Valorization of Sugarcane Bagasse in Thailand: An Economic Analysis of Ethanol and Co-Product Recovery via Organosolv Fractionation

Abstract

A comprehensive techno-economic assessment was undertaken to determine the viability of bioethanol production from sugarcane bagasse in Thailand through organosolv fractionation, incorporating three distinct catalytic systems: sulfuric acid, formic acid, and sodium methoxide. Rigorous process simulations were executed using Aspen Plus, facilitating the derivation of detailed mass and energy balances, which served as the foundational input for downstream cost modeling. Economic performance metrics, including the total annualized cost and minimum ethanol selling price, were systematically quantified for each scenario. Among the evaluated configurations, the formic acid-catalyzed organosolv system exhibited superior techno-economic attributes, achieving the lowest unit production costs of 1.14 USD/L for ethanol and 1.84 USD/kg for lignin, corresponding to an estimated ethanol selling price of approximately 1.14 USD/L. This favorable outcome was attained with only moderate capital intensity, indicating a well-balanced trade-off between operational efficiency and investment burden. Conversely, the sodium methoxide-based process configuration imposed the highest economic burden, with a TAC of 15.27 million USD/year, culminating in a markedly elevated MESP of 5.49 USD/kg (approximately 4.33 USD/L). The sulfuric acid-driven system demonstrated effective delignification performance. Sensitivity analysis revealed that reagent procurement costs exert the greatest impact on TAC variation, highlighting chemical expenditure as the key economic driver. These findings emphasize the critical role of solvent choice, catalytic performance, and process integration in improving the cost-efficiency of lignocellulosic ethanol production. Among the examined options, the formic acid-based organosolv process stands out as the most economically viable for large-scale implementation within Thailand’s bioeconomy.

1. Introduction

The global energy crisis is intensifying due to the depletion of non-renewable fossil fuels such as petroleum and crude oil. This challenge is aggravated by rising energy demand across all sectors, leading to a growing supply–demand imbalance. In Thailand, commercial energy consumption in 2022 averaged 1.52 million barrels of crude oil equivalent per day, an increase of 9.3% from the previous year. Refined petroleum products accounted for 53% of total consumption, while refined oil usage reached 137 million liters per day. Notably, oil imports surged by 123.9% to 12 million liters per day [1].

Heavy reliance on fossil fuels threatens energy security and contributes to environmental issues such as greenhouse gas emissions and air pollution. In response, countries including Thailand are increasingly turning to renewable energy. Among various options, lignocellulosic biomass from agricultural residues has emerged as a promising and sustainable feedstock, offering both environmental and energy benefits [2,3].

In agricultural countries such as Thailand, the widespread cultivation of economic crops generates a substantial amount of biomass waste. This has attracted significant research interest in utilizing such waste materials, particularly for the production of bioethanol, a clean and sustainable alternative energy source that can effectively replace fossil fuels [4]. Agricultural biomass residues are typically rich in carbohydrates, making them a low-cost and suitable feedstock for bioethanol production through fermentation processes. Yeast, a type of microorganism, functions as an enzymatic biocatalyst in these processes, facilitating the conversion of sugars into ethanol through fermentation.

The sugar industry is a key contributor to Thailand’s economy, supporting both domestic consumption and exports. As the world’s second-largest sugar exporter, Thailand generated an average annual value of USD 3222 million between 2011 and 2015. In addition to sugar, by-products such as bagasse serve as valuable feedstock for renewable energy applications, including electricity and ethanol production. Consequently, sugarcane is regarded as a high-potential biomass resource. According to the Department of Alternative Energy Development and Efficiency (DEDE), Thailand had a sugarcane surplus of 44.69 million tons in 2023, with bagasse volume projected to reach 52.70 million tons by 2027 [5]. This underscores the increasing availability of biomass for future energy production.

The production of bioethanol from biomass generally involves four key steps: (1) biomass fractionation, (2) hydrolysis, (3) fermentation, and (4) purification. Most research efforts to date have focused primarily on laboratory-scale investigations, whereas process design, synthesis, and industrial-scale simulation remain relatively underexplored [6]. Among these stages, biomass fractionation is recognized as the most energy-intensive step in the bioethanol production process. Consequently, it has emerged as a critical target for optimization, particularly in the context of scaling up to commercial-level operations [7].

