and ensure a secure supply of feedstock, thus facilitating access to financing. This is one of the reasons why many biomass power projects, particularly for CHP, are promoted by the industry involved with the process that produces the wastes and residues [3].

For example, in India, the price of bagasse is approximately 12 to 14 USD/tonne, the price of rice husks is around 22 USD/tonne, and the small–scale gasifier systems have proven to be effective for off–grid, mini–grid, and grid–connected applications. These systems had been installed in industry with a capacity of up to 28 MW, and in rural areas with a capacity of up to 80 MW [3].

Table 7. Biomass feedstock costs (USD/tonne) in Europe, Brazil, and India, including the production, transport, and total costs.

Feedstock	Production Cost	Transport Cost	Total Costs
Wood chips from local energy crops (Europe)	60–94 (88–139)		60–94 (88–139)
Wood chips (Brazil)			71 (106)
Wood chips from forest residues (Scandinavian) transported to continental Europe	64–77 (94–113)	34–38 (50–56)	98–115 (144–170)
Local agricultural residues (Europe)	55–68 (81–100)		55–68 (81–100)
Pellets transported from USA to Europe	100–119 (147–175)	56–63 (83–93)	157–182 (231–268)
Bagasse	Brazil: 11–13 (16–19) India: 12–14 (17–20)		Brazil: 11–13 (16–19) India: 12–14 (14–20)
Charcoal mill (Brazil)	95 (142)		95 (142)
Rice husk (India)	22–30 (31–43)		22–30 (31–43)

The values in parentheses correspond to updated value in 2025.

shows the price of biomass in different regions.

CHP is a process in which electricity and heat are produced from a single energy source. This process is also referred to as cogeneration. The viability of biomass CHP plants is typically determined by the price of electricity and the availability and cost of biomass feedstock. Despite the existence of numerous biomass sources suitable for cogeneration, the most significant potential is observed in the sugar cane and wood processing industries as the raw materials required for the process are readily available at a relatively low cost, and the process heat requirements can be met on site.

Subsequent to the conversion of the feedstock into syngas, it undergoes a series of downstream processing stages, culminating in its utilization within a cogeneration thermal power plant. Gas cooling facilitates the extraction of heat for the purpose of heating. Syngas, having undergone a process of purification and cooling, is subsequently converted within the framework of a combined thermal power plant. The synthesis gas is combusted in the engine, thereby driving a generator that in turn produces electricity. The electricity that is thus produced can be consumed by the plant itself or fed into the public grid in return for payment. The resultant heat is decoupled, rendering it suitable for utilization in heating applications.

In view of this characteristic, and in the context of gasification coupled with CHP generation, a search was conducted, resulting in the retrieval of the commercial scale processes (TRL 9) with the following search parameters: type (a) TRL 9 Commercial, and status (b) operational [180].

Table 8. Downdraft gasification coupled to cogeneration (CHP) in operational processes with TRL 9.

Owner	Location	Startup	Feedstock	Output	Contact
Azienda Agricola San Vittore	Vigevano, Italy		Wood chips	0.5 MWel 0.4 MWth
Azienda Tenca dei Fratelli Zanotti/AB energy	Orzinuovi, Italy	2009	Forestry waste	0.3 MWel	Available upon request
Ciamber	Forno di Zoldo, Italy		Lignocellulosics	1.0 MWel 0.8 MWth
Comune Quingentole	Quingentole, Italy	2006	Wood chips	0.07 MWel 0.14 MWth	www.comune.quingentole.mn.it (accessed on 29 May 2025)
Duchi Fratelli Societa Agricola/Agroenergia	Gadesco Pieve Delmona, Italy	2010	Wood chips	0.96 MWel 3.2 MWth
Glock Energie GmbH	Griffen, Austria	2019	Wood chips	0.30 MWel 0.66 MWth	Available upon request
GRESCO Power Solution GmbH	Bad Wildungen, Germany	2014	Wood chips	0.3 MWel 0.5 MWth	Available upon request
H.H. Kaeser GmbH	Gasel, Switzerland	2015	Wood chips	0.14 MWel 0.24 MWth Investment including CHP gasifier unit, connection to heating device and power	Available upon request
Hotel Haffhus	Ueckermuende, Germany	2018	Wood chips (ISO 17225-4 A1 P16S-P31S)	0.018 MWel 0.044 MWth	www.glock-oeko.com (accessed on 29 May 2025)
HS Energieanlagen GmbH	Neufahrn bei Freising, Germany		Waste wood Clean wood	0.11 MWel 0.25 MWth
Josef Bucher AG Escholzmatt	Escholzmat, Switzerland	2015	Wood chips	0.13 MWel 0.26 MWth	Available upon request
Ligento Nuernberg	Nürnberg, Germany		Wood chips	0.14 MWel 0.24 MWth	Available upon request
Nurmes	Nurmes, Finland	2012	Wood chips	0.04 MWel 0.10 MWth	https://www.efarm.fi/kohteet/e-farm-kuittilan-tila-nurmes/ (accessed on 29 May 2025)
Qalovis Altenberge	Altenberge, Germany	2012	Wood pellets	0.036 MWel 0.12 MWth	Available upon request
Spanner Bamberg	Landkreis Bamberg, Germany	2011	Wood pellets Wood chips	0.045 MWel 0.120 MWth
Spanner Landshut	Landkreis Landshut, Germany	2011	Wood chips	0.025 MWel 0.500 MWth	Available upon request
Special Purpose Vehicule (MGGE)	Mont-Godinne, Belgium	2018	Clean wood chips Recycled wood	0.75 MWel 1.20 MWth	https://xylowatt.com/ (accessed on 29 May 2025)
Steiner A. & Cie AG	Ettiswill, Switzerland	2013	Wood chips	0.045 MWel 0.105 MWth	Available upon request
Wegscheid Aschaffenburg	Landkreis Aschaffenburg, Germany	2011	Wood pellets Wood chips	0.12 MWel 0.23 MWth	Available upon request
Wegscheid Bamberg	Bamberg, Germany	2011	Wood pellets Wood chips	0.12 MWel 0.23 MWth	Available upon request
Wegscheid Bayreuth	Bayreuth, Germany		Wood pellets Wood chips	0.125 MWel	Available upon request
Wegscheid Passau	Landkreis Passau, Germany	2009	Wood pellets Wood chips	0.12 MWel 0.23 MWth	Available upon request
WUN Bioenergy	Schönbrunn, Germany	2012	Wood pellets Wood chips	0.36 MWel 0.54 MWth	Available upon request

MWel is a term that refers to electric power; MWth is a term that refers to thermal power.

presents the results of the study. In addition, the following projects with high TRL values (planned and operational), were found and reported in

Table 9. Other processes with operational or planed statuses based using downdraft gasification.

Owner	Locality	Status	Input	Output	Use
Charwood Energy + consortium	Cognac, France	Planed (TRL 9)	Lignocellulosics	Clean syngas	Direct use in furnace
Wegscheid Demo	Wegscheid, Germany	Operational (TRL 6-7)	Wood pellets Wood chips	0.125 MWel 0.23 MWth	CHP

.

It has been established that the majority of feedstock is constituted of wood (pellets or chips), with a notable proportion also comprising forestry and lignocellulosic residues. This distribution is influenced by factors such as the availability of resources and the geographical location of the gasification plant. The processes under operation, based on downdraft gasification coupled to CHP generation, are located in Europe and produce both thermal and electrical energy on a small and medium scale. The commencement of these processes occurred in various temporal periods, with the earliest beginning in early 2006 and the most recent initiated in 2019. According to the search parameters, all the projects are in operation and produce electricity in the range of 0.018 to 1 MWel, while thermal energy ranges from 0.044 to 3.2 MWth.

A thorough examination of the existing literature on the operation and capital expenditures, in addition to the net present value and syngas production costs, was undertaken to analyze prevailing trends in these parameters over time. The search was focused on small and medium–sized downdraft gasifiers using different technologies (heat, electricity, combined heat, and power) to ensure a broad cost range. A comprehensive analysis of reports pertaining to various waste streams, including agricultural, municipal solid, animal manure, poultry litter, and waste tyres/biomass co-gasification, was conducted. It is important to note that not all reports included the CapEx, OpEx, net present value (NPV), and syngas production cost; however, all available information regarding these parameters was considered in this study. In order to facilitate the comparison of all values, it was necessary to update the costs to 2025 and report them in USD by consulting the historical prices [181].

In the context of economic feasibility disclosures, it is necessary to consider the net present value, the internal rate of return, and the payback period as important concepts. NPV is a metric employed to ascertain the viability of a project. It is a calculation that determines the present value of the future cash flows of a project, discounted at an appropriate rate, by comparing them to the initial investment. The internal rate of return (IRR) is a metric employed to evaluate the financial viability of a project by determining the discount rate at which the NPV of the project becomes zero. The payback period (PBP) is defined as the necessary time to recover the initial investment, including the project cash flow. In accordance with established financial benchmarks for biomass projects, it is considered acceptable for the NPV to be a positive value, the IRR to exceed 10%, and the PBP to be less than 10 years [31].

A comprehensive literature search was conducted to compile the CapEx and OpEx, and the syngas production cost, where available, was also reported. The study examined the process of downdraft gasification for small and medium–scale applications, as illustrated in

Table 10. Economic variables referred to small and medium–scale downdraft gasifiers using biomass as feedstock.

Year	CapEx, USD/Year	OpEx, USD/Year	Syngas Production Cost a, USD/Nm3	Lifetime, Years	Main Application or Product	Reference
2025	7035.86	52,254.06	0.13	10	Syngas	[182]
2024	14,185.14			10	ICE	[183]
2022	1396.30	3401.00	0.35	10	Syngas	[184]
2022	21,214.16			15	CHP	[185]
2022		21,121.00			ICE	[186]
2020	6975.13	158,305.00		20	ICE	[177]
2017	21,317.81			20	CHP	[187]
2012	72,485.00	187,704.03		20	ICE	[188]
2012	47,554.30	67,845.44			ICE	[189]
2009			0.82	20	Syngas	[190]
2008		178,212.00	1.82	20	Syngas	[191]

^a On dry basis.

. In order to circumvent the issue of a broad range of CapEx values, this variable was obtained by dividing the total capital expenditures by the lifetime of the project. It is evident from the literature that this variable has been documented in this manner in a number of reports. In addition, the NPV, PBP, and IRR are also summarized in

Table 11. Net present value applied to downdraft gasification using biomass as feedstock.

Year	NPV, USD	PBP, Years	IRR, %	Reference
2025	21,861.71	4.1	17.85	[182]
2025	28,399.35	12.09	14.00	[192]
2024	76,329.34	5.14	24.07	[183]
2022	53,586.30	6.0	9.72	[193]
2020	104,884.28	7.7	10.90	[177]
2020	49,123.80 a	9.2	19.34	[194]
2020	121,032.49	6.91	16.63	[195]
2015	126,427.21 b	6.01	18.15	[196]
2015	60,421.01	3.0	23.00	[197]
2009	130,794.37	2.92		[198]

^a Using acacia as feedstock. ^b Values obtained with the best IRR and PBP from several case studies reported by authors.

.

As demonstrated in Table 10, the projected lifetime ranges from 10 to 20 years. In the case of CapEx and OpEx, linear regressions were utilized to model the data, thereby establishing the relationship between CapEx and the time variable (year). As illustrated in Figure 11a, a linear relationship describes the data set. The determination coefficient (R²) demonstrated a value of 0.74, indicating that this result is attributable to the presence of relatively higher and lower costs, which can be ascribed to the dimensions of the equipment, the auxiliary devices, or, in certain instances, the associated cost of connecting to the electric grid [188].

In a similar manner, a correlation was observed between OpEx and time; however, it should be noted that only seven projects presented data on operational costs. In this instance, the determination coefficient was found to be low (0.46), which is due to the distinct assumptions made by the authors (Figure 11b). For instance, the cost of labor, the transportation of biomass, and the maintenance schedule are all factors that have the potential to influence the determination of this economic parameter. In the regression analysis, all the reported operating expenses (OpEx) were taken into consideration in order to ascertain the impact of incorporating differing costs. It is evident that this variable is acutely responsive to transportation, cost, and the type of biomass, in addition to the personnel allocated to the operation of the gasification plant (i.e., one or more persons or operators).

Despite the lack of data regarding syngas production cost, these values were also correlated with a determination coefficient of 0.74 (Figure 11c). The financial implications of syngas production are contingent on various factors, including pretreatment methodologies and the cost of the feedstock. Electricity consumption and processing capacity also play a significant role, with disparities potentially arising between laboratory-scale experiments and large-scale processing units.

In addition to the utilization of syngas for the generation of thermal and electric energy, its sale represents a further potential application. Thus, the financial viability of the system is primarily determined by the cost of biomass and the selling price of producer gas. The potential profitability of the system is indicated by changes in the selling price of syngas gas, while changes in biomass costs can demonstrate the financial benefits to the owner. It is anticipated that demand for syngas will increase in the future, owing to the fact that this kind of energy is derived from a green energy source. This increase in demand will result in the need to raise manufacturing capacity and enable a high marginal value [182]. The price of syngas is a salient factor, given that higher values correspond to reduced payback periods. However, an increase in the selling price could compromise competitiveness in the market, thus necessitating the establishment of an equilibrated selling price.

As Figure 11d illustrates, the NPV trend is contingent on the time variable, exhibiting a determination coefficient of 0.74. This coefficient is influenced by the projected lifetime, the discount rate, the cash flow, and the initial investment. As Table 11 shows, the majority of projects have a payback period of less than 10 years, while the internal rate of return is generally higher than 10%. Given the positive nature of the NPV in all cases, it can be concluded that all projects are potentially cost–effective.

A decline in all economic variables was evident over time. In certain instances, the R² value was elevated due to diminished dispersion in the data, consequent to the feedstock type, processing capacity, investment and operational costs, amongst other parameters. The principal product of gasification was identified as syngas, electricity produced by combustion engines (ICE), and combined heat and power, which also influences the CapEx and OpEx depending on the type of technology coupled to the gasifier. The production of electricity and the combined heat and power applications of biomass gasification had the highest CapEx, which is due to the capacity of the processing. Conversely, the production of syngas and electricity has been observed to incur at the highest OpEx in certain instances, due to the associated costs of labor and personnel allocated to the operation of the plant, as well as the biomass cost, as Table 10 shows. Figure 11d demonstrates a significant dispersion of data, where the OpEx depends on the primary use of the producer gas (syngas production, combined heat and power, electricity).

9. Modeling of the Gasification Reactions and Reactor Focused on Downdraft Gasifiers

A variety of models can be employed to summarize and analyze the reactions occurring on distinct types of gasifiers. The Gibbs free energy minimization principle constitutes the cornerstone of equilibrium modeling, otherwise referred to as zero–dimensional modeling. This modeling technique provides a reliable forecast of the final composition which can monitor process parameters, including pressure and temperature. Kinetic models encompass a multitude of parameters beyond the conventional variables of pressure and temperature. These include the height of the gasifier, the size of the char particles, the density of the gases, the flowrate of the feedstock, and the activation energy and equilibrium constant values. The CFD modeling process incorporates the conservation equations for mass, momentum, and energy, which are coupled to the hydrodynamics of the process. The incorporation of gasification reactions entails the solid phase, encompassing devolatilization and char burning, alongside the gas phase evolution and its properties, such as turbulence and density [6].

9.1. Bibliometric Analysis: Network Visualization for Co-Authorship, Bibliographic Coupling, Network and Overlay Visualization Maps

Following a review of the state of the art and a bibliometric analysis based on VOSviewer software version 1.6.20 [199] (available at https://www.vosviewer.com/), the biomass gasification database was extracted from Scopus using specific keywords (https://www.scopus.com/home.uri) and exported to the VOSviewer software as a CSV file, which was then plotted in Figure 12. The keywords used were “biomass AND gasification AND modeling”, and the results accounted for 2395 papers (information retrieved on 16 May 2025) and different bibliometric analyzes were performed. The network visualization map for co–authorship among countries is presented in Figure 12. As demonstrated, the countries that have produced the highest number of publications in the field of biomass gasification modeling, as determined by the total number of publications in Scopus, are China, the United States, Italy, India, United Kingdom, Germany, Canada, Sweden, Spain, and France (top 10), primarily based on the country of the corresponding author. The choose thresholds were a minimum number of five documents by country. From 105 countries found, only 61 meet the thresholds. The ratio of the citation–to–documents published is showed in

Table 12. Contribution by country and citation–to–document ratio in the biomass downdraft gasification.

Country	Documents	Citations	Ratio Citations/Document
China	332	10,357	31.2
United States	261	7321	28.0
Italy	214	5853	27.4
India	191	7197	37.7
United Kingdom	136	5780	42.5
Germany	122	4144	34.0
Canada	115	4658	40.5
Sweden	115	4149	36.1
Spain	111	4467	40.2
France	100	2874	28.7

, where the leader countries in publishing in this topic are also summarized.

Additionally, the bibliographic coupling among countries is illustrated in Figure 13. The primary clusters identified in this study consist of the following: firstly, a group comprising the United States, Italy, Germany, Sweden, Spain, France, The Netherlands, Japan, Tunisia, Ireland, Denmark, Finland, Greece, Mexico, Norway, Colombia, Hungary, and Argentina (red color); and secondly, a group consisting of China, the United Kingdom, Australia, Malaysia, Saudi Arabia, Pakistan, Nigeria, United Arab Emirates, Qatar, Ethiopia, Croatia, Belgium Czech Republic, Egypt, Indonesia, Kenia, Croatia, Iraq, and Cuba (green color). India is part of another cluster that also includes Iran, Brazil, Portugal and Türkiye, amongst other countries (blue color). It is possible to discern other clusters on the basis of Figure 13 (violet color).

The VOSviewer software was also employed to visually map the occurrence of the major keywords based on the retrieved documents from Scopus. Figure 14 presents the network visualization (top image) and overlay visualization (bottom image) maps of the keyword co–occurrence in the period from 1991 to 2025. The subsequent analysis revealed that, of the 11,950 keywords on the topic retrieved from 2395 documents, 501 keywords satisfied the minimum threshold of five occurrences and were selected for further analysis. Moreover, a screening was conducted to avoid duplicates in keywords, such as the terms ‘biohydrogen and bio–hydrogen’, ‘bio oils and bio–oils’, and so forth. Keywords which were open to interpretation, such as ‘article’ and ‘review’, were also excluded from the analysis. Five clusters have been identified in which the size of the node is contingent on the frequency with which the topic is mentioned. Furthermore, each keyword is linked to another, creating a complex network of interconnections.

In the context of overlay visualization based on keywords, the most recent topics, dating from 2022 onwards, encompass machine learning, decision trees, hydrogen production, artificial neural networks, computational fluid dynamics, support vector machines, biochar, biomethanol, artificial intelligence, decarbonization, response surface methodology, and multi-objective optimization. The utilization of these keywords enables the extrapolation of future trends in research, particularly those related to artificial intelligence and multi–objective optimization. These are nascent topics in the field of biomass gasification in a downdraft gasifier. Furthermore, an increased level of interest in gasification modeling has been observed in the last decade. The focus of this interest has been on computational fluid dynamics (CFD), downdraft gasification, and sensitivity analysis, among other topics.

In the ensuing sections, a thorough exposition of the diverse modeling approaches focused on downdraft gasifiers is presented. This section is devoted to thermodynamic equilibrium models, kinetic models, and CFD, mainly. Furthermore, reactor models, which are not commonly reported in literature reviews, are also depicted.

9.2. Equilibrium Models

Equilibrium models predict the thermodynamic limits that gasification reactions can reach, as well as the composition of the synthesis gas. Two types of equilibrium models exist: stoichiometric models, where gasification chemistry is well–known and equilibrium constants are calculated, and non–stoichiometric models, where Gibbs free energy minimization is required due to unknown gasification chemistry. In both cases, practical limitations are observed because the operating temperature in gasifiers is not high enough to reach thermodynamic equilibrium. Nevertheless, it is acknowledged that equilibrium models are characterized by simplicity when it comes to implementation and that the processes involved in arriving at their solutions can be accomplished with greater expediency, thus rendering them a methodology of choice.

