Comparative and Descriptive Study of Biomass Gasification Simulations Using Aspen Plus

Abstract

Biomass gasification has emerged as a promising method for producing renewable energy, addressing both energy and environmental challenges. This review examines recent research on gasification simulations, covering a range of topics from process modeling to syngas cleanup. Key areas explored include techniques for syngas cleaning, addressing tar formation, and CO₂ capture methods. The aim of this review is to provide an overview of the current state of gasification simulation and identify potential areas for future research and development. This work serves as an invaluable resource for researchers, engineers, and industry professionals involved in biomass gasification modeling. By providing a comprehensive guide to biomass gasification simulation using Aspen Plus software and comparing various modeling approaches, it assists users in selecting the most effective tool for optimizing the design and operation of gasification systems.

1. Introduction

The continuous increase in fossil fuel consumption, along with the negative environmental impacts associated with this, has led to a significant rise in research and development efforts for renewable energy sources, as noted by Mariyam et al. [1]. According to the definition provided by U.S. Energy Information Administration (EIA), renewable energies encompass energy sources that renew naturally. They are considered to be practically inexhaustible in terms of duration, although their availability is limited per unit of time. Among the main categories of renewable energy sources are biomass, geothermal, hydroelectric, wind, and solar energy, according to [2].

In its 2021 report, the International Energy Agency (IEA) forecasted and evaluated the deployment of renewable energies in the fields of electricity, transportation, and heating up to 2026. The projections suggest an increase in the adoption of new biomass processing technologies specifically designed for electricity production [3].

Figure 1 shows that renewable energy sources accounted for 12% of global energy consumption. Biomass stood out as the most utilized renewable energy source, contributing 39% to the total consumption. Wind energy represented 26%, hydroelectric energy 20%, solar energy 11%, and geothermal energy 2%, as reported in [4]. One of the main advantages of using biomass for energy production is the significant reduction in greenhouse gas emissions compared to conventional fossil fuel-based methods, as stated by Mohtasham et al. [5].

The U.S. Energy Information Administration [6] emphasizes that biomass can be transformed into energy through various means. These include biological conversion for the production of liquid and gaseous fuels, chemical conversion for generating liquid fuels, and thermochemical conversion for producing solid, liquid, and gaseous fuels, as well as direct combustion for heat generation.

Thermochemical conversion of biomass encompasses methods such as pyrolysis, liquefaction, combustion, and gasification [7,8].

The biomass gasification process involves a series of complex chemical processes that transform solid fuel into gases, water, char, and tar, as reported by Sreejith et al. [9]. Biomass gasification has the potential to produce a fuel gas that can be used for power generation systems or synthesis gas applications, according to Tasaka et al. [10]. Gasification is one of the most promising technologies for electricity generation, as mentioned in [11,12].

Objectives

This study is based on a comparative analysis of the simulation tools for biomass gasification in Aspen Plus. The main objective is to provide a detailed review of how previous works have been simulated using this software, as well as to identify areas for improvement to optimize the biomass gasification process. Additionally, this study explores the limitations of different simulation approaches and addresses two key issues affecting these processes: tar removal, and carbon dioxide capture.

Despite the numerous works that have been carried out in the field of gasification process simulation, the pertinent question is whether these methods are reliable enough and representative of the experimental results. We have highlighted that many authors consider thermodynamic equilibrium without considering the byproducts found when experiments are carried out.

The interest in conducting simulations involving syngas cleaning lies in the significant impact of emissions on the environment. As for tar, it refers to the condensable components of higher hydrocarbons, leaving residues that can damage installation pipes, as mentioned by Jayanarasimhan et al. [13]. This problem represents one of the main obstacles hindering the commercialization of biomass power generation, as reported by Chan et al. [14]. In the case of CO₂, it has negative effects by contributing to global warming and climate change, as highlighted by Dattatray et al. [15].

Simulation is an invaluable tool that facilitates process evaluation, with the advantages of reducing material usage and saving time. Additionally, these models play a fundamental role in the development of new designs and in accurately estimating operational variables, according to Shamsi et al. [16]. Aspen Plus is a commercial software platform that provides integrated functions for modeling the different stages of the gasification process, as mentioned by Shahabuddin et al. [17].

2. Different Approaches to Biomass Gasification Simulation

According to Rabea et al. [18], the two main approaches for performing gasification simulations are thermodynamic equilibrium modeling and kinetic modeling.

2.1. Thermodynamic Equilibrium Modeling

Thermodynamic equilibrium describes a condition where the rates of forward and reverse reactions are equal, resulting in no overall change in the system’s composition over time, as mentioned by Dincer et al. [19].

The simulations that have been carried out considering the thermodynamic equilibrium model are idealistic and fail to account for the physical and chemical processes, as noted by Abdelouahed et al. [20]. Due to the significant deviations of many thermodynamic equilibrium models from real experimental data, it is not advisable to rely on this model, particularly when gasification takes place at low temperatures, as indicated by Beheshti et al. [21].

2.2. Kinetic Modeling

Chemical kinetics focuses on examining how chemical compounds change from reactants to products at different rates [22]. The kinetic simulation of biomass gasification focuses on predicting the composition of the syngas by considering the reaction rates within a specific residence time or volume, as described by Rabea et al. [18].

3. Parameters Evaluated in the Different Simulations of Biomass Gasification

The research on biomass gasification has been extensively explored in the scientific literature, with numerous studies dedicated to analyzing various crucial parameters that impact this process.

Table 1. Parameter and software analysis in biomass gasification.

Parameter	Software *	References	Conclusions
Temperature	Aspen Plus	[9,23,24,25,26,27,28,29,30,31]	With the increase in temperature, a decrease in the composition of CH4 and CO2 was observed, while the production of H2 and CO increased until reaching a specific maximum temperature, after which some of these authors noticed a slight decrease.
	Aspen HYSYS	[32,33,34]
	IPSEpro	[35,36]
	MATLAB	[37]
	Aspen Plus	[9]	When the temperature increases, the LHV decreases (427–627 °C). Afterwards, an increase is observed (727–1227 °C).
	IPSEpro	[36]	When the temperature increases, the LHV decreases (700–900 °C).
	Aspen Plus	[38,39]	When the temperature increases, the LHV and HHV increase (650–750 °C). After this temperature, a decrease is observed (750–950 °C).
Pressure (not as influential as temperature)	Aspen Plus	[9,40,41,42]	Increasing pressure enhances the generation of CH4 and CO2, while the concentrations of CO and H2 decrease (0–30 atm).
	Aspen Plus	[41]	As pressure increases, calorific value increases HHV (1–20 atm).
	Aspen Plus	[9]	The energy efficiency and exergy efficiency decrease marginally with pressure.
Equivalence ratio (ER)	Aspen Plus	[25,26,30,31,42,43,44]	As ER increases, CO2 production increases, while H2 and CO production decreases (0.1–1).
	Aspen Plus	[39,43]	An increase in ER leads to a decrease in the LHV of the synthetic gas (0.18–0.38).
Steam/biomass ratio (S/B)	Aspen HYSYS	[32,34]	When S/B is increased, the concentrations of H2 and CO2 increase, while the concentration of CO decreases (0.1–2.5).
	IPSEpro	[35,36]
	Numerical simulation/Cantera	[45]
	Aspen Plus	([9,25,26,27,46] used Aspen and MATLAB)
	Aspen Plus	[9,25,26,27,46] ([47] used Aspen and MATLAB)	The LHV value decreases with the increase in the steam-to-biomass ratio (0.2–5).
	IPSEpro	[36]
Percentage of oxygen (OP)	Aspen Plus	[11]	An increase in the percentage of oxygen enhances the quality of the synthetic gas and the efficiency of gasification.
Biomass moisture	IPSEpro	[36]	An increase in the moisture content of the biomass leads to a decrease in the cold gas efficiency for both gasification and the entire process.