The conducted study addresses knowledge gaps by evaluating the techno-economic feasibility of industrial-scale biomass fractionation. A key determinant of economic viability is the efficiency of bioenergy production from biomass feedstocks [8,9]. Among available technologies, organosolv fractionation has gained attention for its ability to deconstruct lignocellulosic structures through solvent penetration into the biomass matrix. Organic solvents such as alcohols (e.g., methanol (CH₃OH) and ethanol (C₂H₆O)), esters, and ketones have been explored for their effectiveness in disrupting biomass and separating key components. The success of lignin and hemicellulose removal largely depends on the solvent type used.

Furthermore, the ability to efficiently isolate cellulose from the lignocellulosic structure directly affects the amount of glucose that can be obtained during the subsequent hydrolysis step, which is a key precursor for ethanol production through fermentation. The amount of ethanol produced is directly correlated with the amount of glucose fed into the process. Theoretically, 1 g of glucose can yield a maximum of approximately 0.51 g of ethanol, equivalent to 51% of the glucose mass [10]. Therefore, the more effectively lignin and hemicellulose are removed, the more accessible the cellulose becomes to enzymatic hydrolysis, resulting in more complete saccharification and higher ethanol yields, potentially approaching the theoretical maximum. These factors are critical for evaluating the technical and economic viability of the process at an industrial scale.

Biomass fractionation is a critical step in reducing the recalcitrance of lignocellulosic structures, with the primary objective of removing lignin and hemicellulose. This structural deconstruction enhances cellulose accessibility to enzymes, thereby improving hydrolysis efficiency and increasing the yield of fermentable sugars and ethanol. Numerous studies have investigated various solvent–catalyst systems to evaluate process performance and industrial applicability. For instance, Weerasai et al. [11] reported that using CH₃ONa in CH₃OH as a basic catalyst in the organosolv process resulted in lignin removal of 86.5% and a glucose yield of 83.9%, although the process is constrained by high chemical costs and the need for precise reaction control. Alternatively, Suriyachai et al. [12] demonstrated that formic acid (CH₂O₂)-based organosolv pretreatment improved cellulose purity and hydrolysis efficiency. CH₂O₂ also offers advantages in terms of recyclability and environmental friendliness, though improper reaction conditions may lead to cellulose degradation. Panakkal et al. [13] investigated sulfuric acid (H₂SO₄) hydrolysis of sugarcane bagasse and identified optimal conditions at 3.50% acid concentration, 136.08 °C, and 75.36 min, yielding a maximum reducing sugar output of 180.15 mg/g, 3.06 times higher than that of untreated biomass. While H₂SO₄ is cost-effective and widely available, it poses challenges such as inhibitor formation, corrosiveness, and the requirement for intensive wastewater treatment. In addition, Kreetachat et al. [14] studied H₂SO₄-catalyzed Liquid Hot Water (LHW) pretreatment combined with Simultaneous Saccharification and Fermentation (SSF) for ethanol production from sweet sorghum stalk. Using the Response Surface Methodology (RSM), the optimal pretreatment conditions were determined to be 110 °C, 90 min, and 0.9% H₂SO₄, resulting in a glucose yield of 91.09% and a maximum ethanol concentration of 23.1 g/L after 72 h of fermentation.

In conventional organosolv processes, H₂SO₄ is widely used for its strong catalytic effect in disrupting lignocellulosic structures [14]. However, its corrosiveness and the high cost of wastewater treatment limit its industrial viability [15]. As a greener alternative, organic acids like CH₂O₂ are less corrosive, recyclable, and gentler on equipment. Yet, their lower acidity can reduce lignin and hemicellulose removal efficiency [16]. Among alkaline catalysts, CH₃ONa has shown strong potential for improving cellulose purity and offers cost benefits, making it a promising substitute for conventional NaOH in specific fractionation applications [11]. In summary, biomass fractionation using various solvents and catalysts offers both benefits and challenges that require thorough technical and economic evaluation [17,18]. The choice of solvent system, i.e., acidic, alkaline, organosolv-based, or ionic liquids, should balance deconstruction efficiency with solvent recovery and reuse potential [19]. Among these, organosolv fractionation stands out for effectively removing lignin while preserving cellulose and hemicellulose. Its ability to recover and recycle solvents helps reduce both operational costs and environmental impact, making it a promising and economically viable approach for large-scale biomass processing [19].