9.2.1. Stoichiometric Models

Stoichiometric models are often adjusted to accurately reflect the concentrations in the synthesis gas. This is achieved by multiplying the equilibrium constants by correction factors or by using computer algorithms to minimize errors in equivalence ratio or temperature [13]. A stoichiometric equilibrium model for a downdraft gasifier was proposed by Gagliano et al. [200] to predict the chemical composition, temperature, and LHV of the producer gas. The authors used two independent equations based on methanation and water–gas shift reactions, along with their respective equilibrium constants, using air at 25 °C at atmospheric pressure. The producer gas is formed by CO, CO₂, H₂, CH₄, H₂O, N₂, and tar, while the ash is considered inert. The model was calibrated by introducing empirical parameters to improve the accuracy of the methane concentration, which is generally underestimated. In addition, a sensitivity analysis was carried out with variations in moisture content between 5 and 35 wt%.

A model was developed to consider the drying, pyrolysis, and gasification reactions, which demonstrated good agreement with the literature data, but overestimated the volume fraction of H₂ and underestimated the volume fraction of CH₄. Additionally, a sensitivity analysis was performed by varying the equivalence ratio, amount of steam, air preheating, degree of oxygen enrichment, and gasification temperature [201].

Azzone et al. [202] developed a stoichiometric equilibrium model that excludes tar. The char is primarily composed of carbon, with only a fraction (α) included in equilibrium while the rest is assumed to bypass the reaction zone. The study conducted a sensitivity analysis on pressure, biomass moisture, oxidant composition, and temperature to determine their effects on the LHV and composition of producer gas. The model was used to assess the reliability of the gasification of rapeseed straw, corn, and sunflower stalks. Huang and Ramaswamy [203] developed another thermodynamic equilibrium model by introducing coefficients that affect the equilibrium constants for methane reforming and water–gas shift reactions involving CH₄ and CO. This modification allowed for better adjustments, and only CH₄ could be fitted. The authors also noted the presence of H₂S in the global reaction.

Sharma [204] proposed an equilibrium model for the reduction zone in a downdraft gasifier. The model incorporates solid (char)–gas and gas–gas reactions, wherein the unconverted char is regarded as a product. The model assumes that the equilibrium is reached by assuming the solid particles to be spherical with a constant diameter and considers the water–gas shift, steam reforming, Boudouard, water–gas, and methanation reactions. To obtain approximations for hydrogen, carbon monoxide, water, and carbon dioxide, the formation ratios of water to carbon dioxide and hydrogen to carbon monoxide were set at 1. The influence of pressure on the calorific values of the producer gas was clearly observed in the range of 1–7 bar. A comparison was made between equilibrium and kinetic modeling of char reduction in a downdraft gasifier. Both methods were used to model the Boudouard, water–gas, methanation, and steam reforming reactions. The energy balance takes into account the effective thermal conductivity of the char bed filled with the gas mixture, the average bed porosity set at 0.4, and the char emissivity set at 0.75. According to the kinetics-based predictions, the length of the char bed increased while the char itself decreased exponentially. Equilibrium and kinetic models predicted that the temperature required for complete conversion of char into gases was 932 K and 950 K, respectively [204]. Hameed et al. [205] reported a kinetic model that includes the Boudouard, water–gas, methanation, steam reforming, and water–gas shift reactions. Equilibrium and rate constants were calculated, and the simulation of kinetic models was carried out with the char reactivity factor set at 100. The authors assumed non–zero initial compositions of carbon monoxide and hydrogen when entering the reduction zone. The simulation of isothermal performance was conducted within the temperature range of 1000–1300 K. Additionally, non-isothermal behavior was studied for heating rates ranging from 25 to 75 (K s⁻¹) to optimize the yield of the producer gas. Ibrahim et al. [206] proposed a model that considers the presence of sulfur in the biomass source. This, in turn, produces H₂S in the syngas as well as ammonia. The char production was modeled through the Boudouard and the ammonia synthesis reactions to predict the ammonia composition in the producer gas. The authors studied the influence of the equivalence ratio and moisture contents on the syngas composition. The model did not require the use of correction factors to predict the methane composition.

Another study [64] tested the use of sewage sludge as feed for gasification, taking into consideration the formation of H₂S in the producer gas due to the presence of sulfur. The study also considered products such as ethylene, acetylene, ethane, and benzene, using a modified stoichiometric equilibrium model. A comparison between the behavior of stoichiometric and kinetic models was reported by Talero and Kansha [207]. The kinetic model was more accurate in predicting the chemical composition and yield of products than the stoichiometric model when gasification was carried out between 600 and 900 °C, and steam–to–biomass mass ratios ranged from 0 to 4.

Table 13. Stoichiometric equilibrium models and modified equilibrium models used in the biomass gasification.

Ref.	Considerations	Main Reactions and Equations
[64]	Model type: stoichiometric equilibrium model for sewage sludge in a downdraft gasifier. Biomass source: Sewage sludge Tar is formed by C2H2, C2H4, C2H6, C6H6. Main results: chemical composition of gas. Yield of producer gas, carbon conversion efficiency, and cold gas efficiency.	CcHhOoNnSs + wH2O(l) + qH2O(g) + m(O2 + 3.76N2) → x1H2 + x2CO + x3CO2 + x4CH4 + x5H2O(g) + x6H2S + x7C2H2 + x8C2H4 + x9C2H6 + x10C6H6 + c(1 − α)C + x11N2 $\mathsf{\alpha }=0.901+0.439\left(1-{\mathrm{e}}^{0.0003\mathrm{T}-\mathrm{E}\mathrm{R}}\right)$ Tar in wt% is given by: $\mathrm{T}\mathrm{a}\mathrm{r}(\%)=35.98{\mathrm{e}}^{-0.00298\mathrm{T}}$ Mass balance equations: x2 + x3 + x4 + 2x7 + 2x8 + 2x9 + 6x10 + c(1 − α) − c = 0 2x1 + 4x4 + 2x5 + 2x6 + 2x7 + 4x8 + 6x9 + 6x10 − h − 2w − 2q = 0 x2 + 2x3 + x5 − o − 2m − w − q = 0 2x11 − n − 2 × 3.76m = 0 x6 − s = 0 x1 + x2 + x3 + x4 + x5 + x6 + xtar + x11 = 1 Equilibrium reactions: (r1) C + CO2 ↔ 2CO (r2) C + H2O ↔ CO + H2 (r3) CO + H2O ↔ CO2 + H2 with equilibrium constant as ${\mathrm{K}}_3={\mathrm{e}}^{\left({\displaystyle \frac{5878}{\mathrm{T}}}+1.86\ln \mathrm{T}-0.27\times {10}^{-3}\mathrm{T}-{\displaystyle \frac{58200}{{\mathrm{T}}^2}}-18\right)}$ (r4) C + 2H2 ↔ CH4 with equilibrium constant as ${\mathrm{K}}_4={\mathrm{e}}^{\left({\displaystyle \frac{7082.842}{\mathrm{T}}}-6.5667\ln \mathrm{T}+3.734\times {10}^{-3}\mathrm{T}-3.612\times {10}^{-7}{\mathrm{T}}^2+{\displaystyle \frac{0.0702\times {10}^{-5}}{{2\mathrm{T}}^2}}+32.541\right)}$ (r5) S + H2 ↔ H2S
[206]	Model type: stoichiometric equilibrium model in the reduction zone of a downdraft gasifier. Biomass source: Several sources, such as rubber wood, wood pellets and chips, rice husk, bamboo, etc. Tar is represented by C6H6.2O0.2 and C. Main results: chemical composition of syngas, tar, and char yields, gasification temperature, cold gas efficiency, and lower heating value.	Global reaction: CHhOoNnSs + wH2O + a(O2 + 3.76N2) → x1H2 + x2CO + x3CO2 + x4CH4 + x5N2 + x6NH3 + x7H2S + x8H2O + x9C6H6.2O0.2 + x10C where: $\mathrm{h}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{H}}\times {\mathrm{M}}_{\mathrm{C}}}{{\mathrm{y}}_{\mathrm{C}}\times {\mathrm{M}}_{\mathrm{H}}}}$, $\mathrm{o}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{O}}\times {\mathrm{M}}_{\mathrm{C}}}{{\mathrm{y}}_{\mathrm{C}}\times {\mathrm{M}}_{\mathrm{O}}}}$, $\mathrm{n}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{N}}\times {\mathrm{M}}_{\mathrm{C}}}{{\mathrm{y}}_{\mathrm{C}}\times {\mathrm{M}}_{\mathrm{N}}}}$, $\mathrm{s}={\displaystyle \frac{{\mathrm{y}}_{\mathrm{S}}\times {\mathrm{M}}_{\mathrm{C}}}{{\mathrm{y}}_{\mathrm{C}}\times {\mathrm{M}}_{\mathrm{S}}}}$, ${\mathrm{M}}_{\mathrm{t}\mathrm{a}\mathrm{r}}=6{\mathrm{M}}_{\mathrm{c}}+6.2{\mathrm{M}}_{\mathrm{H}}+0.2{\mathrm{M}}_{\mathrm{O}}$, with ${\mathrm{x}}_9=0.8212\mathrm{e}\mathrm{x}\mathrm{p}(-3.281\times \mathrm{E}\mathrm{R})\times {\displaystyle \frac{{\mathrm{M}}_{\mathrm{b}\mathrm{i}\mathrm{o}\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{s}}}{{\mathrm{M}}_{\mathrm{t}\mathrm{a}\mathrm{r}}}}$, and $\mathrm{T}\mathrm{a}\mathrm{r}\left(\%\right)=82.12\mathrm{e}\mathrm{x}\mathrm{p}(-3.281\times \mathrm{E}\mathrm{R})$ Mass balance equations: x2 + x3 + x4 + 6x9 + x10 = 1 2x1 + 4x4 + 3x6 + 2x7 + 2x8 + 6.2x9 = h + 2w x2 + 2x3 + x8 + 0.2x9 = o + w + 2a 2x5 + x6 = n + 7.52a X7 = s Equilibrium reactions: (r1) C + 2H2 ↔ CH4 with equilibrium constant as ${\mathrm{K}}_1={\displaystyle \frac{{\mathrm{x}}_4{\mathrm{n}}_{\mathrm{t}}}{{\mathrm{x}}_1^2}}$ (r2) CO + H2O ↔ CO2 + H2 with equilibrium constant as ${\mathrm{K}}_2={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_3}{{\mathrm{x}}_3{\mathrm{x}}_8}}$ (r3) C + CO2 ↔ 2CO with equilibrium constant as ${\mathrm{K}}_3={\displaystyle \frac{{\left({\mathrm{x}}_2\right)}^2}{{\mathrm{x}}_3{\mathrm{n}}_{\mathrm{t}}}}$ (r4) N2 + 3H2 ↔ 2NH3 with equilibrium constant as ${\mathrm{K}}_4={\displaystyle \frac{{\left({\mathrm{x}}_6\right)}^2{{\mathrm{n}}_{\mathrm{t}}}^2}{{\mathrm{x}}_5{\left({\mathrm{x}}_1\right)}^3}}$
[207]	Model type: stoichiometric model in a downdraft gasifier. Biomass source: Japanese cedar considered as CH1.49O0.73. Main results: effects of steam–to–biomass ratios and the temperature.	Reactions: (r1) CH1.49O0.73 → 0.34C + 0.75CH1.79O0.77 (r2) 0.75 CH1.79O0.77 → 0.099CO + 0.077CO2 + 0.08H2 +0.004CH4 + 0.3931CH1.43O0.53 + 0.26H2O (r3) CH1.43O0.53 → 0.526CO + 0.0987CO2 + 0.18CH4 + 0.08H2 + 0.248CH1.43O0.53 Equilibrium reactions: (r4) C + CO2 ↔ 2CO with equilibrium constant $\ln {\mathrm{K}}_{\mathrm{P}}=21.335-{\displaystyle \frac{\mathrm{20,743}}{\mathrm{T}}}$ (r5) C + H2O ↔ CO + H2 with equilibrium constant $\ln {\mathrm{K}}_{\mathrm{P}}=\mathrm{17,156}-{\displaystyle \frac{\mathrm{16,194}}{\mathrm{T}}}$ (r6) CO + H2O ↔ CO2 + H2 with equilibrium constant $\ln {\mathrm{K}}_{\mathrm{P}}=4.33-{\displaystyle \frac{4577.8}{\mathrm{T}}}$
[201]	Model type: zero–dimensional thermodynamic equilibrium model in a downdraft gasification Biomass type: corn cob, pine bark, rubber wood, almond shell, wood pellets and chips, and sawdust. Tar content was limited to 6 g Nm3. Main results: Sensitivity analysis for gasification temperature, equivalence ratio, air preheating, amount of steam, oxygen enrichment degree, etc.	For pyrolysis reactions, the equations for products after drying are: ${\sum }_{\mathrm{j}}\mathrm{Y}={\mathrm{Y}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}+{\mathrm{Y}}_{\mathrm{t}\mathrm{a}\mathrm{r}}+{\mathrm{Y}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ ${\mathrm{Y}}_{\mathrm{g}\mathrm{a}\mathrm{s}}={\mathrm{Y}}_{{\mathrm{C}\mathrm{O}}_2}+{\mathrm{Y}}_{\mathrm{C}\mathrm{O}}+{\mathrm{Y}}_{{\mathrm{C}\mathrm{H}}_4}+{\mathrm{Y}}_{{\mathrm{H}}_2}$ With: ${\mathrm{Y}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}=7.97\times {10}^{-5}{\mathrm{T}}^2-0.125\mathrm{T}+68.87$ ${\mathrm{Y}}_{\mathrm{t}\mathrm{a}\mathrm{r}}=-1.38\times {10}^{-4}{\mathrm{T}}^2+0.12\mathrm{T}+12.64$ ${\mathrm{Y}}_{\mathrm{g}\mathrm{a}\mathrm{s}}=1.12\times {10}^{-4}{\mathrm{T}}^2-0.058\mathrm{T}+30.77$ ${\mathrm{Y}}_{\mathrm{C}\mathrm{O}}=-2.65\times {10}^{-4}{\mathrm{T}}^2+0.27\mathrm{T}-32.71$ ${\mathrm{Y}}_{{\mathrm{C}\mathrm{O}}_2}=-2.85\times {10}^{-5}{\mathrm{T}}^2-0.029\mathrm{T}+70.89$ ${\mathrm{Y}}_{{\mathrm{C}\mathrm{H}}_4}=6.69\times {10}^{-5}{\mathrm{T}}^2-0.037\mathrm{T}+4.28$ ${\mathrm{Y}}_{{\mathrm{H}}_2}=7\times {10}^{-5}{\mathrm{T}}^2-0.0371\mathrm{T}+5.1117$ For gasification reactions, the equations for products after pyrolysis are: ${\sum }_{\mathrm{j}}\mathrm{Y}={\mathrm{Y}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}}+{\mathrm{Y}}_{\mathrm{t}\mathrm{a}\mathrm{r}}+{\mathrm{Y}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ ${\mathrm{Y}}_{\mathrm{g}\mathrm{a}\mathrm{s}}={\mathrm{Y}}_{{\mathrm{C}\mathrm{O}}_2}+{\mathrm{Y}}_{\mathrm{C}\mathrm{O}}+{\mathrm{Y}}_{{\mathrm{C}\mathrm{H}}_4}+{\mathrm{Y}}_{{\mathrm{H}}_2}+{\mathrm{Y}}_{\mathrm{N}{\mathrm{H}}_2}$ Equilibrium constant for the water–gas shift reaction is: ${\mathrm{K}}_1={\mathrm{e}}^{\left[\left({\displaystyle \frac{4276}{\mathrm{T}}}\right)-3.961\right]}$ Equilibrium constant for the methanation reaction is: $\ln {\mathrm{K}}_2={\displaystyle \frac{7082.842}{\mathrm{T}}}-6.567\ln \mathrm{T}+3.73\times {10}^{-3}\mathrm{T}-3.61\times {10}^{-7}{\mathrm{T}}^2+{\displaystyle \frac{0.351}{{\mathrm{T}}^2}}+32.541$ Equilibrium constant for the methane steam reforming reaction is: ${\mathrm{K}}_3=1.198\times {10}^{13}{\mathrm{e}}^{-{\displaystyle \frac{\mathrm{26,830}}{\mathrm{T}}}}$
[200]	Model type: global stoichiometric equilibrium for a downdraft gasifier. Biomass type: rubber wood and pellets Tar is considered as C6H6O0.2 with thermochemical properties as benzene. Char is considered as carbon with thermochemical properties as graphite. Main results: chemical composition of producer gas, lower heating value, and equilibrium temperature.	Global reaction: CHhOoNn + wH2O + m(O2 + 3.76N2) → x1H2 + x2CO + x3CO2 + x4H2O + x5CH4 + x6N2 + xtartar + xcharchar Independent equilibrium reactions: (r1) C + 2H2 ↔ CH4 with equilibrium constant as: ${\mathrm{K}}_1={\displaystyle \frac{{\mathrm{x}}_5 {\mathrm{n}}_{\mathrm{t}}}{{\mathrm{x}}_1^2}}$ and nt is total number of moles (r2) CO + H2O ↔ CO2 + H2 with equilibrium constant as: ${\mathrm{K}}_2={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_3}{{\mathrm{x}}_2{\mathrm{x}}_4}}$ Mass balance equations: x2 + x3 + x5 + 6xtar + xchar = 1 2x1 + 2x4 + 4x5 + 6xtar = h + 2w x2 + 2x3 + x4 + 0.2xtar = o + w + 2m $$\begin{gathered} \mathrm{m}=\left[1+{\displaystyle \frac{\mathrm{h}}{4}}+{\displaystyle \frac{\mathrm{n}}{2}}-{\displaystyle \frac{\mathrm{o}}{2}}\right]\times \mathrm{E}\mathrm{R} \\ \mathrm{E}\mathrm{R}=0.008\times \mathrm{M}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}+0.174 \end{gathered}$$
[205]	Model type: equilibrium and kinetic models under isothermal and non-isothermal behavior for reduction zone in a downdraft gasifier Biomass type: bagasse, wood sawdust, Douglas fir bark, peanut hull, and rice husk Main results: chemical composition of producer gas	Global reaction: CHhOo + wH2O + yO2+ zN2 → x1C + x2H2 + x3CO + x4H2O + x5CO2 + x6CH4 + x7N2 Initial conditions: x7 = z x1 + x3 + x5 + x6 = 1 2x2 + 2x4 + 4x6 = h + 2w x3 + x4 + x5 = o + 2y + w Equilibrium constants calculated through Gibbs function as: ${\mathrm{K}}_{\mathrm{i}}={\mathrm{e}}^{-{\displaystyle \frac{∆{\mathrm{G}}_{\mathrm{i}}^{\mathrm{o}}}{\mathrm{R}\mathrm{T}}}}$ Kinetic model: (r1) C + CO2 ↔ 2CO with ${\mathrm{r}}_1=\mathrm{C}\mathrm{R}\mathrm{F}\left(36.16{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{77.39}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}^2}{{\mathrm{K}}_1}}\right]$ (r2) C + H2O ↔ CO + H2 with ${\mathrm{r}}_2=\mathrm{C}\mathrm{R}\mathrm{F}\left(1.517\times {10}^4{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{121.62}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}{\mathrm{P}}_{{\mathrm{H}}_2}}{{\mathrm{K}}_2}}\right]$ (r3) C + 2H2 ↔ CH4 with ${\mathrm{r}}_3=\mathrm{C}\mathrm{R}\mathrm{F}\left(4.189\times {10}^{-3}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{19.21}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{H}}_2}^2-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}{\mathrm{H}}_4}}{{\mathrm{K}}_3}}\right]$ (r4) CH4 + H2O ↔ CO + 3H2 with ${\mathrm{r}}_4=\mathrm{C}\mathrm{R}\mathrm{F}\left(7.301\times {10}^{-2}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{36.15}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{C}\mathrm{H}}_4}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}{\mathrm{P}}_{{\mathrm{H}}_2}^3}{{\mathrm{K}}_4}}\right]$ (r5) CO + H2O ↔ CO2 + H2 with ${\mathrm{r}}_4=\mathrm{C}\mathrm{R}\mathrm{F}\left(2.824\times {10}^{-2}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{32.84}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{CO}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}{\mathrm{P}}_{{\mathrm{H}}_2}}{{\mathrm{K}}_5}}\right]$ With equilibrium constants as in [50]
[202]	Model type: global stoichiometric equilibrium for a downdraft gasifier. Biomass type: corn and sunflower stalks, rapeseed straw. Char is considered as carbon, and the factor α is the carbon fraction in equilibrium. Main results: chemical composition of producer gas, lower heating value, equilibrium temperature, and cold gas efficiency.	Global reaction: CHhOo + wH2O(l) + qH2O(g) + m(O2 + δN2) → x1H2 + x2CO + x3CO2 + x4H2O + x5CH4 + 3.76δN2 + (1 − α)C(s) (r1) C + 2H2 ↔ CH4 with equilibrium constant as: ${\mathrm{K}}_1={\displaystyle \frac{{\mathrm{x}}_5}{{\mathrm{x}}_1^2}}{\displaystyle \frac{{\mathrm{P}}_{\mathrm{o}}}{\mathrm{P}}}$ (r2) CO + H2O ↔ CO2 + H2 with equilibrium constant as: ${\mathrm{K}}_2={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_3}{{\mathrm{x}}_2{\mathrm{x}}_4}}$ Mass balance equations: x2 + x3 + x5 = α 2x1 + 2x4 + 4x5 = h + 2(w + q) x2 + 2x3 + x4 = o + 2m + w + q $\mathsf{\alpha }=0.32+0.82\left(1-{\mathrm{e}}^{-{\displaystyle \frac{\mathsf{\phi }}{0.229}}}\right)$ $\mathsf{\phi }={\displaystyle \frac{m}{1+\frac{h}{4}-\frac{o}{2}}}$ Energy balance equation: $\sum _{\mathrm{r}}\left[{\mathrm{H}}_{\mathrm{r}}^{\mathrm{o}}+∆{\mathrm{H}}_{\mathrm{T}\mathrm{o}}\right]=\sum _{\mathrm{p}}\left[{\mathrm{H}}_{\mathrm{p}}^{\mathrm{o}}+∆{\mathrm{H}}_{\mathrm{T}}\right]$
[203]	Model type: thermodynamic equilibrium model. Biomass type: rubber wood, sawdust, and biomass solid waste Char may be considered or not in model. Main results: chemical composition of producer gas	Global reaction: CHhOoNnSs + wH2O(l) + mO2 +gN2 → x1H2 + x2CO + x3CH4 + x4CO2 + x5H2O(g) + (n/2 + g)N2 + x6C(s) + sH2S (r1) CH4 + H2O ↔ CO +3H2 with equilibrium constant as: ${\mathrm{K}}_1={\displaystyle \frac{x_1^3{\mathrm{x}}_2{P}^2}{{\mathrm{x}}_3{\mathrm{x}}_5}}$ (r2) CO + H2O ↔ CO2 + H2 with equilibrium constant as: ${\mathrm{K}}_2={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_4}{{\mathrm{x}}_2{\mathrm{x}}_5}}$ (r3) C(s) + H2O ↔ CO + H2 with equilibrium constant as: ${\mathrm{K}}_3={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_{2}P}{{\mathrm{x}}_5}}$ x2 + x3 + x4 = 1 (if char is not considered, i.e., x6 = 0) x2 + x3 + x4 + x6 = 1 (if char is considered) 2x1 + 4x3 + 2x5 + 2s = h + 2w o + w + 2m = x2 + 2x4 + x5 Equilibrium constants for modified model: ${\mathsf{\beta }}_1{\mathrm{K}}_1={\displaystyle \frac{{\mathrm{x}}_1^3{\mathrm{x}}_2{\mathrm{P}}^2}{{\mathrm{x}}_3{\mathrm{x}}_5}}$ where β1 is determined by fixing CH4 fraction in dry gas at its average value of experimental data ${{\mathsf{\beta }}_2\mathrm{K}}_2={\displaystyle \frac{{\mathrm{x}}_1{\mathrm{x}}_4}{{\mathrm{x}}_2{\mathrm{x}}_5}}$ where β2 is determined by fixing CO fraction in dry gas at its average value of experimental data
[50]	Model type: equilibrium model of global reduction reactions for a downdraft gasifier. Biomass source: Douglas fir bark Main results: chemical composition of dry gas. Parametric studies on moisture content, pressure, equivalence ratio, and initial reaction temperature in reduction zone affecting the dry gas composition and temperature, heating value, unconverted char, gasification efficiency, and absorbed heat in reduction zone.	Global reaction: C6HhOoNnSs + $\dot{w}$H2O + $\dot{y}$O2+ 3.76$\dot{y}$N2 → ${\dot{n}}_1$CO + ${\dot{n}}_2$CO2 + ${\dot{n}}_3$H2 + ${\dot{n}}_4$H2O + ${\dot{n}}_5$CH4 + ${\dot{n}}_6$C + ${\dot{n}}_7$N2 + ${\dot{n}}_8$SO2 Kinetic model (r1) CO + H2O ↔ CO2 + H2 (r2) CH4 + H2O ↔ CO + 3H2 (r3) C + CO2 ↔ 2CO (r4) C + H2O ↔ CO + H2 (r5) C + 2H2 ↔ CH4 Approximations: ${\dot{\mathrm{n}}}_3={\dot{\mathrm{n}}}_1$ ${\dot{\mathrm{n}}}_4={\dot{\mathrm{n}}}_2+\dot{\mathrm{w}}$ Mass balance ${\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{S}\mathrm{r}}_{\mathrm{j},\mathrm{i}}{\mathrm{A}}_{\mathrm{i}}={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{S}\mathrm{p}}_{\mathrm{j},\mathrm{i}}{\mathrm{A}}_{\mathrm{i}}$ where i = CO, CO2, H2, H2O, CH4, C, N2, SO2 and j = 1,2,3…rn Heat of absorption in reduction zone ${\dot{\mathrm{Q}}}_{\mathrm{r}}={\sum }_{\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{t}}^{\mathrm{n}}{\dot{\mathrm{m}}}_{\mathrm{i}}{\mathrm{H}}_{\mathrm{r}}^{\mathrm{o}}-{\sum }_{\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}}^{\mathrm{n}}{\dot{\mathrm{m}}}_{\mathrm{i}}{\mathrm{H}}_{\mathrm{p}}^{\mathrm{o}}$
[204]	Model type: equilibrium and kinetic model for reduction zone in a downdraft gasifier Biomass source: rubber wood $\mathrm{C}\mathrm{R}\mathrm{F}=4.0012\left(10\mathrm{z}\right)-3.0012$ where z (in mm) is the downward distance traveled by the particle Main results: chemical composition of dry gas, temperature and calorific value of producer gas, conversion efficiency, absorbed heat in reduction zone, and gasifier power output	Global reaction: C6HhOo + $\dot{\mathrm{w}}$H2O + $\dot{\mathrm{y}}$O2+ 3.76$\dot{\mathrm{y}}$N2 → ${\dot{\mathrm{n}}}_1$CO + ${\dot{\mathrm{n}}}_2$CO2 + ${\dot{\mathrm{n}}}_3$H2 + ${\dot{\mathrm{n}}}_4$H2O + ${\dot{\mathrm{n}}}_5$CH4 + ${\dot{\mathrm{n}}}_6$C(char) + ${\dot{\mathrm{n}}}_7$N2 ${\mathrm{n}}_{\mathrm{C}}={\dot{\mathrm{n}}}_{\mathrm{g}}\left({\mathsf{\chi }}_{\mathrm{e}\mathrm{q},\mathrm{C}\mathrm{O}}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{O}}_2}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{H}}_4}\right)+{\dot{\mathrm{n}}}_{\mathrm{C}}$ ${\mathrm{n}}_{\mathrm{H}}={\dot{\mathrm{n}}}_{\mathrm{g}}\left(2{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{H}}_2}+{2\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{H}}_2\mathrm{O}}+{4\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{H}}_4}\right)$ ${\mathrm{n}}_{\mathrm{O}}={\dot{\mathrm{n}}}_{\mathrm{g}}\left({\mathsf{\chi }}_{\mathrm{e}\mathrm{q},\mathrm{C}\mathrm{O}}+{2\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{O}}_2}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{H}}_2\mathrm{O}}\right)$ ${\mathrm{n}}_{\mathrm{N}}={2\dot{\mathrm{n}}}_{\mathrm{g}}{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},\mathrm{N}}$ ${\mathsf{\chi }}_{\mathrm{e}\mathrm{q},\mathrm{C}\mathrm{O}}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{O}}_2}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{H}}_2}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{H}}_2\mathrm{O}}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},{\mathrm{C}\mathrm{H}}_4}+{\mathsf{\chi }}_{\mathrm{e}\mathrm{q},\mathrm{N}}=1$ Kinetic model (r1) C + CO2 ↔ 2CO with ${\mathrm{r}}_1=\mathrm{C}\mathrm{R}\mathrm{F}\left(36.16{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{77.39}{\mathrm{R}\mathrm{T}}}}\left[{\mathsf{\chi }}_{{\mathrm{C}\mathrm{O}}_2}-{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{C}\mathrm{O}}^2}{{\mathrm{K}}_1}}\right]$ and ${\mathrm{K}}_1={\mathrm{e}}^{-{\displaystyle \frac{1}{\mathrm{R}\mathrm{T}}}\left[2{\mathrm{g}}_{\mathrm{C}\mathrm{O}}^{\mathrm{O}}-{\mathrm{g}}_{{\mathrm{C}\mathrm{O}}_2}^{\mathrm{O}}-{\mathrm{g}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\mathrm{O}}\right]}$ (r2) C + 2H2O ↔ CO + H2 with ${\mathrm{r}}_2=\mathrm{C}\mathrm{R}\mathrm{F}\left(1.517\times {10}^4{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{121.62}{\mathrm{R}\mathrm{T}}}}\left[{\mathsf{\chi }}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{C}\mathrm{O}}{\mathsf{\chi }}_{{\mathrm{H}}_2}}{{\mathrm{K}}_2}}\right]$ and ${\mathrm{K}}_2={\mathrm{e}}^{-{\displaystyle \frac{1}{\mathrm{R}\mathrm{T}}}\left[{\mathrm{g}}_{\mathrm{C}\mathrm{O}}^{\mathrm{O}}+{\mathrm{g}}_{{\mathrm{H}}_2}^{\mathrm{O}}-{2\mathrm{g}}_{{\mathrm{H}}_2\mathrm{O}}^{\mathrm{O}}-{\mathrm{g}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\mathrm{O}}\right]}$ (r3) C + 2H2 ↔ CH4 with ${\mathrm{r}}_3=\mathrm{C}\mathrm{R}\mathrm{F}\left(4.189\times {10}^{-3}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{19.21}{\mathrm{R}\mathrm{T}}}}\left[{\mathsf{\chi }}_{{\mathrm{H}}_2}^2-{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{C}{\mathrm{H}}_4}}{{\mathrm{K}}_3}}\right]$ and ${\mathrm{K}}_3={\mathrm{e}}^{-{\displaystyle \frac{1}{\mathrm{R}\mathrm{T}}}\left[{\mathrm{g}}_{{\mathrm{C}\mathrm{H}}_4}^{\mathrm{O}}-{2\mathrm{g}}_{{\mathrm{H}}_2}^{\mathrm{O}}-{\mathrm{g}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\mathrm{O}}\right]}$ (r4) CH4 + H2O ↔ CO + 3H2 with ${\mathrm{r}}_4=\mathrm{C}\mathrm{R}\mathrm{F}\left(7.301\times {10}^{-2}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{36.15}{\mathrm{R}\mathrm{T}}}}\left[{\mathsf{\chi }}_{{\mathrm{C}\mathrm{H}}_4}{\mathsf{\chi }}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathsf{\chi }}_{\mathrm{C}\mathrm{O}}{\mathsf{\chi }}_{{\mathrm{H}}_2}^3}{{\mathrm{K}}_4}}\right]$ and ${\mathrm{K}}_4={\mathrm{e}}^{-{\displaystyle \frac{1}{\mathrm{R}\mathrm{T}}}\left[{\mathrm{g}}_{\mathrm{C}\mathrm{O}}^{\mathrm{O}}+{3\mathrm{g}}_{{\mathrm{H}}_2}^{\mathrm{O}}-{\mathrm{g}}_{{\mathrm{H}}_2\mathrm{O}}^{\mathrm{O}}-{\mathrm{g}}_{{\mathrm{C}\mathrm{H}}_4}^{\mathrm{O}}\right]}$