* To see the versions, please refer to the Supplementary Material (Table S3).

provides a summary of selected studies, highlighting the parameters of interest, the software used for modeling, relevant references, and key conclusions drawn from the research. Through this compilation, a comprehensive overview is provided of how different factors, such as temperature, pressure, and steam/biomass ratio, among others, influence biomass gasification and the quality of the synthetic gas produced.

The results compiled in Table 1 analyze the impact of various parameters on the composition of syngas using simulations. These parameters include temperature, pressure, equivalence ratio, steam/biomass ratio, oxygen percentage, and biomass moisture. Temperature is one of the most influential factors, affecting the production of CH₄, CO₂, H₂, and CO. Pressure has a smaller effect but generally increases CH₄ and CO₂ production. The equivalence ratio and steam/biomass ratio also affect the syngas composition. The oxygen percentage improves the quality and efficiency of gasification, while biomass moisture reduces efficiency. Overall, simulation is a valuable tool for studying syngas production.

3.1. Parameters That Affect the Biomass Gasification

3.1.1. Type of Biomass

According to Ibitoye et al. [48], the most commonly used biomass is wood, accounting for 67%, compared to other types of biomass: agricultural residues (4%), energy crops (3%), municipal solid waste (3%), wood industry residues (5%), forest residues (1%), and charcoal (7%).

Sharma [49] compared various feedstocks, including pine, alfalfa, hardwood, corn, cypress mulch, and bark nuggets. Pine pellets yielded a gas composition of approximately 18.26 ± 1.59% H₂ and 18.02 ± 2.92% CO.

González-Vázquez et al. [50] conducted a study on the effects of different feedstocks on gasification, including softwood (pine sawdust—PIN), hardwood (chestnut sawdust—CHE), torrefied softwood (PINT), torrefied hardwood (CHET), almond shells (AS), cocoa shells (CS), grape pomace (GP), olive pits (OS), pine nut shells (PKS), and pineapple leaves (PCL). Compared to the gasification performance of the most traditionally used biomass, pine sawdust, the other studied biomasses showed comparable results in the cases of PKS, OS, and PCL, and slightly lower but still acceptable values in the cases of AS, CHE, GP, and CS. This suggests that biomasses with lower results could be gasified in mixtures with those showing better performance.

Migilacio et al. [51] studied sewage sludge at 850 °C. The dry base gas produced, excluding N₂, had the following composition: CO = 30%, CO₂ = 29%, H₂ = 26%, CH₄ = 10%, C₂H₄ = 3%, and C₂H₆ = 0.19%.

Rabah [52] assessed syngas production from a variety of agricultural residues, including sugarcane bagasse, wheat straw, cotton stalks, sesame straw, groundnut shells, sorghum straw, millet straw, and others. Regardless of the specific biomass, the resulting syngas consistently exhibited a molar composition of approximately 32–42% hydrogen, 13–16% carbon monoxide, and 16–22% carbon dioxide, with a lower heating value ranging from 5.0 to 8.0 MJ/kg. Wheat, peanut, and sunflower residues demonstrated superior syngas quality compared to millet and bagasse.

The ash content in agricultural residues can vary significantly, ranging from 0.3% to 16% on a dry basis. This high concentration of ash can lead to various operational issues in industrial processes, such as equipment fouling, sintering, slag formation, and agglomeration, as reported by Puri et al. [53].

3.1.2. Characteristics of Biomass

• Moisture;

Gao et al. [54] concluded that higher moisture content increases the production of hydrogen (H₂) in the syngas. However, excess moisture complicates the gasification process and increases energy consumption. Other authors, such as Akshya [49], have similarly concluded that when the moisture content exceeds 30%, it impairs ignition and reduces the calorific value of the produced gas. Higher moisture content will increase the levels of H₂ and CH₄ but will reduce the concentration of CO. However, this increase in H₂ and CH₄ does not compensate for the energy loss due to the reduction in CO content, resulting in a product gas with a lower calorific value. Biomass residues, commonly wet (80–99% by weight) and originating from various industries (agricultural, food, paper), are poorly suited for traditional thermochemical processes. However, [55] demonstrated that supercritical water gasification (SCWG) is a viable alternative.

• Calorific value of biomass;

Calorific values are important parameters for measuring the performance of gasification. Vaezi et al. [56] evaluated the calorific values of various biomass types, finding that forest residues have the highest calorific value.

The calorific value is related to the moisture content; as the moisture percentage increases, the calorific value decreases, along with the temperature [56].

Biomass with a higher calorific value will produce syngas with a higher energy content, thus increasing the system’s efficiency. However, it is crucial to consider other factors that influence the calorific value of the resulting gas, such as the gasifying agent. Syngas produced by gasification with air has a calorific value of approximately 4–7 MJ Nm⁻³, while using pure O₂ can achieve a calorific value of up to 12–28 MJ Nm⁻³. Increasing the steam/biomass (S/B) ratio boosts H₂ production, which also raises the calorific value of the gas. Conversely, a higher equivalence ratio (ER) results in lower yields of H₂ and CO, along with an increase in the amount of CO₂, which reduces the calorific content of the syngas [57].

The efficient use of municipal waste for energy production through gasification presents certain challenges, such as the generation of a lower calorific value and a large amount of ash, as mentioned by Shahbaz et al. [58].

• Volatiles content;

According to Y. Gao et al. [54], a lower proportion of volatiles in the feedstock results in higher tar production and lower amounts of syngas components.

• Biomass particle size;

Smaller biomass particle sizes improve the quality of the syngas and allow for a reduction in reactor size, thereby lowering the overall production cost. Reducing the particle size increases the surface area available for reaction, enhancing the efficiency of the gasification process [59].

3.1.3. Reactor Diameter

Pérez et al. [60] evaluated the impact of reactor diameter on syngas production. When comparing reactors with diameters of 0.104 m and 0.054 m, they observed that larger-diameter reactors generated syngas with a higher calorific value and higher concentrations of H₂, CO, and CH₄. In contrast, smaller-diameter reactors favored the formation of CO₂.

4. Definition of Properties

4.1. Inlet Streams

The biomass gasification simulations define biomass as an unconventional solid. In order to be able to introduce biomass, inlet streams containing conventional and non-conventional sub-streams are defined. The stream type “MIXNCPSD” contains the sub-streams MIXED, CIPSD, and NCPSD and considers the particular size distribution; AlNouss et al. [61] used it in their simulation. Another commonly used stream when working with non-conventional sub-streams is MIXCINC, containing MIXED, CISOLID, NC, and pure solid substreams; [62,63,64] used it to include both conventional and unconventional elements. Other simulations employ the stream class MCINPSD, containing MIXED, CIPSD, and NCPSD; Mehrpooya et al. [65] used it in their simulation. Another stream type is MIXNC, containing MIXED and NC, as used by [66,67,68]. The stream MIXCIPSD (conventional inert solid mixed with particle size distribution) was also used by Li et al. [69] (see Figure 2).

4.2. Thermodynamic Methods

Property methods are used to calculate the physical properties of conventional components in the gasification process, as described in [70,71].

Commonly, the thermodynamic method considered for this type of simulation is the Peng–Robinson model with a Boston–Mathias function (PR-BM). This method is effective for non-polar or slightly polar mixtures, such as hydrocarbons and light gases like CO₂, H₂S, and H₂ [71,72]. This model is suitable when working with gasification at high temperatures as mentioned by Bach et al. [73].

The Redlich–Kwong–Soave cubic equation of state method with the Boston–Mathias alpha function (RKS-BM) was also adopted for [64,74,75]. This approach not only facilitates the mixing of non-polar and slightly polar components but also proves advantageous in biomass processing, as mentioned by Xiang et al. [76].

Another method for calculating the properties of conventional components is the Redlich–Kwong–Soave cubic equation of state (RK-SOAVE). This method stands out as a valuable tool for handling non-polar and weakly polar substances, such as hydrocarbons and light gases (CO₂, H₂). Its effectiveness stems from its ability to deliver accurate results under a wide range of temperature and pressure conditions, as mentioned by Gao et al. [77].