Previous research on the use of various solvents for biomass fractionation has highlighted both advantages and limitations in terms of process efficiency, cost, and environmental impact. Therefore, process simulation at the industrial scale is essential for evaluating the techno-economic feasibility of biomass fractionation, providing a comprehensive understanding of both technical and economic aspects [20,21,22,23,24]. Process simulation not only facilitates process optimization and cost reduction but also serves as a valuable tool for assessing the economic viability of bioethanol production. By analyzing the overall system, it is possible to identify cost-intensive steps and explore alternative strategies to minimize total expenses. Furthermore, selecting the most appropriate approach should also account for environmental impacts to ensure the long-term sustainability of biomass fractionation processes [24,25,26].

This study investigates the development of organosolv-based fractionation of sugarcane bagasse, an agricultural residue from Thailand’s sugar industry, for bioethanol production. The main objective is to evaluate the economic feasibility of the process using laboratory-scale data. Experiments compared catalytic systems (H₂SO₄, CH₂O₂, and CH₃ONa) and optimized operating conditions for each. The resulting data and key assumptions were scaled up to estimate industrial performance. Mathematical modeling was then applied to assess both technical and economic viability, supporting decision-making for potential commercial-scale implementation.

2. Materials and Methods

2.1. Process Synthesis and Design

The present study applied mathematical modeling and process simulation using Aspen Plus v11 (Aspen Technology, Inc., Bedford, MA, USA) to examine the fractionation of sugarcane bagasse, a key agricultural residue in Thailand. Aspen Plus is an industry-standard tool widely used by institutions like the National Renewable Energy Laboratory (NREL) for biorefinery design. Its strong thermodynamic features, especially the Non-Random Two-Liquid (NRTL) model, support accurate simulation of non-ideal liquid systems in biomass pretreatment. The software’s extensive property database, unit operations, and flexible modeling environment allow for scalable process development and detailed techno-economic analysis, enhancing the reliability of the results. The chemical composition (

Table 1. Comparison of chemical compositions of sugarcane bagasse from different countries.

Composition	Brazil [28]	China [29]	Thailand [11] *
Cellulose	42.19	39.52	38.30
Xylan	27.60	25.63	20.70
Lignin	21.56	30.36	23.70
Ash	2.84	1.45	4.20
Other	5.63	1.72	13.00

Note: * This research is based on the following data.

) with comparison of sugarcane bagasse in different countries was analyzed based on prior studies conducted by Suriyachai et al. [12] and Weerasai et al. [11]. This case study was divided into three categories according to the type of catalyst used in the organosolv fractionation process: (1) H₂SO₄-based organosolv (Industrial-grade, Qingdao Hisea Chem Co., Ltd., Qingdao, China), (2) CH₂O₂-based organosolv (Industrial-grade, Feicheng Acid Chemicals, Feicheng, China), and (3) CH₃ONa-based organosolv (CH₃ONa-based organosolv (Zhengzhou Clover Chemical Co., Ltd., Zhengzhou, China). All simulations were performed using the NRTL thermodynamic model for phase equilibrium calculations. Component property data were sourced from the Aspen Plus database and supplemented with information from NREL to ensure greater simulation accuracy [27].

The Non-Random Two-Liquid (NRTL) model is commonly applied to estimate activity coefficients, especially in liquid–liquid (LLE) and vapor–liquid equilibrium (VLE) systems [30]. Its strength lies in capturing non-ideal behavior by accounting for molecular interactions and the uneven distribution of molecules within the liquid phase. This makes it particularly effective for systems with strong polar interactions or non-random mixing. The activity coefficient of each component in a multicomponent mixture is calculated using the NRTL equations, as illustrated in Equation (1).

$$ln{\gamma }_i={\mathbf{\sum }}_{j=1}^N{\displaystyle \frac{{\tau }_{\mathit{ji}}{G}_{\mathit{ji}}{x}_j}{{\mathbf{\sum }}_{k=1}^N{G}_{\mathit{ki}}{x}_k}}+{\mathbf{\sum }}_{j=1}^N{\displaystyle \frac{x_j{G}_{\mathit{ij}}}{{\sum }_{l=1}^N{G}_{\mathit{li}}{x}_l}}\left[{\tau }_{\mathit{ij}}-{\displaystyle \frac{{\sum }_{m=1}^N{\tau }_{\mathit{mj}}{G}_{\mathit{mj}}{x}_m}{{\sum }_{k=1}^N{G}_{\mathit{kj}}{x}_k}}\right]$$

(1)

where

• ${\tau }_{\mathit{ij}} ={\displaystyle \frac{g_{\mathit{ij}} - g_{\mathit{jj}}}{\mathit{RT}}}$ is the interaction parameter between components i and j;
• $G_{\mathit{ij}} =e^{-{\alpha }_{\mathit{ij}} {\tau }_{\mathit{ij}}}$ is a weighting factor;
• x_j is the mole fraction of component j;
• α_ij is a non-randomness parameter (typically between 0.2 and 0.47).