summarizes the stoichiometric equilibrium models and modified equilibrium models.

9.2.2. Non-Stoichiometric Models

Non–stoichiometric models are employed in a variety of circumstances, including feeds with unknown molecular formulas. The model input encompasses ultimate analysis data and equivalence ratio as an element vector to be solved by minimizing Gibbs free energy utilizing Lagrangian multipliers [208]. In certain instances, tar formation is disregarded due to its minimal quantity in a downdraft gasifier. Nevertheless, tar is formed in the pyrolysis section of the gasifier and is further cracked [117]. Various models have been reported in the literature, and

Table 14. Non-stoichiometric equilibrium models used for biomass gasification.

Ref.	Considerations	Main Reactions and Equations
[60]	Model type: non-stoichiometric equilibrium model for a downdraft gasifier Biomass source: rice husk Main results: Gas lower heating value and gasification efficiency, and chemical composition of producer gas. Maximum gasification efficiency is 58.57% at 725 °C and equivalence ratio of 0.25 with LHV	Assumptions: Equivalence ratios of 0.25, 0.35, and 0.45 Gas lower heating value in (kJ Nm−3): $\mathrm{L}\mathrm{H}\mathrm{V}=126.36\times \mathrm{C}\mathrm{O}+107.98\times {\mathrm{H}}_2+358.18\times {\mathrm{C}\mathrm{H}}_4$ where CO, H2, and CH4 are in mol% Rice husk lower heating value in (kJ kg−1): $\mathrm{L}\mathrm{H}\mathrm{V}=\mathrm{34,835}\times \mathrm{C}+126.36\times \mathrm{H}-\mathrm{10,800}\times \mathrm{O}+6280\times \mathrm{N}+\mathrm{10,465}\times \mathrm{S}$ where C, H, O, N, S are in wt% Gasification efficiency (%): $\mathsf{\eta }={\displaystyle \frac{{\mathrm{L}\mathrm{H}\mathrm{V}}_{\mathrm{g}\mathrm{a}\mathrm{s}}}{{\mathrm{L}\mathrm{H}\mathrm{V}}_{\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}\mathrm{h}\mathrm{u}\mathrm{s}\mathrm{k}}}}\times \mathrm{Y}\times 100$ where Y is in (Nm3 kg−1) Reactions involved: (r1) C + H2O → CO + H2 (r2) CO + H2O → CO2 + H2 (r3) C + CO2 → 2CO
[66]	Model type: non-stoichiometric equilibrium model Biomass source: Tunisian olive pomace Main results: sensitivity analysis of temperature, pressure, and steam/biomass molar ratio in the feed was studied and their influence on gas composition, syngas yield, syngas quality (H2/CO), and carbon conversion	Assumptions: Tar is not considered to be formed Char is only formed by carbon Volatile products are H2, CO, CO2, CH4, H2O, N2, NO, NO2 Biomass gasification proceeds as: CcHhOoNn + wH2O → x1CH4 + x2H2 + x3H2O + x4CO + x5CO2 + x6NO + x7NO2 +x8N2 + x9C with: c = x1 + x4 + x5 + x9 h + 2w = 4x1 + 2x2 + 2x3 o + w = x3 + x4 + 2x5 + x6 + 2x7 n = x6 + x7 + 2x8 Subject to the elemental conservation constraints per one mole of: $\mathrm{C}:{\mathrm{n}}_{{\mathrm{C}\mathrm{H}}_4}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}}+{\mathrm{n}}_{{\mathrm{C}\mathrm{O}}_2}+{\mathrm{n}}_{\mathrm{C}}=0.488$ $\mathrm{H}:4{\mathrm{n}}_{{\mathrm{C}\mathrm{H}}_4}+2{\mathrm{n}}_{{\mathrm{H}}_2}+2{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{O}}=2\times 0.376+2\mathrm{w}$ $\mathrm{O}:{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{O}}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}}+2{\mathrm{n}}_{{\mathrm{C}\mathrm{O}}_2}+{\mathrm{n}}_{\mathrm{N}\mathrm{O}}+2{\mathrm{n}}_{{\mathrm{N}\mathrm{O}}_2}=2\times 0.13+\mathrm{w}$ $\mathrm{N}:{\mathrm{n}}_{\mathrm{N}\mathrm{O}}+{\mathrm{n}}_{{\mathrm{N}\mathrm{O}}_2}+2{\mathrm{n}}_{{\mathrm{N}}_2}=2\times 0.00578$
[209]	Model type: non-stoichiometric equilibrium model Biomass source: forest waste considered as CH1.4O0.85N0.02S0.00004 Main results: Chemical composition of syngas considering the ammonia and hydrogen sulfide contents.	Biomass gasification proceeds as: CHhOoNnSs + wH2O(l) + qH2O(g) + m(O2 + δN2) → x1CO + x2CO2 + x3O2 + x4CH4 + x5H2 +x6H2O + x7N2 + x8NO + x9NO2 + x10NH3 + x11HCN +x12H2S + x13SO2 + x14SO3 + x15COS + (1 − α)C Subject to the elemental conservation constraints: $\mathrm{C}:{\mathrm{n}}_{{\mathrm{C}\mathrm{H}}_4}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}}+{\mathrm{n}}_{{\mathrm{C}\mathrm{O}}_2}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}\mathrm{S}}+{\mathrm{n}}_{\mathrm{H}\mathrm{C}\mathrm{N}}=\mathsf{\alpha }$ $\mathrm{H}:4{\mathrm{n}}_{{\mathrm{C}\mathrm{H}}_4}+2{\mathrm{n}}_{{\mathrm{H}}_2}+2{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{O}}+3{\mathrm{n}}_{{\mathrm{N}\mathrm{H}}_3}+{\mathrm{n}}_{\mathrm{H}\mathrm{C}\mathrm{N}}+2{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{S}}=\mathrm{h}+2\mathrm{w}+2\mathrm{q}$ $\mathrm{O}:{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{O}}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}}+2{\mathrm{n}}_{{\mathrm{C}\mathrm{O}}_2}+2{\mathrm{n}}_{{\mathrm{O}}_2}+{\mathrm{n}}_{\mathrm{N}\mathrm{O}}+2{\mathrm{n}}_{{\mathrm{N}\mathrm{O}}_2}+2{\mathrm{n}}_{{\mathrm{S}\mathrm{O}}_2}+3{\mathrm{n}}_{{\mathrm{S}\mathrm{O}}_3}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}\mathrm{S}}=2\mathrm{m}\times \mathrm{w}\times \mathrm{q}+\mathrm{o}$ $\mathrm{N}:{\mathrm{n}}_{\mathrm{N}\mathrm{O}}+{\mathrm{n}}_{{\mathrm{N}\mathrm{O}}_2}+2{\mathrm{n}}_{{\mathrm{N}}_2}+{\mathrm{n}}_{{\mathrm{N}\mathrm{H}}_3}+{\mathrm{n}}_{\mathrm{H}\mathrm{C}\mathrm{N}}=2\mathsf{\delta }\mathrm{m}+\mathrm{n}$ $\mathrm{S}:{\mathrm{n}}_{{\mathrm{H}}_2\mathrm{S}}+{\mathrm{n}}_{{\mathrm{S}\mathrm{O}}_2}+2{\mathrm{n}}_{{\mathrm{S}\mathrm{O}}_3}+{\mathrm{n}}_{\mathrm{C}\mathrm{O}\mathrm{S}}=\mathrm{s}$
[210]	Model type: non-stoichiometric model for a downdraft gasifier Biomass source: olive wood, Miscanthus, and cardoon, and olive wood had the best performance Main results: chemical composition of producer gas, lower heating value	Assumptions: Low tar amount is produced Pressure drop is negligible Gas behavior is ideal Carbon is converted into gas Equivalence ratio of 0.45 Heating values are calculated as: $\mathrm{H}\mathrm{H}\mathrm{V}=0.3491\times \mathrm{C}+1.1783\times \mathrm{H}+0.1005\times \mathrm{S}-0.1034\times \mathrm{O}-0.0151\times \mathrm{N}-0.0211\times \mathrm{A}\mathrm{s}\mathrm{h}$ where C, H, S, O, N, and Ash content is in (wt%) $\mathrm{L}\mathrm{H}\mathrm{V}=\mathrm{H}\mathrm{H}\mathrm{V}-9{\mathrm{m}}_{\mathrm{H}}{\mathrm{h}}_{\mathrm{w}}$
[208]	Model type: non-stoichiometric equilibrium model Biomass source: rice husk and bamboo and saw dusts Main results: Optimum parameters for Fischer–Tropsch synthesis decentralized power generation were reported	Assumptions: Gas behavior is ideal Equivalence ratios: 0 to 1.0 Temperatures: 400 to 1000 °C Saw dust formula as: CH1.193N0.007O0.585 Rice husk formula as: CH1.699N0.003O0.828 Bamboo dust formula as: CH1.657N0.018O0.904 Higher heating value in (MJ kg−1): $\mathrm{H}\mathrm{H}\mathrm{V}=0.3491\times \mathrm{C}+1.1783\times \mathrm{H}+0.10055\times \mathrm{S}-0.1034\times \mathrm{O}-0.151\times \mathrm{N}-0.0211\times \mathrm{A}\mathrm{s}\mathrm{h}$ where C, H, O, N, S, and Ash are in (wt%) on dry basis

provides a summary of their characteristics. A detailed algorithm that solves the non–stoichiometric model step by step has been published elsewhere [209]. Antonoupoulos et al. [210] proposed a non–stoichiometric model for a downdraft gasifier using three types of biomasses: olive wood, Miscanthus, and cardoon. The model aims to maximize the heating value of the producer gas. However, the authors noted that if the focus is on maximizing the hydrogen composition, the producer gas could be used as fuel in an internal combustion engine connected to a generator.