Another commonly used method, “IDEAL”, as adopted by Singh et al. [24], is suitable for phase equilibrium calculations, based on Raoult’s law, Henry’s law, and the ideal gas law [65].

Another method used with solid input components is the “SOLID” method, recommended by Aspen Technology [78].

5. Stages of a Simulation in Aspen Plus

The biomass simulation in Aspen Plus has been carried out considering different stages, and the most commonly used stages are described here.

5.1. Pretreatment

To enhance the quality of syngas, some researchers implement pretreatment procedures, commonly involving biomass shredding and drying. However, others, such as [15,79,80,81,82,83], did not consider pretreatment steps upstream of the gasification process.

5.2. Crusher

Certain simulations conducted with Aspen Plus consider that the initial stage of the gasification process is the crushing stage. This was the case of Kakati et al. [84], who carried out a simulation that included crushing. Particles outside the desired range were recirculated back to the crusher. Similarly, Alcazar-Ruiz et al. [85] simulated a fast pyrolysis process including a crusher as the first stage of pretreatment, which reduced the particle size by 5 mm. However in some cases, the biomass crushing is considered as a second stage of pretreatment, as in the study of Detchusananard et al. [86], where the particle size was reduced to less than 20 mm.

Some simulations only include biomass crushing as the sole pretreatment. For instance, in the simulation conducted by Ismail et al. [87], biomass shredding was the only pretreatment step, without incorporating a drying phase. In this shredder, the particle size was reduced to less than 15 mm.

5.3. Dryer

Drying is an important aspect of the pretreatment step. It plays the role of significantly reducing the moisture attribute specified in the proximate analysis. Drying only modifies this component, with all other values contained in the ultimate and sulfur analyses remaining constant. According to Aspen Technology [78], during the drying process, the moisture content of the dried biomass is generally reduced to 10–20% in order to produce a syngas with a high calorific value, as indicated in [88,89]. However, other authors consider that the moisture should be reduced to around 5%, such as [71,90].

The temperature range in the drying stage is manipulated to 100–200 °C, as mentioned in [24,89].

Various methods and simulation approaches are employed to achieve optimal moisture reduction. This document explores three primary approaches to biomass drying within the Aspen Plus simulation environment, highlighting the methodologies and configurations utilized in different studies. Each approach leverages distinct types of equipment and drying agents.

• First approach:

Drying of solids is commonly performed through a stoichiometric reactor available in the Aspen Plus (R-STOIC) library. When the drying operation is created, a stream of wet biomass is fed into the reactor in addition to another stream containing a drying agent, as used in [11,78]. In some cases, air is introduced as the drying agent, as in the simulations carried out in [91,92]. Nitrogen can be directly introduced as a drying agent [23].

In this type of reactor, a reaction occurs where a portion of the biomass reacts to produce water. According to the authors of [31,93], the chemical reaction is represented as “Biomass (wet) → 0.05 H₂O + Biomass (dry)”. This coefficient of 0.05 is derived by dividing the molecular weight of the biomass by the molecular weight of water (18 g/mol), as mentioned by Tavares et al. [94]. Aspen Plus assumes that all non-conventional components have a molecular weight of 1 g/mol, so the molecular weight of the biomass is assumed to be 1. Kakati et al. [84] conducted the drying stage as the second step of the pretreatment, which took place at 150 °C and 1.1 bar (Figure 3).

• Second approach:

In other cases, the dryer is located directly in the solids simulation area of the Aspen Plus library. An example of this approach is provided by Detchusananard et al. [86]. Their simulation utilized the “DRYER” unit model to achieve a final moisture content of 5.9% in the biomass. To simulate drying, it is not necessary to use a flash2-type separator, because in this equipment two separate output streams can be considered: one for the agent with water content, and another where the dry biomass will come out (Figure 4).

• Third approach:

The Aspen Plus library also offers a “HEATER” unit, functioning similarly to a heat exchanger dryer. This unit can be configured by means of a calculator to remove the water content of the biomass, as reported by Abdelouahed et al. [20], where the moisture content of the dry biomass was established using the Fortran language. In this work, the moisture content in the biomass was reduced from 50% to 12% in the simulation of the FERCO company, and from 50 to 38% in the simulation of the TNEE company; these data are typical of these two technologies (Figure 5).

5.4. Simulation of Biomass Gasification

Based on the analysis of the different configurations simulated in Aspen Plus, a schematic diagram (Figure 6) was developed that summarizes the variety of approaches employed in recent years. It can be observed that biomass pretreatment is a common preliminary step in some simulations, aimed at improving gasification efficiency. Regarding the gasification simulation itself, the authors have adopted different configurations depending on the approach used: from simulations that consider gasification as a single process, to models that subdivide the process into multiple stages (pyrolysis, gasification, oxidation, reduction, and combustion). For more information, see the Table S1 in the Supplementary Materials.

5.5. Pyrolysis Stage

The simulation of the gasification process is commonly approached by dividing it into successive stages. Models that use a thermodynamic equilibrium approach, employing a Gibbs reactor, typically consider two main stages: pyrolysis and gasification. In these cases, after the biomass pretreatment, devolatilization or pyrolysis occurs. To simulate pyrolysis, authors consider different approaches, such as correlations and kinetics.

5.5.1. Approach with Correlations Based on Elemental Analysis

This stage can be simulated using correlations, where biomass is transformed into its constituent elements through elemental analysis values. Studies such as [9,79,81,95] have used this approach, where they decomposed biomass into its basic elements (carbon, hydrogen, oxygen, and nitrogen) using a combination of proximal and ultimate analysis.

5.5.2. Correlations Approach for Biomass Pyrolysis from Experimental Data

Some authors simulate the pyrolysis process using correlations based on yields determined from experimental data, such as the simulation carried out by Rupesh et al. [96]. These data are introduced through a calculator using the Fortran language. The calculator specifies the quantities of gas, tar, and char produced as a function of temperatures ranging from 750 to 900 °C. The pyrolysis reactor works as a separator, enabling the separation of these three products in separate streams. Likewise, Abdelouahed et al. [20] also employed a yield reactor, where correlations based on experimental data were introduced. Beheshti et al. [21] handled the same correlations for pyrolysis.

We should note that these simulations performed in Aspen Plus used the Fortran language for introducing pyrolytic yields, but this is not always the case. The Aspen Plus simulator also allows for the introduction of these data through Excel, as was the case in the study of Puig-Gamero et al. [97]. This simulation also has the particularity that both the drying and pyrolysis processes occur in the pyrolysis section.

5.5.3. Kinetic Approach for Biomass Pyrolysis

For the simulation of the pyrolysis process, some studies also consider the reaction kinetics. For instance, in the simulation conducted by Kaushal et al. [89], a semi-kinetic approach was used to simulate the devolatilization process. The pyrolysis process is subdivided into two stages: (i) the first stage includes the use of correlations for the decomposition of the biomass into its constituent components using the Fortran language through the information provided in the ultimate and proximate analysis; (ii) the second part includes kinetics with the simulation performed on an R-YIELD performance reactor coupled with a calculator.

5.6. Gasification Stage

The different approaches used in biomass gasification simulation studies were reviewed, with emphasis on tar formation. Not considering tar is a hindrance according to [89]. During gasification, contaminants like fly ash, NOx, SO₂, tar, and alkali metals are produced. Among these, tar is an unwelcome but unavoidable liquid product, especially in low and medium-temperature processes, as mentioned by Zeng et al. [98]. It was observed that for the simulation of this stage, different approaches are used. For more information, see the Table S2 in the Supplementary Materials.

5.6.1. Thermodynamic Equilibrium (GIBBS)

The R-GIBBS free energy minimization approach is widely used in the analysis of gasification process performance. However, under this approach, tar is usually disregarded. Below is an overview of the work conducted in the literature without considering tars.