In the NRTL equation provided, k and m are dummy indices used for summation over components in the system:

• k typically indexes all components in the denominators to normalize the interactions with respect to component i or j;
• m is used similarly to k but specifically for summing interactions between component j and all other components m (inside the inner bracket) in the second term of the equation.

• Roles:

k appears in

• $\sum _{k=1}^N{G}_{\mathit{ki} }{x}_k$: Denominator of the first term (normalizing interaction contributions to component i);
• $\sum _{k=1}^N{G}_{\mathit{kj} }{x}_k$: Denominator inside the brackets in the second term (normalizing interactions related to component j).

m appears in

• $\sum _{m=1}^N{\tau }_{\mathit{mj} }{G}_{\mathit{mj}} x_m$: Numerator inside the bracket (weighted average interaction toward j from all m).

In essence, k and m are used to iterate over all components in the mixture, similar to j, but serve distinct roles depending on where they appear in the formula.

2.2. Process Setup for Organosolv Fractionation

The biomass feed rate was set at 20,000.00 kg/day, and the solvent recycling efficiency was assumed to be 95%. Figure 1a,b presents the process flowsheet for biomass fractionation using the organosolv technique, employing sugarcane bagasse an agricultural residue from Thailand’s sugar industry as the primary feedstock.

The diagram outlines the ethanol production process from sugarcane bagasse, involving a series of integrated unit operations. The process starts with the introduction of sugarcane bagasse (BAG) and catalysts into reactor R1, a Recirculating Continuous Stirred-Tank Reactor (RCSTR), which ensures uniform mixing and enhances reaction efficiency. The output from R1 proceeds to unit F1, a membrane filter press, for solid–liquid separation. The resulting cellulose pulp (PULP) is transferred to the HYDRO unit, an agitated hydrolysis reactor with temperature and pH control, where water (WT-RH) is added to depolymerize cellulose into glucose. The glucose solution is then sent to the FERMEN unit for anaerobic fermentation with yeast (YEAST), converting glucose into ethanol and CO₂. Ethanol is collected in the END unit, while the liquid stream from F1 goes to the distillation column (F2) for solvent recovery. Recovered ethanol is partially recycled (S-RCY), and the residue is processed in the PRECIP unit to extract primary solids (P-SOLID). The remaining liquid is concentrated in the EVAP unit to recover additional solids (RE-SOLIDs), followed by filtration (FILTER) to isolate lignin (LIGNIN) as a by-product. The leftover sugar solution is directed to the SUGAR unit for further use. In the HYDRO unit, cellulose (C₆H₁₀O₅)n is hydrolyzed with water to produce glucose (C₆H₁₂O₆), as represented in Equation (2).

$${\left({\mathrm{C}}_6{\mathrm{H}}_{10}{\mathrm{O}}_5\right)}_n+n{\mathrm{H}}_2\mathrm{O} \to n{\mathrm{C}}_6{\mathrm{H}}_{12}{\mathrm{O}}_6$$

(2)

From the hydrolysis process, 1 g of cellulose can theoretically produce around 1.11 g of glucose due to water incorporation during the reaction. However, actual yields typically range between 80% and 95%, influenced by the catalyst type and operating conditions. The glucose-rich hydrolysate is then fed into the FERMEN unit, an anaerobic fermentation reactor operating at 30–35 °C. Yeast is added to convert glucose into ethanol and carbon dioxide, as shown in Equation (3).

$${\mathrm{C}}_6{\mathrm{H}}_{12}{\mathrm{O}}_6 \to 2{\mathrm{C}}_2{\mathrm{H}}_5\mathrm{OH}+2{\mathrm{CO}}_2+\mathrm{A}\mathrm{T}\mathrm{P}$$

(3)

The final product obtained from the ENDPRO unit is ethanol, which is ready for subsequent separation and purification processes.