Venugopal et al. [60] investigated the effect of temperature (ranging from 600 to 800 °C) and equivalence ratios (ranging from 0.25 to 0.45) on the gasification of rice husk. The H₂/CO and CO/CO₂ ratios were compared using a Gibbs free energy minimization software. Hydrogen formation was found to depend on the water–gas and water–gas shift reactions, while CO formation occurred through the water–gas and Boudouard reactions. The maximum gasification efficiency of 58.6% was achieved at 725 °C with an equivalence ratio of 0.25. Furthermore, the authors reported that the yield of H₂ and CO were 17.97% and 35.53%, respectively. Increasing the equivalence ratio enhances the carbon conversion efficiency but reduces the LHV of the gas. Additionally, Mendiburu et al. [62] found that keeping a moisture content below 15% increases both the LHV and carbon conversion efficiency.

Syngas rich in hydrogen was produced using Tunisian olive pomace as feed and steam as a gasifying agent. The quality of the syngas was evaluated based on the H₂/CO ratio, which was found to be optimal at 1000 K, 1 bar, and steam/biomass molar ratio of 0.5. According to Tilouche et al. [66] increasing the temperature leads to an increase in syngas production, while higher pressure has a negative impact on both its production and quality. Buragohain et al. [208] reported on the optimization of biomass gasification for Fischer–Tropsch synthesis and decentralized power generation using a non–stoichiometric equilibrium model. The optimal parameters for Fischer–Tropsch synthesis were equivalence ratios between 0.2 and 0.4 and temperatures ranging from 800 to 1000 °C with air as the gasifying agent. The optimal parameters for decentralized power generation were equivalence ratios ranging from 0.3 to 0.4 and temperatures ranging from 700 to 800 °C with air. The producer gas obtained after gasification could be used for both purposes. However, in the case of Fischer–Tropsch synthesis, the gas must be compressed prior to entering the reactor due to the high pressure requirement (15–20 bar). The concentrations of minor products, such as nitrogen and sulfur species, were determined by solving a non-stoichiometric equilibrium model. Furthermore, the study investigated the lower heating value and composition of the producer gas. It is of the utmost importance to calculate the quantity of species, such as H₂S, NH₃, and SO_x, as different gas cleaning processes can be employed depending on their concentration, as reported by Gambarotta et al. [209]. The authors utilized forest waste as biomass for gasification and determined that the quantities of H₂S and NH₃ are significant even at low air-to-fuel ratios.

9.3. Kinetic and Reactor Models

The equilibrium and kinetic modeling approaches yielded comparable outcomes with regard to the minimum bed length required for complete char conversion. Following this threshold, the utilization of equilibrium modeling to calculate gas composition is a viable option. Nevertheless, despite the longer calculation time necessitated by the numerous reactions involved in the gasification process and the transport phenomena incorporated in the model solution, kinetic models offer enhanced accuracy in predicting the syngas composition and gasifier behavior [13,211].

As illustrated in

Table 15. Kinetic and reactor models commonly used in the biomass gasification.