Cohce et al. [90] simulated biomass gasification using this approach. In their simulation, the gasification process was subdivided into several stages, including drying, decomposition, and gasification. According to these authors, the gasifier is a key component for improving system efficiency, as it exhibits the highest rate of exergy destruction compared to other equipment, which is directly related to energy losses. The simulation determined that it is possible to obtain 132.7 kg of hydrogen from 4000 kg of biomass, with an energy consumption of 4.5 MW.

Considering that the Gibbs reactor simulates the combustion and reduction zones, Fajimi et al. [43] simulated the coproduction of syngas from tire gasification in three configurations: fixed bed, fluidized bed, and rotary kiln. They divided their simulation into two areas: the pyrolysis zone, and the gasification zone. The results showed that the fluidized bed produced the highest heating value, while the rotary kiln recorded the lowest. The authors concluded that, for the coproduction of syngas and activated carbon, the fluidized bed reactor is the most suitable.

Sreejith et al. [9] simulated the gasification of wood, breaking down the process into the stages of pyrolysis and gasification. They validated their simulation by comparing gas concentrations (H₂, CO, CH₄, CO₂) with experimental data, reporting a root-mean-square error (RMSE) ranging from 2.4 to 5.2 depending on the temperature, which varied between 690 and 770 °C.

M. Singh et al. [41] simulated the co-gasification of plastics mixed with biomass. In their simulation, they first separated the solid and volatile components; the volatiles were then gasified in a Gibbs reactor. Both components were subsequently processed in stoichiometric reactors of the CSTR and PLUG types, where they modeled the reactions in fluidized beds. The study found that co-gasification yielded a higher hydrogen content compared to the gasification of pure biomass. With 30% plastic content, and at a temperature of 750 °C, hydrogen concentrations of 65.32% and 63.80% were achieved for biomass-PE and biomass-PP, respectively. The study reported an RMSE ranging from 3.1 to 6.7 when comparing the syngas composition with experimental data at temperatures between 690 and 770 °C, with greater variation observed in the CH₄ concentration.

Singh et al. [92] validated their results using experimental data on molar compositions for four different feedstocks: hardwood chips (HC), poultry manure pellets (PP), softwood pellets (SP), and rapeseed straw pellets (RP). The validation of gas compositions (CH₄, CO, CO₂, H₂) yielded an RMSE ranging from 0.049 to 0.12 at a temperature of 775 °C.

Gündüz Han et al. [99] used a thermodynamic equilibrium model for five different types of plastics, considering the formation of inorganic compounds such as H₂S, HCl, and NH₃, which are often overlooked in the literature. When comparing with experimental studies, they achieved a root-mean-square error (RMSE) of 0.9 for the gas compositions (H₂, CO, CH₄, CO₂) at temperatures ranging from 850 to 894 °C.

Hosseingholilou et al. [100] conducted a simulation assessing various gasification agents (water, air, and CO₂), with the primary goal of maximizing hydrogen production and improving energy efficiency. They concluded that water vapor was the most effective gasification agent for olive pomace, achieving an energy efficiency of 48% at an optimal temperature of 884 °C. In comparison, air reached an efficiency of 28% at 1000 °C, while CO₂ achieved 46% efficiency at 989 °C. The simulation was carried out in several stages: initially, the biomass underwent pyrolysis in a performance reactor, and the resulting products were sent to a Gibbs reactor to simulate chemical and phase equilibrium. The authors excluded tar from their considerations, assuming that downdraft gasification produces negligible amounts of this compound. They validated their model using the molar fraction of gases and performed three validations with different gasification agents: air–water (RMSE of 0.02 at 900 °C), air (RMSE of 0.03 at 1050–1000 °C), and CO₂ (RMSE of 0.08 at 800–1000 °C).

N. Gao et al. [77] developed a gasification model that separates the volatiles from the solid part, gasifying the volatiles in a Gibbs reactor using steam and air as gasification agents. They validated their model by comparing it with experimental results and another simulation model developed in Aspen Plus. They achieved good agreement with experimental results at 850 °C. The RMSE for each gaseous component, except methane, ranged from 3.72% to 8.30%. However, they observed that the simulation did not predict accurately at lower temperatures. When compared to a simulation model, their model showed much higher errors, ranging between 2.05% and 80%, leading the authors to consider their simulation to be more accurate than the compared simulation model. The authors noted that the model assumed that no tar was generated and that CH₄ was the only hydrocarbon product during the simulation process. However, at lower temperatures, biomass generates more unburned hydrocarbon compounds and tar, which was not considered in this model. As a result, the concentrations of CH₄ and H₂ tend to be overestimated at lower temperatures.

Ali et al. [101] conducted a simulation of the gasification of date palm residues using steam as the gasifying agent. The gasification process was divided into two stages: pyrolysis and gasification. The resulting syngas was used in a power generation system with gas and steam turbines. The authors also performed a sensitivity analysis and found that both temperature and steam flow rate positively impacted energy generation, achieving a power range of 3 to 5 MW. At a temperature of 850 °C, the composition (vol. %) of the syngas obtained was as follows: H₂ 37.88, CO 14.24, CO₂ 11.29, and CH₄ 0.001.

Kombe et al. [63] conducted a study on the gasification of sugarcane bagasse using a thermodynamic equilibrium-based approach. In their study, the reactor was divided into four zones: drying, decomposition, combustion, and gasification. The combustion and gasification zones were analyzed using Gibbs equilibrium reactors, without considering tar formation. They validated their model by comparing it with experimental data from two studies that used various feedstocks, achieving an RMSE ranging from 0.99 to 2.38 when evaluating the composition of gases such as CO, H₂, CO₂, CH₄, and N₂ at temperatures between 900 and 1000 °C. The authors highlighted that regression models for lower heating value, cold gas efficiency, and synthesis gas concentrations (CO₂ and H₂), developed from ANOVA analysis, demonstrated a high degree of accuracy.

Zaman et al. [102] employed response surface methodology to assess the combined effects of key parameters on gasification. The study revealed that steam gasification can produce a relatively clean and hydrogen-rich gas, achieving concentrations of up to 58% on a dry basis. The optimal performance was achieved at a gasification temperature in the range of 750 to 900 °C and a steam-to-biomass (S/B) ratio between 0.70 and 0.81. Under these conditions, the cold gas efficiency (CGE) approaches 90%, and the lower heating value (LHV) of the dry gas reaches 12 MJ/kg. The study did not consider tar formation. Model validation was carried out by comparing the simulated results with experimental data, resulting in an RMSE of 3.79, indicating good agreement between the model and the experimental data.

Zhao et al. [103] conducted an energy analysis of the co-gasification of a biomass and plastic mixture using plasma. The study revealed an RMSE of 3% when compared to a simulation model and 6% compared to experimental data. The authors attributed these differences primarily to the lack of consideration of practical aspects such as material flow properties and reactor geometric characteristics. Additionally, the study highlighted that the energy conversion efficiency of the proposed processes exceeded 57.8%. Notably, a high H₂ selectivity of 73.3% was achieved, with a hydrogen conversion rate of 75.6%.

Ranjan et al. [104] conducted a simulation of the co-gasification of biomass and plastic waste, where both feedstocks were pretreated independently by drying and pyrolysis before gasification. The volatiles generated during the pyrolysis of both materials were mixed and introduced into a Gibbs reactor, simulating a downflow reactor. The study also included a statistical analysis using response surface methodology to develop regression equations, which were created using ANOVA. The results from the Aspen Plus simulation were then analyzed in MINITAB. The model validation was performed by comparing the results with experimental data, yielding an RMSE that ranged from 2.3 to 4.7.

Tavares et al. [94] simulated the gasification of biomass derived from forest residues, demonstrating that the use of steam as a gasifying agent increases both the hydrogen content and the calorific value of the produced gas compared to the use of air. They validated their model using experimental data and by comparing it with a simulation model, calculating the relative error in the composition of gases (N₂, CO₂, CH₄, CO, H₂). When compared with experimental data, the deviation ranged between 3.4% and 17.3%, while compared to a simulation model the values varied between 3.6% and 87.9%. The effect of tar was disregarded in the model, because gasification produces negligible amounts of this byproduct, according to the authors.