Scenario 1 involves organosolv fractionation using a 70:30%v/v ethanol–water solvent mixture, with 2% w/v H₂SO₄ as the catalyst. The process was carried out at 170 °C, 20 bar, and a 60-min residence time. Scenario 2 depicts organosolv fractionation using CH₂O₂ as the catalyst. The reactor conditions were 159 °C, 20 bar, and a 40-min residence time. The solvent mixture comprised water, ethanol, ethyl acetate, and CH₂O₂ in a 43:20:16:21%v/v ratio [12]. Scenario 3 describes organosolv fractionation using 5.1% w/v CH₃ONa as the catalyst. The process runs at 150 °C, 20 bar, with a residence time of 63.9 min [11]. The current study assumes 7920 h of annual plant operation and a 95% solvent recycling rate, a common value in Organosolv processes. Reported recycling efficiencies vary, with studies noting values as high as 99% [31] and as low as 68.9% [32]. Efficient solvent recycling is vital for lowering costs and environmental impact, though repeated use may degrade performance. Thus, advancing solvent regeneration methods and improving biomass preparation are key to enhancing process efficiency and economic viability.

In the Organosolv process, lignin is efficiently extracted from lignocellulosic biomass through the use of organic solvents such as ethanol, methanol, or formic acid in combination with water and either acidic or alkaline catalysts. This process facilitates the cleavage of lignin–carbohydrate linkages within the plant cell wall matrix, particularly targeting the β-O-4 ether bonds, which are the most abundant interunit linkages in native lignin. Under acidic conditions, the chemical mechanism of lignin depolymerization begins with the protonation of the hydroxyl group at the α-position (α-OH), leading to the formation of a stable benzylic carbocation intermediate. This intermediate plays a crucial role in promoting the cleavage of the β-O-4 ether bond. The representative reaction mechanism is shown as follows (Equation (4)):

Ar–CH(OH)–CH₂–O–Ar′ + H⁺ → Ar–CH⁺–CH₂–O–Ar′ → Ar–CHO + HO–CH₂–Ar′

(4)

In this equation, Ar and Ar′ represent aromatic rings in the lignin polymer; CH(OH) denotes the hydroxyl group at the α-position; and CH₂–O corresponds to the β-O-4 ether linkage. The products Ar–CHO and HO–CH₂–Ar′ are aromatic aldehydes and alcohols, respectively, indicating the effective depolymerization of lignin. Once dissolved into the solvent phase, lignin can be recovered by precipitation through water addition or solvent evaporation. The resulting lignin is characterized by its sulfur-free composition, low ash content, and low molecular weight, making it highly suitable for the production of high-value bioproducts such as resins, carbon fibers, and bio-based composite materials. Nevertheless, the Organosolv process presents economic limitations, primarily due to the high cost of organic solvents and the need for efficient solvent recovery systems to ensure the process’s overall economic and environmental sustainability [33].

2.3. Economic Analysis

The factorial estimation method, utilizing parameters specified by Gavin and Ray [34], was employed to calculate the costs associated with both liquid and solid processing systems in this analysis. All cost calculations were conducted using United States dollars as the base currency as shown in

Table 2. Feedstock, chemicals, and utilities prices.

Input	Unit	Price	Reference
Bagasse	USD/ton	14.00	[35]
C2H6O	USD/L	~0.80	[36]
H2O	USD/L	~0.00028	[37]
H2SO4	USD/L	~0.16	[36]
CH2O2	USD/L	~0.49	[36]
C4H8O2	USD/L	~1.30	[36]
CH3ONa	USD/kg	~0.60	[36]
CH3OH	USD/L	~0.28	[36]
Electricity	USD/KV	2.1	[38]

.

The total annual cost (TAC), calculated using the Capital Recovery Factor (CRF), is a common method in techno-economic analysis (TEA) to estimate the annualized cost of industrial-scale processes, as outlined in Equation (5).

$$\mathit{TAC}=\left(\mathit{TCI} \times \mathit{CRF}\right)+\mathit{OC}$$

(5)

where TCI is Total Capital Investment, which includes the cost of plant construction, equipment, and installation. OC refers to the Operating Cost, which encompasses annual expenses such as raw materials, chemicals, energy, and labor. The Capital Recovery Factor (CRF) serves to convert a one-time initial investment into an equivalent annual cost that accounts for the time value of money or the expected rate of return (e.g., 10% per year), as shown in Equation (6).

$$\mathit{CRF}={\displaystyle \frac{{i(1+i) }^n}{{(1+i) }^n-1}}$$

(6)

where

• i = interest rate or desired rate of return (per year);
• n = project lifetime or payback period (years).