Reference and Assumptions	Kinetics	Reactor Model
[212] Biomass: wood pellets (C6H9O4), with char as CH0.26O0.09 and tar as CH1.88O0.7. Reactor model: One-dimension discretization of transient downdraft gasifier. Main results: CO and CO2 composition at different temperatures (700–850 °C), char conversion, and parametric study at steady state conditions to obtain the temperature profiles, gas and tar yields, and conversion of char at different bed heights and air-to-fuel ratios.	(r1) mbm → wcmbm + wg1mbm + wtmbm $r_1={\displaystyle \frac{-m_{bm}\left(2.65\times {10}^3{s}^{-1}\right)e^{-\frac{72.2}{RT}}}{M_{bm}}}$  (r2) CH0.26O0.09 + 1.02O2 → CO2 + 0.13H2O ${\mathrm{r}}_2=-{\mathrm{r}}_{{\mathrm{O}}_{2\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}}}{\mathrm{m}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}{\mathrm{c}}_{{\mathrm{O}}_2}^{0.47}$ (r3) CH0.26O0.09 + CO2 → 2CO + 0.09H2O + 0.04H2 ${\mathrm{r}}_3=-{\mathrm{r}}_{{\mathrm{C}\mathrm{O}}_{2\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}}}{\mathrm{m}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}{\mathrm{c}}_{{\mathrm{C}\mathrm{O}}_2}^{0.52}$ (r4) CH0.26O0.09 + 0.91H2O → CO + 1.04H2 ${\mathrm{r}}_4=-{\mathrm{r}}_{{\mathrm{H}}_2{\mathrm{O}}_{\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}}}{\mathrm{m}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}{\mathrm{c}}_{{\mathrm{H}}_2\mathrm{O}}$ (r5) mt → wCOmt + wCO2mt + wCH4mt ${\mathrm{r}}_5={\displaystyle \frac{-{\mathrm{m}}_{\mathrm{t}}\left(6.2\times {10}^6{\mathrm{m}}^3{\mathrm{k}\mathrm{g}}^{-1}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-\frac{166}{\mathrm{R}\mathrm{T}}}}{{\mathsf{\epsilon }\mathrm{M}}_{\mathrm{t}}}}$ (r6) H2 + ½O2 → H2O ${\mathrm{r}}_6=-\left[1\times {10}^{11}{\mathrm{m}}^3{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}{\mathrm{s}}^{-1}\right]{\mathrm{e}}^{-{\displaystyle \frac{42}{\mathrm{R}\mathrm{T}}}}{\mathrm{c}}_{{\mathrm{H}}_2}{\mathrm{c}}_{{\mathrm{O}}_2}$ (r7) CO + ½O2 → CO2 ${\mathrm{r}}_7=-\left[10\times {10}^{17.6}{\left({\mathrm{m}}^3{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}\right)}^{1.25}{\mathrm{s}}^{-1}{\mathrm{K}}^{-1}\right]{\mathrm{e}}^{-{\displaystyle \frac{166}{\mathrm{R}\mathrm{T}}}}{\mathrm{c}}_{\mathrm{C}\mathrm{O}}{\mathrm{c}}_{{\mathrm{O}}_2}^{0.25}{\mathrm{c}}_{{\mathrm{H}}_2\mathrm{O}}$ (r8) CH4 + 1.5O2 → CO + 2H2O ${\mathrm{r}}_8=-\left[9.2\times {10}^6{\left({\mathrm{m}}^3{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}\right)}^{0.5}{\mathrm{s}}^{-1}{\mathrm{K}}^{-1}\right]{\mathrm{e}}^{-{\displaystyle \frac{80}{\mathrm{R}\mathrm{T}}}}\mathrm{T}{\mathrm{c}}_{{\mathrm{C}\mathrm{H}}_4}^{0.5}{\mathrm{c}}_{{\mathrm{O}}_2}$ (r9) CH1.88O0.7 + 0.62O2 → CO + 0.94H2O ${\mathrm{r}}_9=-\left[9.2\times {10}^6{\left({\mathrm{m}}^3{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}\right)}^{0.5}{\mathrm{s}}^{-1}{\mathrm{K}}^{-1}\right]{\mathrm{e}}^{-{\displaystyle \frac{80}{\mathrm{R}\mathrm{T}}}}\mathrm{T}{\mathrm{c}}_{\mathrm{t}\mathrm{a}\mathrm{r}}^{0.5}{\mathrm{c}}_{{\mathrm{O}}_2}$ (r10) CO + H2O ↔ CO2 + H2 ${\mathrm{r}}_{10}=-\left[2.78\left({\mathrm{m}}^3{\mathrm{m}\mathrm{o}\mathrm{l}}^{-1}\right){\mathrm{s}}^{-1}\right]{\mathrm{e}}^{-{\displaystyle \frac{12.6}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{c}}_{\mathrm{C}\mathrm{O}}{\mathrm{c}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{c}}_{{\mathrm{H}}_2}{\mathrm{c}}_{{\mathrm{C}\mathrm{O}}_2}}{{\mathrm{e}}^{\left(\frac{4577.8}{\mathrm{T}}-4.33\right)}}}\right]$	Mass balance for solid species ${\displaystyle \frac{\mathsf{\partial }{\mathsf{\gamma }}_{\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}=-{\mathrm{U}}_{\mathrm{s}}{\displaystyle \frac{\mathsf{\partial }{\mathsf{\gamma }}_{\mathrm{i}}}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{M}}_{\mathrm{i}}\sum _{\mathrm{n}}{\mathrm{r}}_{\mathrm{i},\mathrm{n}}$ Mass balance for gas species ${\displaystyle \frac{\mathsf{\partial }{\mathsf{\gamma }}_{\mathrm{j}}}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathrm{U}}_{\mathrm{g}}{\mathsf{\gamma }}_{\mathrm{j}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathrm{D}}_{\mathrm{j},{\mathrm{N}}_2}{\displaystyle \frac{\mathsf{\partial }{\mathsf{\gamma }}_{\mathrm{j}}}{\mathsf{\partial }\mathrm{z}}}\right)+\mathsf{\epsilon }{\mathrm{M}}_{\mathrm{j}}\sum _{\mathrm{m}}{\mathrm{r}}_{\mathrm{i},\mathrm{m}}$ Energy balance for solid species ${\mathsf{\gamma }}_{\mathrm{s}}{\displaystyle \frac{\mathsf{\partial }\left({\mathrm{T}}_{\mathrm{s}}{\mathrm{c}}_{\mathrm{P},\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\mathsf{\gamma }}_{\mathrm{s}}{\mathrm{U}}_{\mathrm{s}}{\displaystyle \frac{\mathsf{\partial }\left({\mathrm{T}}_{\mathrm{s}}{\mathrm{c}}_{\mathrm{P},\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{z}}}+\left(1-\mathsf{\epsilon }\right){\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathsf{\lambda }}_{\mathrm{s}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{s}}}{\mathsf{\partial }\mathrm{z}}}\right)+\sum _{\mathrm{n}}{\mathrm{r}}_{\mathrm{n}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{n},\mathrm{i}}+{\dot{\mathrm{Q}}}_{\mathrm{w}\mathrm{s}}+{\dot{\mathrm{Q}}}_{\mathrm{g}\mathrm{s}}$ Energy balance for gas species ${\mathsf{\gamma }}_{\mathrm{g}}{\displaystyle \frac{\mathsf{\partial }\left({\mathrm{T}}_{\mathrm{g}}{\mathrm{c}}_{\mathrm{P},\mathrm{g}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\mathsf{\gamma }}_{\mathrm{g}}{\displaystyle \frac{\mathsf{\partial }\left({{\mathrm{U}}_{\mathrm{g}}\mathrm{T}}_{\mathrm{g}}{\mathrm{c}}_{\mathrm{P},\mathrm{g}}\right)}{\mathsf{\partial }\mathrm{z}}}+\mathsf{\epsilon }{\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathsf{\lambda }}_{\mathrm{g}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{g}}}{\mathsf{\partial }\mathrm{z}}}\right)+\sum _{\mathrm{m}}{\mathrm{r}}_{\mathrm{m}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{m},\mathrm{j}}+{\dot{\mathrm{Q}}}_{\mathrm{w}\mathrm{g}}+{\dot{\mathrm{Q}}}_{\mathrm{s}\mathrm{g}}$ Porosity variation equation ${\displaystyle \frac{\mathsf{\partial }{\mathsf{\epsilon }}_{\mathrm{P}}}{\mathsf{\partial }\mathrm{t}}}=-{\mathrm{U}}_{\mathrm{s}}{\displaystyle \frac{\mathsf{\partial }{\mathsf{\epsilon }}_{\mathrm{P}}}{\mathsf{\partial }\mathrm{z}}}-\sum _{\mathrm{n}}{\displaystyle \frac{\frac{\mathsf{\partial }{\mathsf{\gamma }}_{\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}{\left(1-\mathsf{\epsilon }\right){\mathsf{\rho }}_{\mathrm{i}}}}$ for i= C6H9O4, CH0.26O0.09 where γ, M, U, λ, cp, r, ρ, ε, and $\dot{\mathrm{Q}}$, are the mass concentration in kg m−1, molar mass in (kg mol−1), U is velocity in (m s−1), heat conductivity in (W m−1 K−1), thermal capacity in (J kg−1 K−1), molar reaction rate per volume in (mol s−1 m−3), density in (kg m−3), particle porosity dimensionless, heat flow in (J s−1), respectively, and subscripts i, s, g, p, ws, gs wg, sg, are referred to species i, solid, gas, solid particle, wall to solid transfer, gas to solid transfer, wall to gas transfer, solid particle to gas transfer. Considerations: z = 0.2 m, ε = 0.47, CO2 flowrate (NTP) = 2.15 (m3 h−1), N2 flowrate (NTP) = 4 (m3 h−1)
[213] Biomass: char from pyrolysis of wood Reactor model: one-dimensional steady state downdraft gasifier. Solution method: finite volume discretization in OpenFOAM. Main results: sensitivity analysis to study the influence of reactor inlet temperature and gas composition on char conversion, temperature profile of the bed, and syngas composition.	(r1) C + O2 → CO2 ${\mathrm{r}}_1={\mathsf{\omega }}_1{\mathrm{C}}_{\mathrm{C},0}$ ${\log }_{10}{\mathsf{\omega }}_1=-8.946825+23.2{\mathrm{P}}_{{\mathrm{O}}_2}-65.9{\mathrm{P}}_{{\mathrm{O}}_2}^2-4.95\times {10}^{-3}{\mathrm{P}}_{{\mathrm{O}}_2}\mathrm{T}+8.03334\times {10}^{-3}\mathrm{T}-3.12\times {10}^{-6}{\mathrm{T}}^2$ (r2) C + CO2 → 2CO ${\mathrm{r}}_2={\mathsf{\omega }}_2{\mathrm{C}}_{\mathrm{C},0}$ ${\log }_{10}{\mathsf{\omega }}_2=-32.00657+2.86{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}-2.9{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}^2+0.040878\mathrm{T}-1.45\times {10}^{-5}{\mathrm{T}}^2$ (r3) C + H2O → CO + H2 ${\mathrm{r}}_3={\mathsf{\omega }}_3{\mathrm{C}}_{\mathrm{C},0}$ ${\log }_{10}{\mathsf{\omega }}_3=-24.497639+7.82399{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-2.88{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}^2+0.029489\mathrm{T}-1.02\times {10}^{-5}{\mathrm{T}}^2$ (r4) CO + H2O ↔ CO2 + H2 ${\mathrm{r}}_4=1.85\times {10}^{-5}{\mathrm{e}}^{\left[12.88-{\displaystyle \frac{1855.5}{\mathrm{T}}}\right]}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}{\mathrm{P}}_{\mathrm{C}\mathrm{O}}{\mathrm{M}}_{\mathrm{C}\mathrm{O}}\left[1-{\displaystyle \frac{{\mathrm{P}}_{{\mathrm{H}}_2}{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}}{{{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}{\mathrm{P}}_{\mathrm{C}\mathrm{O}}\mathrm{e}}^{\left(\frac{4577.8}{\mathrm{T}}-4.33\right)}}}\right]$ Char conversion rate for reactions r1–r3 defined as ωi CC,0 is the initial concentration of char	Continuity equation (including Darcy’s law) $\nabla ·\left(-{\displaystyle \frac{\mathrm{P}}{\mathrm{R}\mathrm{T}}}{\displaystyle \frac{\mathrm{K}}{\mathsf{\mu }}}\nabla \mathrm{P}\right)={\sum }_{\mathrm{j}=1}^6{\mathrm{r}}_{\mathrm{j}}$ where j = N2, CO2, H2O, CO, H2, O2 where K, P, μ, T, R, is permeability in (m2), pressure in (Pa), dynamic viscosity in (kg m−1 s−1), temperature in (K), universal gas constant (8.314 J mol−1 K−1), respectively. Mass balance for gas species $\nabla ·\left({\mathrm{U}}_{\mathrm{g}}{\mathrm{C}}_{\mathrm{g},\mathrm{j}}\right)=\nabla ·\left({\displaystyle \frac{\mathsf{\epsilon }}{\mathsf{\tau }}}{\mathrm{D}}_{\mathrm{j},{\mathrm{N}}_2}\nabla {\mathrm{C}}_{\mathrm{g},\mathrm{j}}\right)+{\mathrm{r}}_{\mathrm{j}}$ where Dj,N2 is the diffusion coefficient of species j into nitrogen; τ: tortuosity Mass balance for solid species (char) $\nabla ·\left({\mathrm{U}}_{\mathrm{c}}{\mathrm{C}}_{\mathrm{c}}\right)={\mathrm{r}}_{\mathrm{c}}$ where Uc: velocity of solid char in (m s−1); Cc: concentration of char in (mol m−3); rc: reaction rate of char in (mol m−3 s−1) ${\mathrm{U}}_{\mathrm{c}}={\mathrm{U}}_{\mathrm{c},0}\mathrm{f}\left(\mathrm{x}\right)$ where $\mathrm{f}\left(\mathrm{x}\right)=-1.03\times {10}^{-4}{\mathrm{X}}^2+4.25\times {10}^{-4}\mathrm{X}+1$ where X: char conversion. Energy balance $\left({\mathrm{U}}_{\mathrm{g}}{\mathrm{C}}_{\mathrm{g}}{\mathrm{c}}_{\mathrm{P},\mathrm{g}}{\mathrm{M}}_{\mathrm{g}}+{\mathrm{U}}_{\mathrm{c}}{\mathrm{C}}_{\mathrm{c}}{\mathrm{c}}_{\mathrm{P},\mathrm{c}}{\mathrm{M}}_{\mathrm{c}}\right)\nabla \mathrm{T}=\nabla ·\left({\mathsf{\lambda }}_{\mathrm{b}}\nabla \mathrm{T}\right)+{\dot{\mathrm{Q}}}_{\mathrm{r}}+{\dot{\mathrm{Q}}}_{\mathrm{l}\mathrm{o}\mathrm{s}\mathrm{s}}$ where ${\mathsf{\lambda }}_{\mathrm{b}}=\left(1-\mathsf{\epsilon }\right){\mathsf{\lambda }}_{\mathrm{c}}+{\mathsf{\epsilon }\mathsf{\lambda }}_{\mathrm{g}}$ where cp,g: specific heat capacity of gas phase in (J g−1 K−1), M: molar mass of gas phase in (g mol−1); Mc: molar mass of char in (mol s−1); λb: thermal conductivity of the porous bed char in (W m−1 K−1); λg: thermal conductivity of the gas phase in (W m−1 K−1); λc: thermal conductivity of the char in (W m−1 K−1) Considerations: z = 65 cm; εp = 0.75; particle thickness (ep) = 5.5 × 10−3 m
[214] Biomass: wood pellets. Reactor model: one-dimensional unsteady state Imbert gasifier. Solution method: finite differences. Main results: transient behavior of temperature, syngas concentration, temperature at solid bed, and application of the model to different geometries.	(r1) H2Ol → H2Ov CcHhOo → x1CO + x2CO2 +x3H2 + x4CH4 + x5H2O + x6C6H6O + x7C10H8 + x8C6H6 with ${\mathrm{k}}_{\mathrm{o}}=2.119\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=202.7$ (r2) C6H6O → CO + 0.4C10H8 +0.15C6H6 + 0.1CH4 + 0.75H2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^4$ and ${\mathrm{E}}_{\mathrm{a}}=100$ (r3) C10H8 → 7.38C + 0.275C6H6 + 0.97CH4 + 2.235H2 with ${\mathrm{k}}_{\mathrm{o}}=3.39\times {10}^{14}$ and ${\mathrm{E}}_{\mathrm{a}}=350$ Oxidation zone: (r4) CH4 + 1.5O2 → CO + 2H2O with ${\mathrm{k}}_{\mathrm{o}}=5.012\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=200$ (r5) CO + ½O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=4.4\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r6) H2 + ½O2 → H2O with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{14}$ and ${\mathrm{E}}_{\mathrm{a}}=42$ (r7) H2O → H2 + ½ O2 with ${\mathrm{k}}_{\mathrm{o}}=2.06\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=272.8$ (r8) 2C + O2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=1.47\times {10}^5$ and ${\mathrm{E}}_{\mathrm{a}}=113$ (r9) C + O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=5.67\times {10}^9$ and ${\mathrm{E}}_{\mathrm{a}}=160$ (r10) C6H6O + 4O2 → 6CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=2.4\times {10}^{11}\mathrm{T}$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r11) C6H6 + 4.5O2 → 6CO + 3H2O with ${\mathrm{k}}_{\mathrm{o}}=3.8\times {10}^7$ and ${\mathrm{E}}_{\mathrm{a}}=5.545$ (r12) C10H8 + 7O2 → 10CO + 4H2O with ${\mathrm{k}}_{\mathrm{o}}=9.2\times {10}^6\mathrm{T}$ and ${\mathrm{E}}_{\mathrm{a}}=80$ Reduction zone: (r13) CO + H2O → CO2 + H2 with ${\mathrm{k}}_{\mathrm{o}}=2.78$ and ${\mathrm{E}}_{\mathrm{a}}=12.6$ (r14) CH4 + H2O → CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=3.015\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r15) C + CO2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r16) C+ H2O → CO + H2 with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r17) C + 2H2 → CH4 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=42$ (r18) C6H6O + 3H2O → 2CO + CO2 + 2.95CH4 + 0.05C + 0.1H2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^5$ and ${\mathrm{E}}_{\mathrm{a}}=100$ (r19) C6H6 + 2H2O → 1.5C +2.5CH4 + 2CO with ${\mathrm{k}}_{\mathrm{o}}=3.39\times {10}^{16}$ and ${\mathrm{E}}_{\mathrm{a}}=443$	Mass conservation for biomass (wood): ${\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{s}}{\mathrm{A}}_{\mathrm{c}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{s}}{\mathrm{A}}_{\mathrm{c}}{\mathrm{U}}_{\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{A}}_{\mathrm{c}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{s}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ Mass conservation for moisture in the solid phase: ${\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{m}}{\mathrm{A}}_{\mathrm{c}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{m}}{\mathrm{A}}_{\mathrm{c}}{\mathrm{U}}_{\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{A}}_{\mathrm{c}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{m}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ Mass conservation for char: ${\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{c}}{\mathrm{A}}_{\mathrm{c}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{c}}{\mathrm{A}}_{\mathrm{c}}{\mathrm{U}}_{\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{A}}_{\mathrm{c}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{c}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ Mass conservation of gas (i = O2, CO, CO2, H2O, CH4, H2, tar, N2, and k = O2, H2O, N2) $\mathsf{\epsilon }{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{i}}{\mathrm{A}}_{\mathrm{c}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{i}}{\mathrm{A}}_{\mathrm{c}}{\mathrm{U}}_{\mathrm{s}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\sum }_{\mathrm{i}=\mathrm{k}}^{\mathrm{k}}{\mathsf{\rho }}_{\mathrm{i}0}{\mathrm{u}}_{\mathrm{i}0}{\mathrm{A}}_{\mathrm{i}}\mathsf{\delta }\left(\mathrm{z}-{\mathrm{z}}_0\right)+{\mathrm{A}}_{\mathrm{c}}{\mathrm{M}}_{\mathrm{i}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{i}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ Mass conservation for the total gas: $\mathsf{\epsilon }{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{g}}{\mathrm{A}}_{\mathrm{c}}\right)}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\left({\mathsf{\rho }}_{\mathrm{g}}{\mathrm{A}}_{\mathrm{c}}{\mathrm{U}}_{\mathrm{g}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\dot{\mathrm{W}}}_{\mathrm{g}\mathrm{i}}\mathsf{\delta }\left(\mathrm{z}-{\mathrm{z}}_0\right)+{\mathrm{A}}_{\mathrm{c}}{\mathrm{M}}_{\mathrm{i}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{i}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ Energy conservation for the solid phase: ${\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}{\mathrm{H}}_{\mathrm{s}\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }\left({\mathrm{A}}_{\mathrm{c}}{\mathrm{k}}_{\mathrm{s}}\frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{s}}}{\mathsf{\partial }\mathrm{z}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\dot{\mathrm{W}}}_{\mathrm{s}\mathrm{i}}{\mathrm{H}}_{\mathrm{s}\mathrm{i}}}{\mathsf{\partial }\mathrm{z}}}+{\sum }_{\mathrm{j}=1}^{\mathrm{m}}-\mathsf{\Delta }{\mathrm{H}}_{\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}-{\mathrm{A}}_{\mathrm{c}}{\mathrm{q}}_{\mathrm{s}\mathrm{t}}^{\prime \prime \prime }$ Energy conservation for the gas phase: $\mathsf{\epsilon }{\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}{\mathrm{H}}_{\mathrm{g}\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }\left({\mathrm{A}}_{\mathrm{c}}{\mathrm{k}}_{\mathrm{g}}\frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{g}}}{\mathsf{\partial }\mathrm{z}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\dot{\mathrm{W}}}_{\mathrm{g}\mathrm{i}}{\mathrm{H}}_{\mathrm{g}\mathrm{i}}}{\mathsf{\partial }\mathrm{z}}}+{\sum }_{\mathrm{j}=1}^{\mathrm{m}}-\mathsf{\Delta }{\mathrm{H}}_{\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}+{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\dot{\mathrm{W}}}_{\mathrm{g}\mathrm{i}}{\mathrm{H}}_{\mathrm{g}\mathrm{i}}\mathsf{\delta }\left(\mathrm{z}-{\mathrm{z}}_0\right)-{\mathrm{A}}_{\mathrm{c}}{\mathrm{q}}_{\mathrm{g}\mathrm{t}}^{\prime \prime \prime }$ Energy conservation for the inner wall: ${\displaystyle \frac{\mathsf{\partial }{\mathsf{\rho }}_{\mathrm{w}}{\mathrm{c}\mathrm{p}}_{\mathrm{w}}{\mathrm{T}}_{\mathrm{w}}}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathrm{k}}_{\mathrm{w}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{w}}}{\mathsf{\partial }\mathrm{z}}}\right)-{\displaystyle \frac{{\mathrm{q}}_{\mathrm{w}\mathrm{e}}^{\prime \prime }{\mathrm{A}}_{\mathrm{c}}}{{\mathrm{V}}_{\mathrm{w}}}}-{\mathrm{q}}_{\mathrm{w}\mathrm{t}}^{\prime \prime \prime }$ Energy conservation for the exit gas: ${\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{e}\mathrm{i}}{\mathrm{H}}_{\mathrm{e}\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }\left({\mathrm{A}}_{\mathrm{c}}{\mathrm{k}}_{\mathrm{g}}\frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{e}}}{\mathsf{\partial }\mathrm{z}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\displaystyle \frac{\mathsf{\partial }{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\dot{\mathrm{W}}}_{\mathrm{e}\mathrm{i}}{\mathrm{H}}_{\mathrm{e}\mathrm{i}}}{\mathsf{\partial }\mathrm{z}}}+{\sum }_{\mathrm{j}=1}^{\mathrm{m}}-\mathsf{\Delta }{\mathrm{H}}_{\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}+\mathrm{S}\left({\mathrm{q}}_{\mathrm{w}\mathrm{e}}^{\prime \prime }-{\mathrm{q}}_{\mathrm{e}\mathrm{a}}^{\prime \prime }\right)$ Energy conservation for the exiting gas: ${\displaystyle \frac{\mathsf{\partial }{\mathsf{\rho }}_{\mathrm{e}\mathrm{i}}}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }\left({\mathrm{q}}_{\mathrm{e}\mathrm{i}}{\mathrm{u}}_{\mathrm{e}}\right)}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{M}}_{\mathrm{i}}{\sum }_{\mathrm{j}=1}^{\mathrm{m}}{\mathsf{\nu }}_{\mathrm{i}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ where the subscripts for s, m, c, g, w, ea, we, gt, and wt, correspond to solid, moisture, char, gas, wall, exiting gas to ambient, wall to exiting flow, total heat transfer, and total heat transfer from the wall to gas and solid phases, respectively; ρ is density in (kg m−3); Ac is cross-section area in (m2); W is mass flow per unit length in (kg m−1); U is superficial velocity in (m s−1); t is time in (s); T is temperature in (K); k is thermal conductivity in (kW m−1 K−1); q″ is heat flux in (kW m−2); q‴ is heat flux in (kW m−3), H is specific enthalpy in (kJ kg−1); z is axial coordinate; ΔH is heat of reaction in (kJ kmol−1); $\dot{\mathrm{W}}$ is mass flowrate in kg s m−1, r is reaction rate in (kmol s m−3); V is volume in (m3); M is molecular weight in (kg kmol−1). Considerations: Bed diameter: 0.1 m; ε = 0.5; dp = 0.01 m; moisture: 7.28 wt%; ρs0 = 416 (kg m−3)
[215] Biomass: biochar Reactor model: dynamic and steady state downdraft gasifier. Solution method: implicit finite volume and the upwind method. Main results: dynamic and steady state profiles for temperature and concentration of gas and solid phases	(r1) CO + H2O ↔ CO2 + H2 ${\mathrm{r}}_1=2.824\times {10}^{-2}{\mathrm{e}}^{\left(-{\displaystyle \frac{32.84}{\mathrm{R}\mathrm{T}}}\right)}\times \mathrm{C}\mathrm{R}\mathrm{F}\times \left({\mathrm{x}}_{\mathrm{C}\mathrm{O}}{\mathrm{x}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{x}}_{{\mathrm{C}\mathrm{O}}_2}{\mathrm{x}}_{{\mathrm{H}}_2}}{{\mathrm{K}}_1}}\right)$ (r2) H2O + C ↔ H2 + CO $r_2=1.517\times {10}^4{e}^{\left(-{\displaystyle \frac{121.62}{RT}}\right)}\times CRF\times \left(x_{H_{2}O}-{\displaystyle \frac{x_{CO}{x}_{H_2}}{K_2}}\right)$  (r3) CH4 + H2O ↔ CO + 3H2 $r_3=7.31\times {10}^{-2}{e}^{\left(-{\displaystyle \frac{36.15}{RT}}\right)}\times CRF\times \left(x_{C{H}_4}{x}_{H_{2}O}-{\displaystyle \frac{{x_{CO}x}_{H_2}^3}{K_3}}\right)$  (r4) CO2 + C ↔ 2 CO ${\mathrm{r}}_4=36.16{\mathrm{e}}^{\left(-{\displaystyle \frac{77.39}{\mathrm{R}\mathrm{T}}}\right)}\times \mathrm{C}\mathrm{R}\mathrm{F}\times \left({\mathrm{x}}_{{\mathrm{C}\mathrm{O}}_2}-{\displaystyle \frac{{\mathrm{x}}_{\mathrm{C}\mathrm{O}}}{{\mathrm{K}}_4}}\right)$ where $\mathrm{C}\mathrm{R}\mathrm{F}={\mathrm{e}}^{0.007\mathrm{z}}$ and z is the axial position in (mm)	Mass balance in the gas phase for species i: ${\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\epsilon }{\mathrm{C}}_{\mathrm{i}}\right)}{\mathsf{\partial }\mathrm{t}}}={\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathrm{D}}_{\mathrm{i}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{C}}_{\mathrm{i}}}{\mathsf{\partial }\mathrm{z}}}\right)-{\mathrm{U}}_{\mathrm{g}}{\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\epsilon }{\mathrm{C}}_{\mathrm{i}}\right)}{\mathsf{\partial }\mathrm{z}}}+\mathsf{\epsilon }{\sum }_{\mathrm{j}=1}^4{\mathsf{\upsilon }}_{\mathrm{i}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ where νij: stoichiometric coefficient of i in reaction j; Ug: gas input velocity in (m s−1); rj: reaction rate of species j; z: axial direction or bed height in (m); ε: bed porosity. Mass balance for solids: ${\displaystyle \frac{\mathsf{\partial }\mathrm{M}}{\mathsf{\partial }\mathrm{t}}}=-{\displaystyle \frac{\mathsf{\partial }\dot{\mathrm{m}}}{\mathsf{\partial }\mathrm{z}}}+\mathsf{\epsilon }{\sum }_{\mathrm{j}=2}^4{\mathsf{\upsilon }}_{\mathrm{i}\mathrm{j}}{\mathrm{r}}_{\mathrm{j}}$ where M: mass of solids in (kg); t: time in (s), ṁ: mass flowrate in (kg s−1). Heat balance in the gas phase for species i: ${\sum }_{\mathrm{i}=1}^5{\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\epsilon }{\mathrm{C}}_{\mathrm{i}}{\mathrm{C}\mathrm{p}}_{\mathrm{i}}{\mathrm{T}}_{\mathrm{g}}\right)}{\mathsf{\partial }\mathrm{t}}}={\mathrm{U}}_{\mathrm{g}}{\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\sum }_{\mathrm{i}=1}^5{\mathrm{C}}_{\mathrm{i}}{\mathrm{C}\mathrm{p}}_{\mathrm{i}}{\mathrm{T}}_{\mathrm{g}}\right)-{\displaystyle \frac{1}{{\mathrm{A}}_{\mathrm{R}}}}{\displaystyle \frac{\mathrm{d}{\mathrm{A}}_{\mathrm{S}}}{\mathrm{d}\mathrm{z}}}{\mathrm{h}}_{\mathrm{s}\mathrm{e}}\left({\mathrm{T}}_{\mathrm{g}}-{\mathrm{T}}_{\mathrm{s}}\right)+\mathsf{\epsilon }{\sum }_{\mathrm{j}=1}^4{\mathrm{r}}_{\mathrm{j}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{r}\mathrm{j}}$ where Tg: gas temperature in (K); Ci: concentration of species i in (kg m−3) Heat balance for solids: ${\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{t}}}\left(\left(1-\mathsf{\epsilon }\right){\mathsf{\rho }}_{\mathrm{s}}{\mathrm{C}\mathrm{p}}_{\mathrm{s}}{\mathrm{T}}_{\mathrm{s}}\right)={\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }\mathrm{z}}}\left({\mathsf{\lambda }}_{\mathrm{e}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{s}}}{\mathsf{\partial }\mathrm{z}}}+{\mathrm{Q}}_{\mathrm{r}}\right){\displaystyle \frac{1}{{\mathrm{V}}_{\mathrm{R}}}}+\left({\dot{\mathrm{m}}}_{\mathrm{e}}{\mathrm{C}\mathrm{p}}_{\mathrm{s}}{\mathrm{T}}_{\mathrm{s}}-{\dot{\mathrm{m}}}_{\mathrm{s}}{\mathrm{C}\mathrm{p}}_{\mathrm{s}}{\mathrm{T}}_{\mathrm{s}}\right)+{\displaystyle \frac{1}{{\mathrm{A}}_{\mathrm{R}}}}{\displaystyle \frac{\mathrm{d}{\mathrm{A}}_{\mathrm{S}}}{\mathrm{d}\mathrm{z}}}{\mathrm{h}}_{\mathrm{s}\mathrm{e}}\left({\mathrm{T}}_{\mathrm{e}}-{\mathrm{T}}_{\mathrm{s}}\right)-\mathsf{\epsilon }{\sum }_{\mathrm{j}=3}^4{\mathrm{r}}_{\mathrm{j}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{r}\mathrm{j}}$ where hse: convection heat coefficient between solids and gas in (kJ s−1m−2 K−1); As: solid face area in the cross section in (m2); Te: emulsion gas temperature in (K); Ts: solids temperature in (K); AR: bed cross area in (m2). Radiative flux density: ${\mathrm{Q}}_{\mathrm{r}}=-{\displaystyle \frac{16\mathsf{\sigma }{\mathrm{T}}_{\mathrm{s}}^3}{3\mathrm{K}}}{\displaystyle \frac{\mathsf{\partial }{\mathrm{T}}_{\mathrm{s}}}{\mathsf{\partial }\mathrm{z}}}$ where Qr: radiative flux density in the bed in (W m−2); K: extinction coefficient in (m−1); σ: Stefan-Boltzmann constant in (W m−2 K−4). Considerations: Gasifier 1: z = 0.083 m, diameter = 0.15 m; ε = 0.5; T steam = 473 K; steam velocity: 0.14–0.28 (m s−1) Gasifier 2: z = 0.236 m, diameter = 0.15 m; ε = 0.5; T steam = 473 K; steam velocity: 0.69 (m s−1)
[216] Char assumption: Biomass: char of beech chips (CH0.28O0.04). Reactor model: dynamic one-dimensional fixed bed reactor. Solution method: finite volume discretization Main results: temperature profiles, syngas composition and volume, cold gas efficiency, and LHV and HHV.	(r1) CO + H2O ↔ CO2 + H2 $$\begin{gathered} {\mathrm{r}}_1={\displaystyle \frac{\left[2\times {10}^7{\mathrm{e}}^{-\frac{199}{\mathrm{R}\mathrm{T}}}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}+3.3\times {10}^8{\mathrm{e}}^{-\frac{248}{\mathrm{R}\mathrm{T}}}{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}\right]\mathrm{f}\left(\mathrm{x}\right)}{1+\mathrm{A}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}+\mathrm{B}{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}+\mathrm{C}{\mathrm{P}}_{{\mathrm{H}}_2}+\mathrm{D}{\mathrm{P}}_{\mathrm{C}\mathrm{O}}}} \\ \mathrm{A}={\displaystyle \frac{2\times {10}^7{\mathrm{e}}^{-\frac{199}{\mathrm{R}\mathrm{T}}}}{8.4\times {10}^7{\mathrm{e}}^{-\frac{225}{\mathrm{R}\mathrm{T}}}}} \\ \mathrm{B}={\displaystyle \frac{3.3\times {10}^8{\mathrm{e}}^{-\frac{248}{\mathrm{R}\mathrm{T}}}}{8.4\times {10}^7{\mathrm{e}}^{-\frac{225}{\mathrm{R}\mathrm{T}}}}} \\ \mathrm{C}={\displaystyle \frac{1.8\times {10}^6{\mathrm{e}}^{-\frac{146}{\mathrm{R}\mathrm{T}}}}{8.4\times {10}^7{\mathrm{e}}^{-\frac{225}{\mathrm{R}\mathrm{T}}}}} \\ \mathrm{D}={\displaystyle \frac{2\times {10}^8{\mathrm{e}}^{-\frac{217}{\mathrm{R}\mathrm{T}}}}{8.4\times {10}^7{\mathrm{e}}^{-\frac{225}{\mathrm{R}\mathrm{T}}}}} \end{gathered}$$ $$\begin{gathered} \mathrm{f}\left(\mathrm{x}\right)=32.17{\mathrm{x}}^6-57.17{\mathrm{x}}^5+46.1{\mathrm{x}}^4-16.04{\mathrm{x}}^3+2.92{\mathrm{x}}^2+0.297\mathrm{x}+0.529 \\ \mathrm{x}=1-{\displaystyle \frac{{\mathrm{w}}_{\mathrm{a}\mathrm{s}\mathrm{h}}/{\mathrm{w}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}}{{\mathrm{w}}_{{\mathrm{a}\mathrm{s}\mathrm{h}}_0}/{\mathrm{w}}_{{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}_0}}} \end{gathered}$$	Mass balance (char, gas, and ash content of the char): ${\displaystyle \frac{\mathrm{d}{\mathrm{M}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\prime }}{\mathrm{d}\mathrm{t}}}+{\displaystyle \frac{\mathsf{\partial }{\dot{\mathrm{m}}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}}{\mathsf{\partial }\mathrm{x}}}={\dot{\mathrm{x}}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\prime }$ where ${\mathrm{M}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\prime }$ is the mass of char per unit length in (kg m−1), ${\dot{\mathrm{m}}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}$ is the char mass flowrate in (kg m−1 s−1), ${\dot{\mathrm{x}}}_{\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{r}}^{\prime }$ is the char converted during gasification per unit length ${\displaystyle \frac{\mathsf{\partial }{\dot{m}}_{gas}}{\mathsf{\partial }x}}={\dot{x}}_{gas}^{\prime }{=-\dot{x}}_{char}^{\prime }$ where ${\dot{\mathrm{m}}}_{\mathrm{g}\mathrm{a}\mathrm{s}}$ is the mass flowrate of char, ${\dot{\mathrm{x}}}_{\mathrm{g}\mathrm{a}\mathrm{s}}^{\prime }$ is the gas obtained per unit length. ${\displaystyle \frac{\mathrm{d}{\mathrm{M}}_{\mathrm{a}\mathrm{s}\mathrm{h}}^{\prime }}{\mathrm{d}\mathrm{t}}}+{\displaystyle \frac{\mathsf{\partial }{\dot{\mathrm{m}}}_{\mathrm{a}\mathrm{s}\mathrm{h}}}{\mathsf{\partial }\mathrm{x}}}=0$ where ${\mathrm{M}}_{\mathrm{a}\mathrm{s}\mathrm{h}}^{\prime }$ is mass of ash per unit length, ${\dot{\mathrm{m}}}_{\mathrm{a}\mathrm{s}\mathrm{h}}$is the ash mass flowrate. Energy balance: $\sum {\displaystyle \frac{\mathrm{d}\left({\mathrm{M}}^{\prime }\mathrm{u}\right)}{\mathrm{d}\mathrm{t}}}-\sum {\displaystyle \frac{\mathsf{\partial }\left(\dot{\mathrm{m}}\mathrm{H}\right)}{\mathsf{\partial }\mathrm{x}}}=\dot{{\mathrm{Q}}^{\prime }}$ where ${\mathrm{M}}^{\prime }$ is the mass per unit length, u is the internal energy, $\dot{\mathrm{m}}$ is the mass flowrate, H is the specific enthalpy, $\dot{{\mathrm{Q}}^{\prime }}$ heat exchanged per unit length. Equilibrium constant for water–gas shift reaction: $\mathrm{K}={\displaystyle \frac{{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}{\mathrm{P}}_{\mathrm{C}\mathrm{O}}}{{\mathrm{P}}_{{\mathrm{H}}_2}{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}}}=1.303\times {10}^{-6}{\mathrm{T}}^2+7.17\times {10}^{-4}\mathrm{T}-1.3006$ where T is temperature in (K).
[217] Biomass: Douglas fir bark (H3.03O1.17). Reactor model: steady state reduction zone of downdraft gasifier. Solution method: explicit finite differences. Main results: composition of producer gas and temperature profiles as the CRF varied.	(r1) C + CO2 ↔ 2CO ${\mathrm{r}}_1=\mathrm{n}\mathrm{C}\mathrm{R}\mathrm{F}\left(36.16{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{77.39}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{C}\mathrm{O}}_2}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}^2}{{\mathrm{K}}_1}}\right]$ (r2) C + H2O ↔ CO + H2 ${\mathrm{r}}_2=\mathrm{n}\mathrm{C}\mathrm{R}\mathrm{F}\left(1.517\times {10}^4{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{121.62}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}{\mathrm{P}}_{{\mathrm{H}}_2}}{{\mathrm{K}}_2}}\right]$ (r3) C + 2H2 ↔ CH4 ${\mathrm{r}}_3=\mathrm{n}\mathrm{C}\mathrm{R}\mathrm{F}\left(4.189\times {10}^{-3}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{19.21}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{H}}_2}^2-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}{\mathrm{H}}_4}}{{\mathrm{K}}_3}}\right]$ (r4) CH4 + H2O ↔ CO + 3H2 ${\mathrm{r}}_4=\mathrm{n}\mathrm{C}\mathrm{R}\mathrm{F}\left(7.301\times {10}^{-2}{\mathrm{s}}^{-1}\right){\mathrm{e}}^{-{\displaystyle \frac{36.15}{\mathrm{R}\mathrm{T}}}}\left[{\mathrm{P}}_{{\mathrm{C}\mathrm{H}}_4}{\mathrm{P}}_{{\mathrm{H}}_2\mathrm{O}}-{\displaystyle \frac{{\mathrm{P}}_{\mathrm{C}\mathrm{O}}{\mathrm{P}}_{{\mathrm{H}}_2}^3}{{\mathrm{K}}_4}}\right]$	$$\begin{gathered} {\displaystyle \frac{\mathrm{d}{\mathrm{n}}_{\mathrm{x}}}{\mathrm{d}\mathrm{z}}}={\displaystyle \frac{1}{\mathsf{\nu }}}\left[{\mathrm{R}}_{\mathrm{x}}-{\mathrm{n}}_{\mathrm{x}}{\displaystyle \frac{\mathrm{d}\mathsf{\nu }}{\mathrm{d}\mathrm{z}}}\right] \\ {\displaystyle \frac{\mathrm{d}\mathsf{\nu }}{\mathrm{d}\mathrm{z}}}={\displaystyle \frac{1}{\sum _{\mathrm{x}}{\mathrm{n}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}+\mathrm{n}\mathrm{R}}}\left\{{\displaystyle \frac{\sum _{\mathrm{x}}{\mathrm{n}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}\sum _{\mathrm{x}}{\mathrm{R}}_{\mathrm{x}}}{\mathrm{n}}}-{\displaystyle \frac{\sum _{\mathrm{i}}{\mathrm{r}}_{\mathrm{i}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{i}}}{\mathrm{T}}}-{\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{z}}}\left[{\displaystyle \frac{\mathsf{\nu }}{\mathrm{T}}}+{\displaystyle \frac{\mathsf{\nu }\sum _{\mathrm{x}}{\mathrm{n}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}}{\mathrm{P}}}\right]-{\displaystyle \sum _{\mathrm{x}}}{\mathrm{R}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}\right\} \\ {\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{z}}}=1183\left[{\mathsf{\rho }}_{\mathrm{g}\mathrm{a}\mathrm{s}}{\displaystyle \frac{{\mathsf{\nu }}^2}{{\mathsf{\rho }}_{\mathrm{a}\mathrm{i}\mathrm{r}}}}\right]+388.19\mathsf{\nu }-79.893 \end{gathered}$$ Energy balance: ${\displaystyle \frac{\mathrm{d}\mathrm{T}}{\mathrm{d}\mathrm{z}}}={\displaystyle \frac{1}{\mathsf{\nu }\sum _{\mathrm{x}}{\mathrm{n}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}}}\left[{\displaystyle \sum _{\mathrm{i}}}{\mathrm{r}}_{\mathrm{i}}\mathsf{\Delta }{\mathrm{H}}_{\mathrm{i}}-\mathsf{\nu }{\displaystyle \frac{\mathrm{d}\mathrm{P}}{\mathrm{d}\mathrm{z}}}-\mathrm{P}{\displaystyle \frac{\mathrm{d}\mathsf{\nu }}{\mathrm{d}\mathrm{z}}}-{\displaystyle \sum _{\mathrm{x}}}{\mathrm{R}}_{\mathrm{x}}{\mathrm{c}}_{\mathrm{x}}\mathrm{T}\right]$ where nx, ν, cx, P, T, ρ, and z, are molar density of species x in (mol m−3), superficial gas velocity in (m s−1), molar heat capacity in (J mol−1 K−1), total pressure in (Pa), temperature in (K), density in (kg m−3), and axial distance in (m), respectively. Considerations: z = 245 mm; νo = 1.175 (m s−1); To = 1400 K; Po = 1.005 atm; fp = 0.3; CRF = 1, 10, 100, 1000, exponential and linear variations