All of the previously mentioned studies used Gibbs equilibrium without considering tar. However, other simulations using the thermodynamic equilibrium method do not ignore tar. In this case, tars are introduced as inert, following the approach outlined by Gagliano et al. [105], who modeled the gasification process in two stages: pyrolysis, and gasification in a downflow reactor, treating char and tar as inert. The model was specifically validated for the main components of syngas (H₂, CH₄, CO). The results showed an average error of 15% in the concentrations of these gases in the syngas, with an error of less than 7%. To validate the model, two different types of biomass were used: PE (eucalyptus pellets) and RW (rubber wood).

Also, certain authors consider thermodynamic equilibrium only for the gasification of volatiles; tars are separated in earlier stages and treated independently, as outlined by Rupesh et al. [96], who developed a model for the air–steam gasification of sawdust, incorporating a CO₂ capture process. The model included pyrolysis, tar cracking, and carbon conversion. In the simulation, biomass was decomposed into volatiles, tar, and char. The tar was subjected to a cracking process in a specific reactor, while the volatiles were gasified in a Gibbs equilibrium reactor. The RMSE obtained by comparing experimental molar fractions was 2.8, indicating a good agreement between the model and the experimental data. The study also evaluated the impact of the gasification agent, observing an increase in H₂ production when steam was used, unlike air, which led to a decrease in H₂ content. Other authors who also considered tar in their models include Marcantonio et al. [82], who simulated the gasification of hazelnut shells in a bubbling fluidized bed. After pyrolysis, the biomass was decomposed into volatiles, char, and inorganic compounds (such as H₂S, NH₃, and HCl). They used Gibbs equilibrium for the gasification of the volatiles, and their model included a separate stage for the production of tar, encompassing compounds like naphthalene and benzene. Additionally, the simulation included a hot gas cleaning process.

5.6.2. Kinetic Approach for Biomass Gasification

Authors who consider the formation of tar considering non-equilibrium and including reaction kinetics equations are commonly found in the literature, such as the simulation performed by Rabea et al. [18], who conducted a simulation of the gasification of woody biomass using a downward flow kinetic model. They employed Aspen Plus in combination with MATLAB. The gasification was modeled as a sequence of stages, including decomposition, pyrolysis, combustion, and reduction. Notably, this simulation used eight stoichiometric reactors in sequence to represent the gasification process. The model was validated against experimental data, achieving RMSD values ranging from 0.7 to 1.7.

Abdelouahed et al. [20] also developed a kinetic model for a dual fluidized bed gasifier, where the biomass was first dried and pyrolyzed. Subsequently, the volatile and solid fractions were separated. The volatile fraction was gasified using both homogeneous and heterogeneous kinetic reactions, while the solid fraction was decomposed and then combusted. The results obtained were compared with experimental data from two technologies: Tunzini Nessi Equipment Companies (TNEE), and the Battelle High-Throughput Gasification Process (FERCO).

Beheshti et al. [21] simulated the gasification of wood chips with air–steam in a bubbling fluidized bed reactor. They separated the volatile and solid fractions, and the volatile fraction was gasified using reaction kinetics. The simulation considered the reactor’s hydrodynamics. Additionally, they analyzed the effect of the equivalence ratio (ER) on tar production, showing that the amount of tar decreases as the equivalence ratio increases. The results were validated against experimental data, with an RMSE ranging from 1.9 to 2.4.

Puig-Gamero et al. [97] also developed a simulation based on a kinetic model. In this model, the gasification process was divided into several stages: pyrolysis (including the drying process), char decomposition, oxidation, and reduction. Additionally, the authors analyzed the behavior of tar in relation to the equivalence ratio (ER), concluding that its formation is favored at low ER and temperatures. The results were validated with experimental data, and the model predicted CO, CO₂, CH₄, and C₂H₄ compounds with greater accuracy than H₂ and N₂, attributing this behavior to the water–gas shift reaction and other water–gas reactions considered to be the most influential. The absolute errors obtained ranged from 0.1% to 8%, with the highest absolute error being 8% for N₂ and 4.4% for H₂.

Haji Hashemi et al. [106] developed a detailed simulation model for gasification, dividing the process into successive stages: drying, pyrolysis, tar cracking, and gasification of char and gas. This kinetic model was validated using experimental data and compared with other models from the literature, such as equilibrium and modified equilibrium models. Modified equilibrium models aim to improve predictions by applying correction factors; in the reviewed case, these factors were based on the equivalence ratio (ER). The validation of the model with experimental data yielded highly accurate results, with mean absolute percentage errors (MAPE) of 8.91%, 1.98%, 8.62%, and 1.10% for H₂, CO, CH₄, and N₂, respectively. These results were significantly better than those obtained with the equilibrium and modified equilibrium models, demonstrating that the kinetic model was more accurate.

However, some authors use the kinetic approach without considering tar formation, like Bach et al. [73]. These authors simulated biomass gasification using steam in a dual fluidized bed reactor, employing stoichiometric reactors in the process. Their model did not account for tar formation. Although they did not explicitly specify the type of reactor used, the model was validated with experimental data, and while statistical deviation was not calculated, the comparative graphs demonstrated good agreement between the simulated and experimental results.

5.6.3. Correlations Approach for Biomass Gasification from Experimental Data

Empirical correlations can also be used to simulate biomass gasification. For example, in the technical report from the National Renewable Energy Laboratory (NREL), gasification simulations were conducted using Aspen Plus software, where empirical correlations based on temperature were applied. This analysis focused on the economic feasibility of hydrogen production from biomass gasification and considered tar treatment. According to the report, the sensitivity analysis revealed that any parameter significantly affecting the thermal balance of the system will have a significant impact on the minimum hydrogen selling price. For example, using a feedstock with lower moisture content also affects the thermal balance, leading to a reduction in the hydrogen price [107].

In the simulation conducted by Piazzi et al. [64], gasification was divided into two stages: decomposition followed by a gasification reactor that operates based on empirical correlations for syngas yields. Four plant configurations were modeled in Aspen Plus, comparing two gasification systems (air and steam) and two cleaning systems (hot and cold). The results showed that the combination of steam gasification and hot gas cleaning achieved the best performance across all analyzed metrics: mass yield (11.6% by weight), energy efficiency (52.0%), and exergy efficiency (55.5%). The comparison of gas cleaning systems highlighted that the hot gas cleaning system has lower irreversibilities, while the cold gas cleaning system presents higher exergy losses.

In the same way, Hernández et al. [108] proposed a simulation where the mass yield of the gasification products was calculated using correlations implemented in a Fortran language program. The reactor operated at temperatures ranging from 600 to 900 °C, considering the formation of tar components such as benzene, naphthalene, phenol, furan, o-cresol, indene, toluene, pyrene, and p-xylene. The authors reported good reproduction of experimental results; however, they clarified that the correlations were designed for a specific process, so their application to other installations may have limitations.

6. Synthesis Gas Cleaning

To improve the quality of syngas, cleaning processes are currently used to remove contaminants from the syngas. Tars and CO₂ are among the main impurities.

6.1. Tar Treatment

There are various methods for treating tar, each with its own advantages and disadvantages. The choice of the most suitable method depends on several factors, such as the composition of the tar, the desired end product, and the applicable environmental regulations.

Tar is formed during the incomplete conversion of biomass. Tar treatments are divided into two methods: primary methods involve cleaning inside the gasifier, while secondary methods involve cleaning the synthesis gas produced after the gasification stage, as mentioned by Harb et al. [109]. Thapa et al. [110] categorized secondary methods into two main groups: wet techniques and dry techniques. Dry methods, which include catalytic cracking, thermal cracking, and plasma cracking, aim to eliminate tar by breaking down heavy hydrocarbons. On the other hand, wet methods, such as packed-bed scrubbers, spray towers, and venturi scrubbers, are designed to capture tar in a liquid medium (for example, water, vegetable oil, diesel, biodiesel).

6.1.1. Primary Methods

The primary methods for reducing tar in biomass synthesis gas focus on improving the efficiency of the gasification process to convert as much biomass as possible into syngas and minimize tar formation from the outset, as described by Deviet al. [111]. These methods have the advantage of reducing the process cost, as mentioned in [112]. However, according to the conversion rates, these primary methods are not high enough to eliminate all of the tar, as mentioned in [112,113].

Lotfi et al. [114] emphasized the fundamental role that primary tar removal methods play in biomass gasification, not only in the distribution of end products but also in the generation of tar. According to these authors, various parameters, such as system configuration and operating conditions, significantly influence these processes. Among them, noteworthy are temperature, pressure, equivalence ratio (ER), oxygen (O₂) content, feedstock type, gasifying medium, and residence time.

6.1.2. Secondary Methods

The secondary methods are further subdivided into physical methods and chemical methods.

• Secondary physical methods:

These methods are based on physical separation without altering the chemical composition. These methods can be further classified into dry and wet methods according to Lateh et al. [115]. According to Anis et al. [116], dry methods are typically employed at temperatures exceeding 500 °C, while wet methods are generally favored for lower temperature ranges between 20 °C and 60 °C. As the gas temperature needs to be lowered, they not only incur additional costs but also diminish the calorific value of the gas itself. Moreover, the wet method may potentially lead to water contamination, as mentioned in [117,118].

2.

Secondary chemical methods:

The thermochemical pathways for tar cracking include thermal cracking, catalytic cracking, and catalytic reforming. These processes directly transform tar compounds into lighter tar fragments [119].

Tar cracking, both thermal and catalytic, is commonly performed after gasification, constituting the most widely used chemical method for its processing, as mentioned in [120].

• Thermal cracking:

Thermal cracking is the process of breaking down tar without the presence of a solid catalyst, according to Vreugdenhil et al. [121].

Monir et al. [122] observed a substantial increase in tar reduction efficiency as the temperature increased, with a notable increase from 81.87% to 97.25% when increasing the temperature from 700 °C to 1000 °C. According to Basu et al. [123], this process uses temperatures around 1200 °C. This temperature requirement depends on the constituents of the tar. Oxygenated tars can crack at around 900 °C. Other authors reaching similar conclusions include Tang et al. [124], who mentioned that complete tar removal requires temperatures above 1000 °C. Even in the presence of air, oxygen, or other gasifying agents, the temperature necessary for tar removal must surpass 900 °C. However, due to this high energy demand, thermal cracking of tar is considered to be an unfavorable method.

Another important aspect is carbon deposition, with increasing temperature during thermal cracking, as observed by Deng et al. [125]. Kaisalo et al. [126] mentioned that a limitation of this technology is the production of soot and a reduction in efficiency due to the need to reach temperatures above 1100 °C.

• Catalytic cracking of tar:

The catalytic cracking process occurs when there is a solid present. If this solid lacks chemical activity, it is known as “heterogeneous thermal cracking”; however, if the solid facilitates the chemical reaction, it is referred to as “catalytic cracking”, according to [121]. Catalytic cracking is performed at lower temperatures because the use of catalysts decreases the activation energy, as described by Xu et al. [127]. One other advantage of catalytic cracking over thermal cracking is that catalytic cracking does not reduce the heating value of the gas, as is the case with thermal cracking at elevated temperature, as mentioned by Fjellerup et al. [128].

To date, different catalysts have been used for catalytic cracking, including alumina, dolomite, olivine, limestone, alumina silicates and zeolite, magnesites, Ni, Mo, Pt, K₂CO₃, Fe²⁺/Fe³⁺, and char [128].

• Tar reforming:

Catalytic steam reforming of tar is considered to be an attractive method, as the presence of a catalyst can remove tar more effectively. Additionally, this process converts tar into useful gases (H₂, CH₄, and CO) at a lower temperature than non-catalytic techniques [129]. However, the presence of impurities in syngas, such as H₂S, HCl, HBr, siloxanes, alkali metals, and NH₃, affects the lifespan, activity, and stability of nickel-based catalysts, as mentioned by Binte et al. [130]. According to Kaisalo et al. [126], sulfur can poison catalysts.

Figure 7 provides a comprehensive overview of tar treatment strategies in biomass gasification. These encompass both primary and secondary methods, with secondary methods further categorized into physical (dry and wet) and chemical approaches.

6.2. CO₂ Capture

Currently, it is thought that gasification technologies are the most environmentally friendly. The authors of [21,32,131] claim the CO₂ neutrality of biomass based on the natural carbon cycle. They argue that photosynthesis fixes atmospheric CO₂ into biomass, and that its subsequent combustion or gasification releases only the previously absorbed CO₂, thus offsetting emissions.

The choice of method and carbon capture technology depends directly on the type of biomass to be gasified, as well as on the gasification process itself and the intended application of the resulting gas. There are three possible technologies for carbon capture: pre-combustion, post-combustion, and oxy-combustion, as described by Ghiat et al. [132].

One of the most common problems faced by this type of process is the large amount of CO₂ usually produced in them. One of the techniques used to minimize CO₂ emissions is the use of solvents that can absorb CO₂. However, several attempts to capture CO₂ in power generation plants have been obstructed by the penalization of thermal efficiency due to the huge energy consumption with conventional processes, such as CO₂ capture based on absorption in amines, which are also corrosive and require a lot of energy to reactivate according to [15,133]. Despite this, amines are commonly used for CO₂ capture.

Typically, Aspen Plus employs an absorption column from the RADFRAC library to simulate CO₂ capture. In this setup, syngas is introduced and washed in direct counter-current contact with a solvent stream. The CO₂-free syngas exits at the top of the absorber, while the CO₂-rich solvent solution exits at the bottom. This method was utilized in a simulation conducted in [134], which used amines as a solvent and, in turn, justified the use of this method by stating that it is a mature and commercially available technology.

However, amines are not the only solvent used for CO₂ capture; such was the case of the simulation carried out by Haider et al. [133] who proposed using deep eutectic solvents (DESs) based on phosphonium. This author mentioned that this type of solvents is non-corrosive, easily regenerable, and has an economical production cost.

Ionic liquids also are used as solvents, as was the case in the study of Hospital-Benito et al. [135], who carried out a simulation of a CO₂ capture process by chemical absorption.

Use CO₂ as a Gasification Agent

The use of CO₂ as a gasification agent is a strategy to reduce its emissions. While steam and oxygen are the most common agents in gasification, their high energy consumption has driven research towards alternatives such as CO₂, as mentioned by Li et al. [136]. An example of this is the simulation carried out by Dattatray et al. [15], who used part of the CO₂ produced as a gasification agent.

Li et al. [136] conducted a simulation using Aspen Plus. They concluded that, under optimal design parameters, energy efficiency could reach 53.25% and the primary energy saving ratio could reach 10.17% at an optimal gasification temperature of 825 °C and a CO₂/C ratio of 0.25.

Sadhwani et al. [137] conducted experiments and found that the maximum higher heating value (HHV) for CO₂ gasification is slightly higher than that of air gasification due to the greater presence of hydrocarbons. CO₂ gasification can be more energy-efficient than air or oxygen gasification, especially for the production of syngas with a high hydrocarbon content. However, the yield of CO and H₂ in CO₂ gasification is lower than in oxygen gasification.

Reyes et al. [138] claim that the production of CO and H₂ is lower in gasification with CO₂ compared to gasification with oxygen. However, gasification with CO₂ can achieve a slightly greater higher heating value (HHV) than gasification with air, due to the greater amount of hydrocarbons in the syngas.

Table 2. Different simulations of tar treatment and CO₂ capture.