, a comprehensive overview of the reactions, kinetics and reactor models is provided, underscoring the intricate nature of certain models. The simulations conducted by several authors incorporate a series of chemical reactions that adhere to power law or Langmuir–Hinshelwood kinetic models. Some studies solve steady–state first–order differential equations, while others introduce time–dependent partial differential equations into the reactor model to solve transient models. The objective of presenting various kinetic models, particularly including downdraft gasifier models, is to facilitate comprehension of the development of specific mathematical representation, contingent on the intended simulation. Assuming that the gasifier is modeled as steady state implies the existence of stable zones. For instance, the reduction zone where complete char consumption occurs. Other models propose that the char conversion depends on the air flow or another gasifying agent and the residence time.

A model reported in the literature is the one–dimensional steady–state model, which considers both transient and steady–state heat transfer through the wall, with or without simulation of the movement of the flame front. Furthermore, the energy balances take into account the heat exchange between the solid and gas phases, as well as the radiation resulting from solid particles [218]. A study was conducted to investigate the diffusion of species in all reacting phases and the heat transfer in the solid phase when the flame front moves through the bed. The findings demonstrated that the transient behavior of local temperature and oxygen consumption could be modeled with a high degree of accuracy [164]. As the bed is composed of a limited number of particles, the single–particle model may be used to analyze the processes of drying, pyrolysis, gasification, and combustion. The thermal conversion occurs within each particle, which then transfers energy to neighboring particles through conduction and radiation. A one–dimensional transient model is used to model this process [219]. Other energy balance factors, such as simulating heat transfer within the gasifier inner wall and incorporating reactions for tar and methane reforming, have also been cited [214].

The literature contains various models for kinetic modeling of gasification. These models consider parameters such as gasifying agent, residence time, char particle size, pressure, temperature, moisture, and equivalence ratio [220]. Additionally, activation energies and rate constants are included to predict the composition of the syngas and the temperature under steady–state conditions in a downdraft gasifier. Although kinetic modeling has made significant progress, it overestimated the methane concentration during simulations [221]. The reduction zone is crucial for modeling downdraft gasifiers because it is where the primary gasification reactions occur. The steady–state modeling of the reduction zone in a downdraft biomass gasifier was carried out using air as the gasifying agent. In the reduction zone, gases such as pyrolysis products, CO₂, and N₂ enter from the oxidation zone, while oxygen is consumed during combustion. However, the exact quantity of each gas species is uncertain. To estimate the amount of pyrolysis gases, a pyrolysis fraction (f_p) is introduced, which varies from 0 (no pyrolysis products) to 1 (only pyrolysis products), as reported by Babu and Sheth [217]. The authors assumed a reactive process for the char particles, whereby their size decreases as they pass through the reactor. The authors assigned a pyrolysis fraction (f_p) value of 0.3. To model this, a char reactivity factor is employed. The CRF was first proposed by Giltrap et al. [221] to represent the relative reactivity of the chars when modeling downdraft gasifiers. However, when considering this value as a constant, some variations with respect to the experimental data were detected. To increase the accuracy of predictions during modeling, an alternative strategy was employed in which this parameter was modified in a constant, linear, and exponential manner. The set of kinetic equations was then coupled into a steady-state model, which includes molar balances for N₂, CO, CO₂, CH₄, H₂O, and H₂. The finite difference method was used to solve the energy balance and molar balance equations. The airflow rate was assumed to be the superficial gas velocity. The main results include the composition and temperature profiles of the producer gas. The reaction rate of the Boudouard reaction, considered in the kinetics, became negative around 933 K, indicating the reverse reaction. This caused a decrease in the LHV value due to the decrease in CO amount. By utilizing the exponential form of CRF, a better fit to the temperature profile was achieved [217].

Gasification is an autothermal process where some carbonaceous compounds are burnt to supply energy. Thus, solar energy can be used as a source by irradiating an emitter plate directly in the reactor top with highly concentrated sunlight to heat the solids. The reactor behaves as a downdraft gasifier and has higher efficiencies compared to packed bed gasifiers heated with solar energy. This gasifier model includes radiative heat transfer effects, with the chemical regime being the controlling step during modeling. Heat transfer to the walls is negligible due to the small distance between the emitter plate and the bed top and reactor bottom. The inclusion of the temperature gradient theory improves gasifier performance [215].

In another study, Gøbel et al. [216] proposed a Langmuir–Hinshelwood model to investigate the kinetic parameters of char using mixtures of H₂O/H₂ and CO₂/CO. The inhibition of char reactivity in the presence of hydrogen and carbon monoxide was also considered. To define a structural profile f(x) based on the conversion ratio of char (X), the gas composition at the bottom of the reactor was measured with low H₂O and high H₂ concentrations, which was then fitted to a polynomial function. The conversion ratio of char over time was calculated using its ash content. This was then integrated into a dynamic one–dimensional model of char in a downdraft gasifier, which takes into account the water–gas shift reaction. Char and methane were formed from the pyrolysis of beech wood chips. However, CH₄ was considered inactive during the water–gas shift reaction, as was nitrogen. The shrinking-particle model provided a better description of the conversion of char [216].

Ephraim et al. [213] modeled the kinetics of combustion, Boudouard, steam gasification, and water–gas shift reactions using char from wood pyrolysis as biomass feed. The char conversion rates for all reactions, except the water–gas shift reaction, were correlated to the partial pressure of gas species and temperature. The properties of the char, such as porosity, particle thickness, and pre–exponential factors, were directly substituted into the equations provided elsewhere [222]. For the water–gas shift reaction, the CO conversion was treated as an equilibrium reaction. The kinetic models were included in the modeling of a downdraft gasifier under steady-state operation and laminar flow regime for the gas phase. The gasifier was assumed to operate at atmospheric pressure and ideal gas conditions. Negligible heat transfer resistance was considered between the solid and gas phases, as well as heat transfer by radiation. The energy balance takes into account the heat of reaction for combustion, Boudouard, steam gasification, and water–gas shift reactions. Teixeira et al. [223] assumed that char conversion is an empirical function, as the velocity of char decreases with bed compaction due to particle size reduction by conversion or fragmentation. The bed porosity, tortuosity, and particle diameter were assumed to be constant over time. After conducting a sensitivity analysis, the authors concluded that increasing the steam flow resulted in an increase in hydrogen concentration in the producer gas, resulting in a high H₂/CO ratio. This ratio is suitable for use in turbines and solid oxide fuel cells. Additionally, temperatures above 900 °C did not significantly affect char conversion or the H₂/CO ratio. However, it is important to note that, as the amount of oxygen (air) increases, the concentration of CO in producer gas also increases, resulting in a low H₂/CO ratio that is beneficial for Fischer–Tropsch synthesis.

Gradel et al. [212] proposed a kinetic model that considers several species in both gas and solid phases. The model includes the evaporation of water during drying and the pyrolysis reaction, which is modeled using the Arrhenius equation. The parameters for the equation were obtained through thermogravimetric analysis and include char, tar, and gases such as H₂O, CO, CO₂, and CH₄. Thermogravimetric analysis was used to ascertain the kinetic parameters associated with oxidation and gasification, using steam and carbon dioxide with O₂, H₂O, and CO₂ as reactants. A power law rate equation, which incorporates pore diffusion, was derived. The equivalent diameter was used as an estimate, given that catalyst particles are cylindrical in shape. The gas phase reactions included the oxidation of hydrogen, methane, carbon monoxide, and tar, which form CO, CO₂, and CH₄ by cracking. The water–gas shift reaction was considered as an equilibrium reaction. Authors present kinetic equations for a numerical model of a downdraft gasifier that incorporates pyrolysis, oxidation, char gasification with CO₂ and steam, and internal and external mass transfer restrictions. The aim is to forecast gas composition, including the tar content. The gas phase balance considers convection, diffusion effects, and reaction terms, treating the gas mixture as ideal. Tars were analyzed in the vapor phase during pyrolysis. The solid phase was comprised of biomass and char and disregarded the ash content in the feedstock. The energy balance considered both the solid and gas phases, along with the heat flux through the reactor wall and heat losses.

Yucel and Hastaoglu [214] presented a one–dimensional transient system for a throated downdraft gasifier model. The model accounts for drying, pyrolysis, combustion, and char conversion while keeping the bed porosity constant and decreasing the particle size along the reactor. To solve the reactor model, the authors considered various mass and energy balances, including the mass conservation of the feedstock wood, moisture, char, and exit gas. The mass balance of gas species entering the gasifier was applied to O₂, CO, CO₂, H₂O, CH₄, H₂, N₂, and tars. The energy balance was applied to both the solid and gas phases, and the conservation of energy was applied to the inner wall and exit gas. The model also accounted for pressure drop.

It is to be noted that dynamic analysis provided that the gasification temperature and gas concentration were the primary factors affected by transient operation. The variables that demonstrated the greatest delay in achieving stability following air disturbances were identified. Under dynamic conditions, the airflow, which is associated with the equivalence ratio, is one of the variables with the most pronounced impact on the process.

It is evident that alterations in the gasification temperature directly influence gas production, particularly for H₂ concentration, which exhibits significant variations due to the kinetics of the water–gas shift reaction and methane reforming reaction. Carmona et al. [224] found that 1200 s were required for the hydrogen concentration to stabilize after changing the airflow. The tests focused into the process stabilization time following disturbances, as well as the ranges of the most significant variables in the steady state. No significant differences in the process were observed in relation to changes in biomass moisture content. However, a decrease in airflow was found to have a significant impact on the process’s thermochemical conditions, with the maximum temperature and biomass consumption rate being the most affected variables.

In steady–state, higher ER, and lower steam–to–biomass ratio result in higher reaction temperature, which promotes the char conversion into syngas. As the ER increased, the yield of H₂ and CO also increased, respectively. An increase in the flow rate of H₂O results in a decrease in H₂ yield if ER is reduced and more steam is fed into the gasifier, corresponding to a higher S/B ratio. Transient state demonstrates that the trend of H₂ flow rate declined followed by an increase in conjunction with rising ER, attributable to the interplay between oxidation and reduction reactions. It is important to note that the variation of H₂O is always inverse compared with H₂ during ER changes. When S/B is changed alone, the yield of H₂O is dominated by the inlet steam, regardless of whether the flow rate of H₂ increases or not. As the mass flow rate of biomass varies, it has been demonstrated that all gas species except CO are positively correlated with changes in biomass flow rate [225].

9.4. Models Based on Computational Fluid Dynamics (CFD)

Simulations are commonly useful tools for designing reactors and establishing optimal operating conditions in plants and/or reactors, such as gasifiers. CFD models can accurately predict temperature and gas yield values in the gasifier when the hydrodynamics is well understood, allowing for the development of mainly 2D and 3D models. Nevertheless, the execution of these models necessitates a considerable quantity of kinetic data and is a time–consuming process [6].

The application of CFD to biomass combustion systems encompasses a range of sub-models, as outlined by Dernbecher et al. [226]. These include: (1) the bed model, which addresses heat transfer, drying, pyrolysis, shrinking, and combustion/gasification involving the biomass. It is also necessary to consider: (2) the gas phase simulation, which includes turbulence, combustion, heat radiation, and reaction mechanisms in the gas phase; and (3) additional models for the formation of NO_x, pollutants, and particulate matter, in order to enhance the robustness of the model and to improve the accuracy of the predictions. Figure 15 presents a summary of the sub–models required for CFD modeling.