Treatment	Equipment	Technologies	Description	Tars	Reference
Tar treatment	R-CSTR and R-GIBBS	Steam reforming	The tar is produced during the pyrolysis stage. It is then separated and sent to a gas cleaning unit, where nickel reactions occur. It is then sent to a CSTR reactor, where steam reforming reactions occur.	Biomass: wheat straw, Gasification agent: steam, Catalyst of TSR: Ni-Co-Al2O3 (15, 10, 5)%, Tars: phenol, toluene, naphthalene.	[139]
	RADFRAC	Absorption of tar with oils (canola, soybean, palm, used cooking oils, tallow biodiesel, and resin oil)	A gas mixture containing H2, CO, CO2, CH4, H2O, N2, and tars was introduced. Then, oil was introduced at the top of the absorber. The tar was absorbed by the oils.	Feed: syngas, Tars: benzene, toluene, xylene, styrene, naphthalene, 2-methylnaphtalene, 1 methylnaphthalene, diphenanthrene, fluorene, phenanthrene and anthracene.	[140]
	R-PLUG	Thermal cracking (oxidation kinetics and reduction stage)	In this simulation, reactions with their kinetics are used to simulate the thermal cracking.	Biomass: macadamia nutshells, Tars: phenol, naphthalene, benzene toluene.	[141]
	R-CSTR	Tar thermal cracking	The tars produced in the pyrolysis stage were cracked using kinetics.	Biomass: pomegranate wood, Tars: phenol, naphthalene, acetol (C3H6O2).	[106]
	R-GIBBS/R-CSTR	Combustion and cracking	In this simulation, the tar is separated from the mixture after the pyrolysis process. Subsequently, it is directed to a cracking reactor using a Gibbs reactor. Then, these products are also subjected to cracking through reaction kinetics using a CSTR reactor.	Biomass: white pine, Gasification agent: steam, Tars: benzene, toluene, phenol y naphthalene.	[142]
	R-GIBBS	Thermal cracking combustion of tar	The tar is separated from the volatiles and the char, and it is burned in a combustion reactor. The final product does not contain tars.	Biomass: pine sawdust, Gasification agent: air–steam, T: 700–800 °C.	[143]
	RADFRAC	Scrubbing	It consists of two scrubbers followed by a stripper. The producer gas is cooled (and washed in the first scrubber with water) to condense the heavy tar fraction. Then, in the second scrubber, the light tar fraction is absorbed. Finally, the stripper allows for the regeneration of the washing liquid.	Feed: syngas, Tars: toluene, benzene phenol, naphthalene, indene and fluoranthene.	[109]
	R-STOIC	Thermal cracking combustion of tar	The tar produced in the pyrolysis stage is taken to a reactor that models oxidation reactions, incorporating tar cracking reactions.	Biomass: Prosopis juliflora, Gasification agent: air gasification, T:800–1000 °C, Tars: phenol, toluene, naphthalene, and benzene.	[144]
		Steam reforming (Aspen HYSYS).	The kinetics is introduced into a stoichiometric reactor.	Feed: syngas, Tars: benzene, toluene, naphthalene, pyrene, p-xylene, indene, ethylbenzene, anthracene, acenaphthylene.	[16]
	R-GIBBS	Tar cracking $r_{j_{crack}}=y_j{\left(10\right)}^{4.98}Exp\left(-{\displaystyle \frac{93.37}{RT}}\right)\left(w_{tar}{p}_g\right)$	The tar produced in the devolatilization stage, which is a primary tar, is cracked in the gasification stage through a general tar cracking kinetics; CO, CO2, CH4, H2, and inert tar are involved.	The tar was considered as a substance.	[89]
	R-STOIC	Cracking	The tar produced in the pyrolysis stage is modeled using correlations.	Biomass: sawdust; tar is considered as a substance.	[96]
	RADFRAC	Absorption and desorption	The synthesis gas was introduced in a counter-current with oils in the absorption tower; the tar components were absorbed, heated, and separated from the solvent in a desorption tower for recirculation.	Tars: benzene, toluene, phenol, and naphthalene.	[145]
	R-CSTR	Thermal cracking	The tar produced in the pyrolysis stage is directed along with the other gases and cracked using reaction kinetics.	Tars: toluene, hydrogen, naphthalene, phenol, CnHm).	[21]
	R-PLUG/ R-EQUIL	Catalytic cracking, steam reforming, and thermal cracking reactions	The conversion degrees are derived from empirical results, and olivine is employed as a catalyst.	Tars: phenol, benzene, toluene, naphthalene.	[146]
	R-PLUG	Thermal and catalytic cracking with biochar	Conversions were used.	Tars: phenol, benzene, toluene, naphthalene.	[20],
	R-GIBBS	Tar reformer	The tar reforming involves a bubbling fluidized bed that also uses olivine as the catalytic bed material.	Biomass: poplar, Tar: naphthalene.	[147]
	R-GIBBS	Tar reformer/water scrubbing	The tar reformer consists of a fluidized bed reactor operating at 766 °C and 1.5 bar, with olivine as the bed material.		[95]
	R-STOIC	Tar reformer (dolomite)	Reactions were used to simulate the reforming stage.	Biomass: pine, Tar: benzene.	[79]
CO2 capture	Absorption tower		Chemical absorption with MDEA-PZ (10 wt.% methyl diethanolamine and 30 wt.% piperazine).	Solvent: MDEA (Aspen HYSYS).	[16]
	RADFRAC	Chemical absorption	An amine-based absorption column and a stripper are used to simulate the solvent recovery.	Solvent: MEA.	[134]
	RADFRAC/FLASH2	Chemical absorption	In this simulation, the synthesis gas was counter-current fed through an absorption column coupled with a regeneration section consisting of a flash liquid–gas separation column available in the Aspen Plus library to regenerate the solvent. This regeneration section consists of a series of expansion drums operating at lower pressures. The DESs have negligible volatility, so at low pressures they only results in the release of carbon dioxide from the top and the bottom of the DESs.	Solvent: deep eutectic solvents (DESs).	[133],
	RADFRAC	Chemical absorption	The synthesis gas is introduced in a counter-current with the ionic liquid that absorbs CO2. RADFRAC columns were used for both the absorption simulation and the regeneration part of the ionic liquids.	Solvent: ionic liquids.	[135],
	FluidBed (unit solid in Aspen Plus)	In situ lime-based CO2 capture in a bubbling fluidized bed (BFB); calcium looping	Limestone was used assuming complete calcination in this simulation. CaO and syngas were introduced into a carbonation reactor, using an LHHW-type kinetics.	Feed: syngas.	[148]
	Absorption/flash column	CO2	In this simulation, an absorption column is used, followed by a depressurization in the flash column to simulate solvent regeneration. Pre-combustion capture.	Feed: syngas, Solvent: (MDEA/PZ), Capture percentage: 90%.	[149]
	RADFRAC	Physical absorption	The synthesis gas is dehydrated with triethylene glycol (TEG) and then mixed with recycled gas, and the ionic liquid absorbs the acidic gas. The purified gas is extracted and the CO and H2 are recycled. The ionic liquid is regenerated in a flash tank and the acidic gases are separated in a distillation tower.	Feed: coal, syngas, Impurities: CO2 and H2S, Capture percentage: (98%CO2), Solvent: ionic liquid/N-ethylmorpholine acetate ([NEMH][Ac]).	[150]
	R-GIBBS	Carbonation	Carbon dioxide is captured through carbonation with calcium oxide (CaO), and then the formed CaCO3 is separated using a cyclone.	Biomass: palm kernels, Efficiency: the CO2 content decreased from 20 to 5.32%, Sorbent: CaO.	[151]
	RADFRAC	Chemical absorption	The absorber and stripper, designed as packed-bed columns, allow a counter-current flow between the exhaust gases and the lean potassium carbonate. Using the RADFRAC model, the kinetic reactions in both columns are considered.	Solvent: piperazine is added to the potassium carbonate solvent, Capture percentage: 80%.	[132]
	RADFRAC	Absorption	A post-combustion capture was performed to clean the synthesis gas through absorption.	Solvent: 30 wt.% MEA, Capture percentage: 90%.	[152]
		Absorption	A post-combustion capture was performed to clean the synthesis gas through absorption.	Solvent: MDEA, Capture percentage: 96.49%.	[153]
	RADFRAC	Physic absorption	The overall process includes a packed-bed absorber and a flash distiller for IL regeneration. Additionally, it features an intermediate-pressure cooling system for separating carbon dioxide from water, followed by compressors with intermediate cooling to increase the pressure of the captured carbon dioxide.	Solvent: ionic liquid using [hmim][Tf2N], Capture percentage: 93.7%.	[154]
		Absorption	A syngas cleaning process was simulated. The syngas was cooled to 300 °C, preventing tar condensation. Then, the syngas was introduced into an absorption tower that operates through chemical absorption, using a solvent. The CO2 was absorbed by the solvent and subsequently separated in a desorption tower.	Feed: syngas from woody biomass, Solvent: hot potassium carbonate solution, Capture percentage: 94.9%.	[155]

summarizes various simulations of tar and CO₂ treatments, detailing the different approaches and equipment used. This provides a comprehensive overview of current strategies in this field.

Table 2 provides an overview of the strategies employed to address two major challenges in biomass gasification: tar treatment and CO₂ capture. For tar, thermal methods such as cracking, reforming, and catalytic cracking are predominant. Due to the complexity of tar, simplified models are often used in many simulations. Naphthalene is the most frequently mentioned model compound, followed by toluene, benzene, and phenol, while compounds such as pyrene, xylene, acetol, diphenanthrene, and others appear less frequently. Some authors consider tar to be a non-conventional substance.

For CO₂ capture, absorption columns utilizing amines, ionic liquids, and other solvents are commonly employed. Additionally, carbonation and calcium looping are emerging as alternatives to CO₂ capture.

7. Recaps

In the studies reviewed, it was observed that the chemical equilibrium approach is the most commonly used in the literature for simulating biomass gasification processes. Although it is relatively straightforward to implement, it has limitations when applied to systems that are out of equilibrium, such as those operating at temperatures below 900 °C.

It has been observed that many authors ignore CH₄ when using the thermodynamic equilibrium method in their validations, due to the significant discrepancies obtained. These models typically assume that CH₄ is the only hydrocarbon product generated during the simulation process. However, biomass produces a range of hydrocarbon compounds at lower temperatures, leading to an overestimation of CH₄ and H₂ concentrations when temperatures are low. This issue has also been highlighted by Kombe et al. (2022) and N. Gao et al. (2021).

The overestimation of CH₄ affects the higher heating value (HHV) of the produced gas, as even a small variation in the CH₄ fraction can have a significant impact on the HHV of the fuel gas. According to Gonzále et al. [156], equilibrium models tend to overestimate the energy performance at low steam-to-biomass ratio (SBR) and steam ratio (SR) values.

According to some authors, this approach does not accurately predict syngas. Gagliano et al. [105] found that the data predicted by their uncalibrated model were nearly inaccurate, particularly for H₂ and CH₄ species. There was a significant overestimation of the H₂ percentage, while the CH₄ percentage was substantially underestimated, almost reaching zero. Due to these inaccuracies, the authors improved their model by calibrating it, including adjustments for the ER ratio and moisture content, and incorporating a temperature approximation in the Gibbs reactor. After these adjustments, their model showed improved predictions compared to experimental data. According to these authors, the LHV (lower heating value) is generally overestimated due to the overestimation of the combustible gases H₂ and CO.

It has been noted that many authors who simulate downflow reactors using the thermodynamic equilibrium method claim that this type of reactor produces insignificant amounts of tar [94,100,157].

It has been noted that the most commonly used method for calculating deviation when validating against experimental results is the root-mean-square error [9,41,63,77,92,99,100,103,104]. However, other error measurement methods were also observed, including relative error [94], absolute error [97], and mean absolute percentage error (MAPE) [106]. According to these reviews, it is recommended that, when evaluating the thermodynamic equilibrium method, errors should be provided for each component rather than as an overall average.

Kinetic method: Although considered the most suitable according to the literature, this approach faces challenges due to the complexity of reaction mechanisms and the scarcity of available kinetic data.

Correlation method: Based on empirical correlations derived from experimental data, this method provides relatively accurate results under conditions similar to those used to develop the correlations. However, its ability to predict system behavior under different conditions is limited, and it does not allow for a detailed analysis of the physical and chemical phenomena occurring in the reactor.

It was identified that temperature is one of the most decisive factors influencing biomass gasification. Upon analyzing the evolution of the generated gases (H₂, CO, CH₄, CO₂) in the reviewed studies, it was observed that the molar percentage of H₂ increased by 8–11%, rising from 22% to 23%, as indicated by Rosha et al. [28] (550–900 °C). A similar behavior was reported by Atikah et al. [29] (700–900 °C), with an increase of 10%. Regarding CO, an increase in the range of 14–30% was observed, while CO₂ showed a decrease of approximately 10%. On the other hand, CH₄ registered a reduction of 2–5%. The percentage increase with temperature is affected by the S/B ratio [42]; as the S/B ratio decreases, the temperature effect also decreases in percentage.

The percentage increase related to temperature is also influenced by the steam/biomass ratio [42]; as the S/B ratio decreases, the effect of temperature on gasification percentages also diminishes.

The effect of pressure, on the other hand, was significantly lower.

It was observed that the steam/biomass ratio must be handled with caution, as an increase in this ratio raises the concentration of H₂ and CO₂, but at the same time, it can reduce the calorific value of the gas, thereby decreasing the efficiency of the system.

The ER (equivalence ratio) factor also plays a crucial role in biomass gasification, as it increases the production of CO₂.

It is recommended that the moisture content of the biomass not exceed 10%, as higher moisture content reduces the heating value of the gas [31].

Lignocellulosic biomass from forest residues offers the best gasification material; however, this does not exclude other types of biomass, such as sewage sludge and agricultural residues. When including biomass pretreatments, such as grinding and drying, these other types of biomass could also yield promising results.

8. Conclusions

Gasification processes are critical components of modern energy and chemical production systems, and the use of the Gibbs reactor in Aspen Plus represents a valuable tool in their simulation and analysis. This reactor finds particular utility in scenarios where the temperature is known but the pressure and stoichiometry are uncertain, reflecting the inherent complexities of gasification. Despite the challenges posed, many researchers favor this reactor for its ability to approximate the gasification process and provide valuable insights into system behavior.

However, it is important to recognize the limitations of this approach. The idealized conversion of biomass into CO, CO₂, H₂, and CH₄ may not fully capture the diverse range of reactions and products observed in real-world gasification. Although the kinetic method would be more realistic, uncertainties persist regarding some reactions and their kinetics. Simulating gasification through empirical correlations of yields is attractive for its ability to provide reliable results for economic evaluations and product synthesis. However, this approach limits the evaluation of gasification performance to correlated variables, without the ability to modify other parameters, such as reactor specifications. Despite these limitations, it provides precise and reliable results, effectively allowing the representation of the amount of tar obtained—a challenging task with the Gibbs reactor.

Processes such as tar cracking, absorption towers for CO₂ capture, and the use of CO₂ as a gasification agent present viable strategies to improve gasification efficiency and reduce environmental impact. The integration of synthetic gas valorization pathways underscores the potential of gasification not only to produce clean energy but also to serve as a platform for sustainable chemical synthesis.

While challenges persist, the use of Aspen Plus has emerged as a valuable approach for simulating gasification processes. By addressing its limitations and leveraging emerging technologies and methodologies, we can continue to drive innovation and progress in the field of gasification, paving the way towards a more sustainable and efficient energy future.

This review summarizes biomass gasification simulations in Aspen Plus, focusing on key process stages. For future studies, it is recommended to conduct more detailed analyses of simulation approaches and their deviation from experimental results. A thorough examination of the validation methods used by authors is also suggested. While this review describes common methods for the removal of tar and CO₂ in Aspen Plus, further research is needed to better understand their modeling. Additionally, studies are recommended on synthesis gas applications, such as electricity production and hydrogen production.