When modeling the fuel bed, biomass particles are typically treated as thermally thin to avoid temperature gradients. The particles then undergo liquid water evaporation, pyrolysis, and combustion successively. To accurately model this process, it is important to consider properties such as solid density, porosity, heat capacity, and thermal conductivity. On the other hand, if the biomass particles are large, they are modeled as thermally thick. As a result, temperature gradients are present, which take into account changes in the heat of reaction and enthalpies. If the Biot number (Bi) is less than 0.2 (Bi < 0.2), the particle is considered as thermally thin [227]. The observation is that the Biot number increases as the particle dries, and the limit of biomass particle size for thermally thin combustion is independent of the moisture content [227]. When modeling a fuel bed, it is important to consider the drying of the biomass. The moisture evaporation rate can be modeled using a kinetic approach, with the activation energy and the pre–exponential factor to be calculated for a specific biomass type. Alternatively, an equilibrium approach can be used for temperatures below the boiling point, where the liquid and vapor phases of water are in thermodynamic equilibrium [228]. After drying, the biomass particles are passed through the pyrolysis zone. For modeling purposes, the products are classified into three categories: char (solid fraction) obtained at low heating rates, liquids obtained at high heating rates, and gases, including light hydrocarbons. The number of lumps or pseudo-components varies depending on the pyrolysis pathway. For example, the biomass can be grouped into cellulose, hemicellulose, and lignin. Biomass decomposition can be assumed to occur through three competitive reactions: volatiles, char, and tar. Tar undergoes a secondary decomposition to form more char and volatiles. After passing through the pyrolysis zone, the char particles react with substoichiometric oxygen in the combustion zone to produce CO and CO₂.

The combustion of a particle in a packed bed can be characterized by the dimensionless groups, the Damköhler number, and the Thiele modulus. This is achieved by including different combustion regimes. For example, the particles can follow a reaction path limited by kinetics or transport phenomena, uniformly traversing the packed bed (behavior similar to that of a well–stirred tank). Alternatively, the conversion in the packed bed can advance despite the particles combusting, matching the reacting core or the shrinking core models [229].

The bed models employed in CFD simulations are categorized into four distinct classifications: empirical, separate bed, porous medium, and discrete bed models. Empirical models involve the gas phase that contains the fuel gas formed in different sections of the gasifier, namely drying, pyrolysis, combustion, and gasification. The composition of the gas phase is obtained from either calculations or measurements. The second approach involves the separate bed model, which considers the simulation of the bed model, including the drying, pyrolysis, and conversion of char, as well as the combustion of the gas phase in the freeboard. Different models are then obtained, such as 0D, 1D, 2D, and 3D. The zero-dimensional models enable the calculation of gas composition based on kinetics or thermodynamic equilibrium. When there are gradients in a spatial coordinate, one-dimensional models can be used to describe the process. However, when considering the length and height of the fixed bed reactor, a two–dimensional model should be used. Three-dimensional models are less commonly used and are only applicable for porous media calculations [226].

The discrete particle model (DPM) facilitates the individual tracking of each particle, whilst accounting for gravitational forces and interactions with other particles. Conversely, the porous medium model considers both phases, gas and fuel, as a continuum phase, also known as the Eulerian model. The fuel bed and freeboard are linked in terms of flow and heat transfer and are subject to radiation and turbulence [230].

In the context of conducting CFD simulations, it is imperative to consider gas phase modeling, a process that entails the incorporation of parameters such as turbulence, combustion, gas phase mechanisms, and radiation models. The selection of turbulence models is contingent on the flow regime and the geometry of the gasifier. In certain instances, the flow may not be fully turbulent, while in others, it is turbulent, particularly in gas phase reactions. The simulation of fluid turbulence can be achieved through various methodologies, including the following. First, direct numerical simulation (DNS), which does not require a model. Instead, the simulation is carried out directly, as in the case of combustion. Alternatively, the large eddy simulation (LES) can be used, as in the simplified case of combustion using two-dimensional geometries or during flame simulation. Finally, the Reynolds-averaged Navier-Stokes (RANS) equation is commonly used to average the fluctuations caused by the turbulent flow, as in furnaces or internal combustion engines [226].

During the biomass combustion, the modeling of turbulence is accomplished through the utilization of k-epsilon models. Within these models, the transport equations are satisfied by the turbulent kinetic energy (k) and the dissipation rate (epsilon) [231,232]. In summary, the turbulence models can be grouped as follows:

(1)

The standard k–ε model, which is known for its numerical stability and is used to describe the fluid flow under both reacting and non-reacting conditions, such as in chemical reactors and furnaces.

(2)

The realizable k–ε model can be modified to enhance its accuracy in simulating fluid rotation and high strain rates. This modification involves adjusting the eddy viscosity and dissipation rate parameters, which are derived from the standard model.

(3)

The renormalization group (RNG) k–ε model has been proven to be more accurate for strain and swirl flows, as derived from the Navier–Stokes equation.

(4)

The low Reynolds number k-ε model, which necessitates a highly refined grid in the proximity of the wall, a consequence of its interaction with the fluid.

Other models used for simulating fluid turbulence include the k − ω model, which is employed to simulate the near-wall region; the shear stress transport (SST) k − ω model [231] that is based on the k − ε model to simulate the core flow and the k − ω model near the wall; and the differential Reynolds stress model (RSM) used in the anisotropic turbulence of fluids [232].

As previously stated, the standard k − ε model is one of the most commonly used turbulence models in CFD due to its high accuracy in predicting turbulent flows with negligible molecular viscosity. The equation below illustrates the turbulent kinetic energy:

$${\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\mathrm{k}\right)}{\mathsf{\partial }\mathrm{t}}}+{\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\mathrm{k}{\mathrm{u}}_{\mathrm{i}}\right)}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{i}}}}={\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{j}}}}\left[\left(\mathsf{\mu }+{\displaystyle \frac{\mathsf{\rho }{\mathrm{C}}_{\mathsf{\mu }}\frac{{\mathrm{k}}^2}{\mathsf{\epsilon }}}{{\mathsf{\sigma }}_{\mathrm{k}}}}\right){\displaystyle \frac{\mathsf{\partial }\mathrm{k}}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{j}}}}\right]+{\mathrm{G}}_{\mathrm{k}}+{\mathrm{G}}_{\mathrm{b}}-\mathsf{\rho }\mathsf{\epsilon }-{\mathrm{Y}}_{\mathrm{M}}+{\mathrm{S}}_{\mathrm{k}}$$

(21)

While its dissipation rate is given by:

$${\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\mathsf{\epsilon }\right)}{\mathsf{\partial }\mathrm{t}}}+{\displaystyle \frac{\mathsf{\partial }\left(\mathsf{\rho }\mathsf{\epsilon }{\mathrm{u}}_{\mathrm{i}}\right)}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{i}}}}={\displaystyle \frac{\mathsf{\partial }}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{j}}}}\left[\left(\mathsf{\mu }+{\displaystyle \frac{\mathsf{\rho }{\mathrm{C}}_{\mathsf{\mu }}\frac{{\mathrm{k}}^2}{\mathsf{\epsilon }}}{{\mathsf{\sigma }}_{\mathsf{\epsilon }}}}\right){\displaystyle \frac{\mathsf{\partial }\mathsf{\epsilon }}{\mathsf{\partial }{\mathrm{x}}_{\mathrm{j}}}}\right]+{\mathrm{C}}_{1\mathsf{ϵ}}{\displaystyle \frac{\mathsf{\epsilon }}{\mathrm{k}}}\left({\mathrm{G}}_{\mathrm{k}}+{\mathrm{C}}_{3\mathsf{\epsilon }}{\mathrm{G}}_{\mathrm{b}}\right)-{\mathrm{C}}_{2\mathsf{\epsilon }}\mathsf{\rho }{\displaystyle \frac{{\mathsf{\epsilon }}^2}{\mathrm{k}}}+{\mathrm{S}}_{\mathrm{e}}$$

(22)

where k is the turbulent kinetic energy, ε is the dissipation rate, S_k and S_ε are user-defined source terms, G_k is the production of turbulent kinetic energy as a function of the mean velocity gradient and the Reynolds stress, G_b is the generation of turbulent kinetic energy by buoyancy, the term ρC_µk²/ε is the turbulent viscosity with C_µ to be 0.09, C_1ε, C_2ε, and C_3ε are constants, σ_k (equal to 1.0) and σ_ε (equal to 1.3) are the turbulent Prandtl numbers for k and ε, respectively, and ρ is the density of the fluid [233,234].

When considering combustion models, it is important to take into account both the reaction kinetics and the mixing of fuel and oxidizer. This leads to the development of two models: (1) the eddy break up (EBU) model, which considers turbulent premixed flames and assumes reactions occur rapidly; and (2) the eddy dissipation model (EDM), which is currently used for non–premixed flames and is based on turbulence, similar to k – ε models, to determine the kinetic energy and the dissipation rate.

During biomass combustion in the gas phase, oxidation reactions occur in the freeboard. The producer gas composition includes gaseous hydrocarbons, CO, CO₂, H₂, H₂O, and CH₄. Tar or heavier gases are modeled as benzene [235]. Hydrocarbon combustion is typically described by two– or four–step oxidation pathways, as reported elsewhere [236,237]. It is necessary to consider radiative heat transfer during combustion, as it is higher than heat transfer by convection or conduction. This is described by the radiative transfer equation and involves absorption, emission, and scattering of heat. Two models are commonly used to describe radiation: the discrete ordinate radiation model (DOM), which solves the radiative heat equation, and the P_N model, which solves spherical harmonics. The P1 model is a specific case of the P_N model but is less accurate than DOM.

To improve CFD performance, additional models can be used. These include adding reaction steps in the gas phase combustion to describe the formation of NO_x and accounting for the formation of coarse particles from the remaining ash and biomass. Furthermore, the presence of soot and inorganic particles should be considered. Additionally, pollutant emissions should be taken into account, including the decomposition of dioxins, furans, and chlorobenzene, which depend on the temperature and residence time [227,238]. The extant literature provides numerous examples of CFD modeling; see

Table 16. CFD modeling for biomass on downdraft gasifiers.

Ref.	Considerations	Main Reactions in the Model
[157]	Model type: Discrete phase model based on the Lagrangian approach, standard k − ε for turbulence. Reactor type: Imbert downdraft gasifier. Biomass source: Rubber wood, Neem. Tar is considered as benzene, naphthalene, toluene, and phenol. Main results: The formation of tar species and their residence time. Production of syngas was also modeled. Grid consisted of 90,770 cells. Reactor dimensions of 950 (mm) height and 218 (mm) diameter. Activation energy is given in (kJ mol−1).	Oxidation: (r1) 2C + O2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=\mathrm{147,000}$ and ${\mathrm{E}}_{\mathrm{a}}=112.99$ (r2) CO + ½ O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{10}$ and ${\mathrm{E}}_{\mathrm{a}}=126$ (r3) 2H2 + O2 → 2H2O with ${\mathrm{k}}_{\mathrm{o}}=2.2\times {10}^9$ and ${\mathrm{E}}_{\mathrm{a}}=109$ (r4) CH4 + 2O2 → CO2 + 2H2O with ${\mathrm{k}}_{\mathrm{o}}=4.4\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=126$ Gasification: (r5) C + CO2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r6) C + H2O → CO + H2 with ${\mathrm{k}}_{\mathrm{o}}=42.5$ and ${\mathrm{E}}_{\mathrm{a}}=142$ (r7) ½ C + H2 → ½ CH4 with ${\mathrm{k}}_{\mathrm{o}}=8.8894\times {10}^{-6}$ and ${\mathrm{E}}_{\mathrm{a}}=67.16$ (r8) CH4 + H2O → CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=3\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=125$ (r9) CO + H2O → CO2 + H2 with ${\mathrm{k}}_{\mathrm{o}}=2.35\times {10}^{10}$ and ${\mathrm{E}}_{\mathrm{a}}=288$ (r10) CO2 + H2 → CO + H2O with ${\mathrm{k}}_{\mathrm{o}}=1.785\times {10}^{12}$ and ${\mathrm{E}}_{\mathrm{a}}=326$ Tar conversion: (r11) C7H8 → 0.17C10H8 + 0.89C6H6 + 0.67H2 with ${\mathrm{k}}_{\mathrm{o}}=2.23\times {10}^{13}$ and ${\mathrm{E}}_{\mathrm{a}}=315$ (r12) C10H8 → 10C + 4H2 with ${\mathrm{k}}_{\mathrm{o}}=5.56\times {10}^{15}$ and ${\mathrm{E}}_{\mathrm{a}}=360$ (r13) C10H8 + 4H2O → C6H6 + 4CO +5H2 with ${\mathrm{k}}_{\mathrm{o}}=1.58\times {10}^{12}$ and ${\mathrm{E}}_{\mathrm{a}}=324$ (r14) C7H8 + H2 → C6H6 + CH4 with ${\mathrm{k}}_{\mathrm{o}}=1.04\times {10}^{12}$ and ${\mathrm{E}}_{\mathrm{a}}=247$ (r15) C6H6 + 5H2O → 5CO + CH4 + 6H2 with ${\mathrm{k}}_{\mathrm{o}}=4.4\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=220$ (r16) C6H6 + 7.5O2 → 6CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=17.83$ and ${\mathrm{E}}_{\mathrm{a}}=125.5$ (r17) C7H8 + 3O2 → 6CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=1.58\times {10}^{15}$ and ${\mathrm{E}}_{\mathrm{a}}=202.6$ (r18) C7H8 + 9O2 → 7CO2 + 4H2O with ${\mathrm{k}}_{\mathrm{o}}=14.26$ and ${\mathrm{E}}_{\mathrm{a}}=125.5$ (r19) C6H6O → CO + 0.4C10H8 + 0.15C6H6 + 0.1CH4 + 0.75H2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^7$ and ${\mathrm{E}}_{\mathrm{a}}=100$
[234]	Model type: 2D steady-state, discrete phase model based on the Euler–Lagrangian approach, P1 radiation model, standard k − ε for turbulence. Reactor type: Imbert downdraft gasifier. Biomass source: biomass pellets. Char is carbon. Main results: Parametric study of the effect of equivalence ratio in temperature, gas production rate. Cold gas efficiency of 71.8% at ER of 0.25. Grid consisted of 88,642 cells. Reactor dimensions of 1083 (mm) height and 435 (mm) diameter. Activation energy is given in (kJ mol−1).	(r1) CcHhOoNn → x1CO + x2H2 + x3CH4 + x4CO2 + x5H2O + x6N2 (r2) 2C + O2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=\mathrm{147,000}$ and ${\mathrm{E}}_{\mathrm{a}}=112.99$ (r3) CO + ½O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=2.239\times {10}^{12}$ and ${\mathrm{E}}_{\mathrm{a}}=170$ (r4) 2H2 + O2 → 2H2O with ${\mathrm{k}}_{\mathrm{o}}=9.87\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=31$ (r5) CH4 + 1.5O2 → CO + 2H2O with ${\mathrm{k}}_{\mathrm{o}}=5.012\times {10}^{11}$and ${\mathrm{E}}_{\mathrm{a}}=200$ (r6) C + CO2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r7) C + H2O → CO + H2 with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r8) C + 2H2 → CH4 with ${\mathrm{k}}_{\mathrm{o}}=8.8894\times {10}^{-6}$ and ${\mathrm{E}}_{\mathrm{a}}=67.16$ (r9) CH4 + H2O → CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=5.922\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=209$ (r10) CO + H2O → CO2 + H2 with ${\mathrm{k}}_{\mathrm{o}}=2.35\times {10}^{10}$ and ${\mathrm{E}}_{\mathrm{a}}=288$ (r11) CO2 + H2 → CO + H2O with ${\mathrm{k}}_{\mathrm{o}}=1.785\times {10}^{12}$ and ${\mathrm{E}}_{\mathrm{a}}=326$
[239]	Model type: 3D, P1 radiation model, realizable k − ε model for turbulence. Reactor type: Downdraft gasifier. Biomass source: Miscanthus briquettes. Main results: Temperature profile, syngas composition, and LHV. High predictability for different feedstocks. Grid consisted of 383,031 cells. Reactor dimensions of 900 (mm) height and 500 (mm) diameter.	Oxidation: (r1) C + ½ O2 → CO (r2) C + O2 → CO2 Gasification: (r3) C + CO2 → 2CO (r4) C + H2O → CO + H2 (r5) C + 2H2 → CH4 Gas-phase reactions: (r6) CO + ½ O2 → CO2 (r7) H2 + ½ O2 → H2O (r8) CH4 + H2O → CO + 3H2 (r9) CO + H2O → CO2 + H2
[240]	Model type: 2D, porous media model to represent the packed bed, RNG k − ε model for turbulence. Reactor type: Imbert downdraft gasifier. Biomass source: Olive wood. Tar is considered as phenol, naphthalene, and benzene, while char is carbon. Main results: The influence of ratio of throat to gasifier diameters and the height to air nozzle from the throat on the tar concentration and cold gas efficiency was studied. Grid consisted of 20,000 cells. Reactor dimensions of 1370 (mm) height and 600 (mm) diameter. Activation energy is given in (kJ mol−1).	Pyrolysis including tar cracking: (r1) CcHhOo → x1CO + x2CO2 +x3H2 + x4CH4 + x5H2O + x6C6H6O + x7C10H8 + x8C6H6 with ${\mathrm{k}}_{\mathrm{o}}=2.119\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=202.7$ (r2) C6H6O → CO + 0.4C10H8 +0.15C6H6 + 0.1CH4 + 0.75H2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^4$ and ${\mathrm{E}}_{\mathrm{a}}=100$ (r3) C10H8 → 7.38C + 0.275C6H6 + 0.97CH4 + 2.235H2 with ${\mathrm{k}}_{\mathrm{o}}=3.39\times {10}^{14}$ and ${\mathrm{E}}_{\mathrm{a}}=350$ Oxidation: (r4) CH4 + 1.5O2 → CO + 2H2O with ${\mathrm{k}}_{\mathrm{o}}=5.012\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=200$ (r5) CO + ½O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=4.4\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r6) H2 + ½O2 → H2O with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{14}$ and ${\mathrm{E}}_{\mathrm{a}}=42$ (r7) H2O → H2 + ½ O2 with ${\mathrm{k}}_{\mathrm{o}}=2.06\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=272.8$ (r8) 2C + O2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=1.47\times {10}^5$ and ${\mathrm{E}}_{\mathrm{a}}=113$ (r9) C + O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=5.67\times {10}^9$ and ${\mathrm{E}}_{\mathrm{a}}=160$ (r10) C6H6O + 4O2 → 6CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=2.4\times {10}^{11}\mathrm{T}$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r11) C6H6 + 4.5O2 → 6CO + 3H2O with ${\mathrm{k}}_{\mathrm{o}}=3.8\times {10}^7$ and ${\mathrm{E}}_{\mathrm{a}}=5.545$ (r12) C10H8 + 7O2 → 10CO + 4H2O with ${\mathrm{k}}_{\mathrm{o}}=9.2\times {10}^6\mathrm{T}$ and ${\mathrm{E}}_{\mathrm{a}}=80$ Reduction: (r13) CO + H2O → CO2 + H2 with ${\mathrm{k}}_{\mathrm{o}}=2.78$ and ${\mathrm{E}}_{\mathrm{a}}=12.6$ (r14) CH4 + H2O → CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=3.015\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=125.52$ (r15) C + CO2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r16) C+ H2O → CO + H2 with ${\mathrm{k}}_{\mathrm{o}}=8.268$ and ${\mathrm{E}}_{\mathrm{a}}=188.2$ (r17) C + 2H2 → CH4 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=42$ (r18) C6H6O + 3H2O → 2CO + CO2 + 2.95CH4 + 0.05C + 0.1H2 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^5$ and ${\mathrm{E}}_{\mathrm{a}}=100$ (r19) C6H6 + 2H2O → 1.5C +2.5CH4 + 2CO with ${\mathrm{k}}_{\mathrm{o}}=3.39\times {10}^{16}$ and ${\mathrm{E}}_{\mathrm{a}}=443$
[241]	Model type: 2D, porous media model to represent the packed bed, RNG k − ε model for turbulence. Reactor type: Imbert downdraft gasifier. Biomass source: Oil palm residues. Tar is considered as phenol, hydroxyacetaldehyde, naphthalene, and benzene, while char is carbon. Main results: The effect of the equivalence ratio on the gas composition, tar concentration, and cold gas efficiency. Grid consisted of 20,000 cells. Reactor dimensions of 1370 (mm) height and 600 (mm) diameter.	Set of reactions as reported by [163]
[234]	Model type: 3D, Eulerian-Eulerian approach, standard k − ε model for turbulence Reactor: throat downdraft gasifier Biomass source: wood considered as C6H10.5O5N0.05 Main results: The ratio of diameters of throat-to-gasifier of 0.4, and the air inlet nozzles at 100 (mm) above the throat to give 31.2 mol% of H2 and H2/CO ratio of 1.25. Reactor dimensions of 550 (mm) height and 200 (mm) diameter. Activation energy given in (kJ mol−1).	Pyrolysis: (r1) CcHhOoNn → x1CO + x2CO2 + x3CH4 + x4H2 + x5Char + x6Ash with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=140$ Oxidation: (r2) C + O2 → CO2 with ${\mathrm{k}}_{\mathrm{o}}=5.67\times {10}^9$ and ${\mathrm{E}}_{\mathrm{a}}=160$ (r3) C + ½ O2 → CO with ${\mathrm{k}}_{\mathrm{o}}=7.92\times {10}^4$ and ${\mathrm{E}}_{\mathrm{a}}=218$ (r4) H2 + ½ O2 → H2O with ${\mathrm{k}}_{\mathrm{o}}=3.53\times {10}^8$ and ${\mathrm{E}}_{\mathrm{a}}=30.5$ Reduction: (r5) C + CO2 → 2CO with ${\mathrm{k}}_{\mathrm{o}}=589$ and ${\mathrm{E}}_{\mathrm{a}}=223$ (r6) C + H2O → CO + H2 with ${\mathrm{k}}_{\mathrm{o}}=5.71$ and ${\mathrm{E}}_{\mathrm{a}}=65.8$ (r7) C + 2H2 → CH4 with ${\mathrm{k}}_{\mathrm{o}}=1\times {10}^{11}$ and ${\mathrm{E}}_{\mathrm{a}}=42$ (r8) CH4 + H2O → CO + 3H2 with ${\mathrm{k}}_{\mathrm{o}}=73$ and ${\mathrm{E}}_{\mathrm{a}}=36.2$ (r9) CO + H2O → CO2 + H2 with ${\mathrm{k}}_{\mathrm{o}}=0.03$ and ${\mathrm{E}}_{\mathrm{a}}=65.8$

for a summary of recent models.

Yepes Maya et al. [239] reported a model capable of simulating various types of biomasses based on ultimate and proximate analyzes. The non–premixed combustion was simulated by injecting the biomass and gasification agent through different inlets into the gasifier. The model predicted the gasification products and their concentrations under different reaction conditions. Furthermore, the compositional analysis allows for simulation of various feedstocks, providing valuable insights for gasifier design and optimization.

Authors have different point of views on the reactions that occur in the gasifier when using CFD simulation. Some suggest that all reactions occur simultaneously, while others define detailed reactions that occur sequentially through each zone of the gasifier, namely pyrolysis, oxidation, and reduction. It is important to consider the formation of tar and its simulation. Only a few reports consider tar modeling to improve simulations. Tar is often modeled as aromatic compounds, such as benzene and polycyclic aromatic hydrocarbons, to increase result reliability. Salem and Paul [157] included various reactions to model tar formation using CFD in an Imbert downdraft gasifier with the assumption that benzene, naphthalene, phenol, and toluene, would be tar constituents. Of all the species mentioned, benzene had the highest concentration, followed by naphthalene, and small amounts of toluene and phenol. During pyrolysis, tars are formed and subsequently consumed in the oxidation and reduction zones. The authors observed a low formation of tars during pyrolysis, which were then consumed in the oxidation zone and reformed in the reduction zone. The residence time for tar species passing was calculated using the discrete phase model for particle tracking. Pandey et al. [233] reported a parametric analysis on the effect of equivalence ratio on syngas production and temperature profile in the gasifier. The simulation used standard models for k–ε, energy, species transport, and discrete phase. The temperature inside the gasifier increased as the equivalence ratio augmented, while the levels of CO and H₂ decreased due to rapid oxidation. Furthermore, an equivalence ratio of 0.25 resulted in higher cold gas efficiency.

The use of CFD allows for the analysis of design parameters in gasifiers, such as the throat–to–gasifier diameter ratio, or the study of the behavior of an Imbert downdraft gasifier, considering the height of the air nozzle from the throat. Ngamsidhiphongsa et al. [240] constructed a model of an Imbert downdraft gasifier, incorporating an equivalent particle diameter for biomass of 0.01 (m) and a bed porosity of 0.32. The model comprised distinct sections designated for drying, pyrolysis, oxidation, and reduction, with the formation of tar (phenol, naphthalene, and benzene) being simulated. The authors provide a detailed description of the reactions occurring in the gasifier. It is noted that reducing the nozzle/throat dimensions decreases the tar and hydrogen content, as shown by the CGE. In another report [241], a simulation was conducted on an Imbert downdraft gasifier using pelletized palm oil empty fruit bunches. The study focused on the impact of equivalence ratio, tar concentration, and cold gas efficiency, with optimized values of 0.3, 5 (mg Nm⁻³), and 62%, respectively. The model robustness enables its application to other feedstocks, such as bagasse or wood. Prasertcharoensuk et al. [234] used the modified Eulerian–Eulerian approach in CFD to simulate gasification with throat-to-gasifier diameters ranging from 0.25 to 0.50. The simulations maintained a constant equivalence ratio of 0.25. The reactor was divided into three zones: pyrolysis, oxidation, and reduction. The biomass (wood) was assumed to be fed at a rate of 1 (kg h⁻¹) from the upper part of the gasifier at 400 K, while preheated air at 350 K was used as the gasifying agent. The diameter of the throat and the position of the air inlet nozzles influenced the gas properties and temperature profiles.

9.5. Other Programs/Techniques Used for Simulation of Biomass Gasification

The utilization of process simulators facilitates the implementation of thermodynamic equilibrium and kinetic models, enabling comprehensive investigation of each stage within the gasification process. In the majority of cases, these simulators are sequential, modular, and equation–oriented, based on mass and energy balances and thermodynamic equilibrium [242]. A significant benefit of this approach is the incorporation of sub-models as coded libraries, which substantially enhances the efficacy of the simulations. The incorporation of sub–models as coded libraries is an advantage of this approach due to encompassing the tar formation and its subsequent cracking kinetics. It has been demonstrated that this factor results in a substantial enhancement in performance [243].

One study that incorporated tar in their simulation was reported elsewhere [244]. This research divided the simulation model into four sub–models: drying, devolatilization, tar cracking, and gasification. The tar yield decreases as the steam–to–biomass ratio increases. Tar cracking was assumed to follow the Gibbs equilibrium principle, and the simulated tar species were benzene, toluene, phenol, and naphthalene. Hu et al. [245] reported that the tar yield decreased slightly with the addition of steam. The accuracy of the syngas composition improved when tar was added to the simulations, taking into account model compounds such as benzene, phenol, and naphthalene, and including empirical correlations in a sub–model to represent tar formation [246]. In some cases, phenol and pyrene were used as model compounds for tar, and their yields were determined using the thermodynamic equilibrium approach [247].

A model of the downdraft biomass gasification process was based on minimizing the Gibbs free energy while restricting the chemical reaction equilibrium in the gasification reduction zone and using hardwood chips. After successful model validation, sensitivity analysis was performed to study the impact of temperature, equivalence ratio, and biomass moisture on the syngas composition [248]. In a separate study, the Gibbs free energy minimization method was used to predict the producer gas from various biomass feedstocks. The use of food waste as a feedstock resulted in a H₂/CO molar ratio of approximately 2.15, which is suitable for feeding into the Fischer–Tropsch reactor [249]. Simulations have also been used to study the impact of substituting steam with CO₂ on the gasification efficiency, and the results indicate that there is no significant effect [250]. The use of CO₂ as a gasifying agent at low temperature and high pressure has a positive influence on the efficiency of the gasification system and the total CO₂ emission per syngas production [251]. However, at low pressure, the use of CO₂ has only a marginal effect on both parameters [251]. Renganathan et al. [93] reported on the impact of gasifying agents, including CO₂, CO₂ plus steam, and CO₂ plus O₂, on syngas composition and cold gas efficiency.

A study was conducted by simulating a downdraft fixed bed reactor for the co–production of biochar and syngas through gasification. The study found that the yield of both syngas and biochar is affected by the temperature and type of gasifying agent used [59]. The validation of the simulation model using different biomass sources is a significant research area. The simulator can use a variety of feedstocks, such as conventional biomass and microalgae (Nannochloropsis oculata), depending on their composition. This makes them appropriate subjects for the gasification study [252].

Process simulators can rapidly evaluate various parameters, a feature that is especially useful in providing theoretical support for industrial facilities. However, several limitations arise during its use, which include the assumptions made in order to simplify the process, such as isothermal operation and homogeneous temperature profiles, steady-state behavior, a lack of an adequate sub–model to represent tar formation and cracking, and the consideration of model compounds to represent tar [253]. Further research is needed to improve the simulation results by developing better sub–models that represent the tar compounds and their cracking reactions.

In order to enhance the efficiency of the process, it is recommended that the gasification module of biomass be isolated, thereby reducing the time of execution and the amount of computing resources required. The development of a self–contained biomass gasification model that is both fully automated and open source has the potential to assist researchers in conducting simulations to identify optimal operating conditions for maximizing gas yield. In this sense, the utilization of open–source software for the modeling of the gasification process is reported [254]. The authors provided a code designed to model and simulate the gasification of biomass, with the objective of producing syngas products, including hydrogen, carbon monoxide, carbon dioxide, and water/steam. The application under discussion is a Python version 3.13.6 program (available on https://www.python.org/) that is stored in a Jupyter Notebook (version 7.3.2) file (available on https://docs.jupyter.org/en/latest/). The application under discussion is intended to function as a comprehensive, self–contained solution, thereby obviating the necessity for any third–party libraries apart from those which are standard for Python, namely the scientific and mathematical packages NumPy version 2.3.0 (highly efficient and versatile N–dimensional array package for the solving matrices; available on https://numpy.org/), Pandas version 2.3.1 (it has the capacity to import XLS, CSV and tab–separated value files (TSV) into a table; available on https://pandas.pydata.org/), SciPy version 1.16.0 (which includes linear and nonlinear algebra, integral calculus, differential equations, and optimization; available on https://scipy.org/), and Matplotlib version 3.10.0 for the purpose of visualization (a Python-based software that facilitates the creation of visualizations in a variety of forms, including static, animated and interactive formats; available on https://matplotlib.org/).

An open–source software for computational fluid dynamics can be also used for gasification simulations. Such a program contains a suite of applications used for performing calculations of fluid dynamics with applications that include configuration of simulations, manipulation of geometry, generation of computational meshes, processing, and visualization of results [255].

When provided with a sufficient dataset, the artificial neural networks (ANN) can be employed for process modeling. These networks possess the capacity to discern correlations between variables of the system (inputs). In circumstances where direct knowledge of the system behavior is unavailable, outputs are obtained with agreement with respect to the real system [6]. The architecture of the ANN is composed of three distinct layers: the input, hidden, and output layers. The input signals are transported from the input layer to the output layer via the hidden layer using activation functions, weights, and bias vectors. The number of neurons in the hidden layer is determined by a trial–and–error method, which has a substantial impact on the performance of the ANN model [256]. This approach requires substantial volumes of data and highly accurate models for training, which may not be readily available. However, following the training phase, the computational requirements are minimal, a factor that is considered advantageous [6].

The utilization of a specific program is contingent upon the selection of the researcher, the computational infrastructure, the program capabilities, the implemented libraries, or routines developed by the researcher, the availability of data or correlations, and licenses, among other factors.

10. Summary and Perspectives

Thermochemical conversion of biomass, including lignocellulosics, municipal solid waste and sewage sludge, is an alternative means of obtaining syngas from the aforementioned feedstock. This syngas can then be converted into energy, heat, fuels or chemical products.

Biomass sources, such as wood and sawdust, husks, stalks, bagasse, and straw, are composed of cellulose, hemicellulose, and lignin. While the availability of diverse biomass sources appears to be economically viable, it is imperative to evaluate the associated costs of transporting, drying, and grinding the feed to a particle size suitable for processing in a dedicated gasifier. Consequently, all energy costs associated with biomass conditioning must be taken into account to optimize and ensure the viability of syngas production from lignocellulosic residues. It has also been documented that the utilization of sewage sludge or municipal solid waste is employed; nevertheless, the expense associated with conditioning the feedstock may prove to be prohibitive at an industrial level.

The reactors employed in gasification to process biomass are primarily of three distinct types: fixed bed, fluidized bed, and entrainment flow reactors. The construction of fixed–bed reactors is relatively straightforward; however, they exhibit a low thermal efficiency. In the specific instance of a downdraft reactor, the amount of tar formed is minimal. Fluidized bed reactors are predominantly employed for the processing of large batches; however, the occurrence of carbon carry–over is a salient issue, particularly in the context of circulating fluidized bed reactors. Entrained flow reactors, conversely, are characterized by their high operating temperature, which results in a very high conversion rate. It is also important to note that the fixed bed gasifiers have the lowest CapEx values compared to fluidized bed and entrained flow gasifiers, but the fixed bed and entrained flow gasifiers have lower OpEx values. However, it is acknowledged that these costs are subject to variation depending on the characteristics of the biomass, the utilization of the syngas, and the installed capacity of the gasification plant. Commonly, the gasifying agent used in reactors are air, oxygen, or steam. The utilization of air in gasifiers has been demonstrated to result in a decline in LHV of the syngas, attributable to the presence of nitrogen. In addition, the utilization of oxygen and steam has been observed to enhance the heating value of the syngas, although this is accompanied by increased operational costs. The equivalence ratio, when utilizing air, currently ranges from 0.2 to 0.4.

Nevertheless, the process of power generation from biomass gasification gives rise to a number of significant health and environmental concerns. Tar is understood to consist of a significant proportion of highly toxic and carcinogenic aromatic hydrocarbons, which must be removed or, if possible, avoided inside the gasifier. The tar mitigation involves the adjustment of the operation conditions in the gasifier, such as pressure, temperature, gas residence time, and gasifying agent. Depending on the configuration of the gasifier, the use of in situ catalysts, including naturally occurring ones such as dolomite and olivine, as well as metal catalysts, can be recommended. Furthermore, the development of novel reactor designs or improvements can play a pivotal role in this regard, encompassing the enhancement of the air supply system, the feeding and recirculation of biomass. In instances where mitigation of tar formation is not feasible, secondary methods involving physical and chemical treatments emerge as effective alternatives.

Once the producer gas is cleaned up, it can be used in a variety of ways, although it must first be conditioned to adjust its pressure to specific values, or to maintain a specific H₂/CO ratio, i.e., in the case of synthetic natural gas production. The Fischer–Tropsch reaction can be used to produce commercially important fuels and chemicals, such as methanol, which can then be converted to gasoline or diesel by oligomerization in the methanol–to–diesel process, or to olefins in a process called methanol–to–olefins, which produces mainly propylene and ethylene. Synthesis gas has the potential to be utilized as a fuel source, with applications including the production of thermal and electric power. Additionally, it can be employed in fuel cells, further expanding its functional scope.

Regarding the biomass gasification modeling and/or simulation, different models can be employed to meet this aim. Equilibrium models calculate the maximum yield possible under equilibrium conditions. However, it should be noted that these conditions may deviate considerably from the actual conditions within a gasifier. Conversely, kinetic modeling may be more appropriate when a more precise model is required, which can be coupled to the reactor model that involves conservation equations to acquire the yields and composition of producer gas, tar, and char at a particular operating state (steady or transient state). In instances where a comprehensive understanding of reactor hydrodynamics is available, CFD models have been demonstrated to be highly accurate in predicting temperature and gas yield from the reactor. However, it should be noted that CFD simulation of the gasification process necessitates a substantial amount of kinetic and design data. Additionally, these models are computationally expensive.

Recent techniques employing artificial intelligence algorithms have emerged as a promising field of research, offering a range of possibilities for predicting syngas quality and composition. The employment of artificial intelligence–based neural networks, such as the ANN and the convolutional neural network (CNN), in conjunction with regression algorithms, including extreme gradient boosting (XGBoost), gradient boosting for regression, random forest regression, among others, is a promising avenue for the near future. These algorithms have the potential to facilitate the prediction of syngas composition with a high degree of accuracy.

It is therefore evident that the present review disclosed a range of key elements pertaining to gasification, encompassing the characteristics and properties of biomass, the types of reactors employed, gasifying agents, syngas cleaning techniques, prevalent syngas applications, the economic viability of gasification processes, and modeling approaches through various methodologies, including different reactor models for a downdraft gasifier.

11. Concluding Remarks

Biomass processing represents a viable alternative means of generating energy, heat, fuels, or chemicals from lignocellulosic, municipal solid waste, and sewage sludge. A potential avenue for its utilization is through the implementation of thermochemical processes, such as gasification. This process entails the exposure of biomass to a gasifying agent (air, oxygen, or steam) in a proportion that facilitates partial combustion, resulting in the generation of a gaseous product comprising essential components, such as CO and H₂, commonly referred to as synthesis gas, syngas, or producer gas.

With regard to reactors, the fixed–bed gasifier produces a lower amount of syngas compared to both the fluidized-bed gasifier and the entrained flow gasifier. Notably, the downdraft gasifier generates minimal tar. Entrained flow gasifiers are capable of producing higher volumes of syngas; however, they are typically used for coke production. Nevertheless, coprocessing with biomass has been shown to be an effective strategy for increasing the syngas volume obtained during gasification. Downdraft gasifiers are typically employed in small and medium–sized capacities, with the syngas primarily utilized in combined heat and power units. At present, some processes involving the use of syngas in CHP units are operating at TRL 9 in certain countries.

The selection of the gasifying agent has a direct impact on the syngas composition. Air is the most commonly used gas in the process of gasification, despite the fact that steam increases the yield of hydrogen in the producer gas. In the latter case, the steam generators are necessary, and their capital and operational expenditures need to be added to the total investment. The equivalence ratio currently ranges between 0.2 and 0.4. Oxygen can also function as a gasifying agent; however, it should be noted that this can result in an increase in gasification costs. Despite its potential to utilize the carbon dioxide generated from several processes, the use of CO₂ as a gasifying agent has not yet been fully addressed. However, it should be noted that higher gasification temperatures and catalysts are required when utilizing CO₂ as a gasifying agent. The economics associated with CO₂ as a gasifying medium are dependent on its availability.

The nature of the biomass is crucial due to its composition and moisture influence on the syngas composition. In addition, the size of the particles is a key factor in determining the gasifier type to be used and/or the level of biomass grindability and, consequently, the associated cost. Torrefaction has the potential to enhance the energy density of biomass; however, it concomitantly increases the cost of gasification. The process economics would be improved by using a multi–generation process involving torrefaction, gasification, and power generation.

Despite the progress achieved in kinetic and reactor modeling related to gasification, these models are unable to fully describe the reactions occurring in the gasifier in some cases. Furthermore, the process of modeling invariably entails a certain degree of challenge, due to the necessity of making certain assumptions and simplifications. The latest methods based on artificial intelligence (AI) are very promising in terms of addressing this inaccuracy. Machine learning is a field of AI that encompasses a range of methodologies, including neural networks, support vector machines, and decision trees, among others. These methods have been shown to achieve high levels of accuracy with minimal errors in calculations. The bibliometric analysis completed in Section 9.1 indicates that AI methods are a developing tool for gasification modeling, with the expectation that further literature will be generated based on them in the coming years.