2.4. Sensitivity Analysis

Sensitivity analysis helps assess a project’s robustness by examining how cost and performance respond to changes in key variables. This includes evaluating the economic impact of variations in biomass characteristics and catalyst reaction rates, with a particular focus on the effects of process parameter shifts. The analysis also considers future technological developments, focusing on factors like raw material properties, chemical prices, and utility costs. The key variables tested include solvent type, catalyst amount, temperature, and pressure, with each adjusted independently to determine its specific influence on system performance [39,40,41].

3. Results

3.1. Scenario

Based on an initial sugarcane bagasse feedstock of 20,000.00 kg/day (6,600,000 kg/year), the simulation results from the organosolv fractionation process across three scenarios demonstrate the distribution of chemical components within the product stream. Sugarcane bagasse consists of 38.30% cellulose (2,527,800 kg/year), 20.70% hemicellulose (1,359,600 kg/year), 23.70% lignin (1,564,200 kg/year), 4.20% ash (277,200 kg/year), and 13.00% other components (871,200 kg/day). The chemical composition of sugarcane bagasse (Table 1) influences the techno-economic performance of the biorefinery process. Variations in cellulose, hemicellulose, and lignin contents affect conversion yields, utility requirements, and co-product value. Higher cellulose content enhances fermentable sugar yield and product revenue, while increased lignin may contribute to energy recovery or elevate residue management costs. Although moderate, these compositional differences impact mass balances, equipment sizing, and operating costs. Accordingly, TEA outcomes must be evaluated in the context of the specific compositional characteristics of the feedstock used in each scenario.

For Scenario 1,

Table 3. Mass balance for organosolv fractionation experiments in three scenarios.

	Units	Bagasse	Downstream	Lignin	Sugar	Pulp	to Fermentation	Product
Scenario 1; Organosolv Fractionation by H2SO4
Cellulose	kg/year	2,797,815.00	213,473.28	196,395.42	17,077.86	2,584,341.72	516,868.34	516,868.34
Xylan	kg/year	1,504,830.00	1,449,452.26	246,406.88	1,203,045.37	55,377.74	55,377.74	55,377.74
Lignin	kg/year	1,731,285.00	1,572,353.04	1,430,841.26	141,511.77	158,931.96	158,931.96	158,931.96
Ash	kg/year	306,810.00	253,670.51	-	253,670.51	53,139.49	53,139.49	53,139.49
Extractives	kg/year	964,260.00	904,668.73	-	904,668.73	59,591.27	59,591.27	59,591.27
Enzyme	kg/year	-	-	-	-	-	-	-
Ethanol	kg/year	-	2,041,590.44	-	3583.91	-	-	1,057,365.16
Co2	kg/year	-	-	-	-	-	-	1,010,101.99
Glucose	kg/year	-	-	-	-	-	2,297,087.79	229,708.78
Water	kg/year	-	1,089,128.38	-	155,788.26	-	8,536,287.65	10,603,698.80
Xylose	kg/year	-	-	-	-	-	-	-
Yeast	kg/year	-	-	-	-	-	-	5,823,742.37
H2SO4	kg/year	-	73,050.00	-	73,048.73	-	-	-
CH2O2	kg/year	-	-	-	-	-	-	-
CH3OH	kg/year	-	-	-	-	-	-	-
Scenario 2; Organosolv fractionation by CH2O2
Cellulose	kg/year	2,797,815.00	153,320.26	141,054.64	12,265.62	2,644,494.74	528,898.95	528,898.95
Xylan	kg/year	1,504,830.00	1,351,487.82	229,752.93	1,121,734.89	153,342.18	153,342.18	153,342.18
Lignin	kg/year	1,731,285.00	1,402,513.98	1,276,287.72	126,226.26	328,771.02	328,771.02	328,771.02
Ash	kg/year	306,810.00	175,311.23	-	175,311.23	131,498.77	131,498.77	131,498.77
Extractives	kg/year	964,260.00	869,280.39	-	869,280.39	94,979.61	94,979.61	94,979.61
Enzyme	kg/year	-	-	-	-	-	-	-
Ethanol	kg/year	-	583,311.56	-	-	-	-	1,081,976.34
Co2	kg/year	-	-	-	-	-	-	1,033,613.08
Glucose	kg/year	-	-	-	-	-	2,350,554.71	235,055.47
Water	kg/year	-	1,561,084.02	-	-	-	8,530,940.88	10,646,472.99
Xylose	kg/year	-	-	-	-	-	-	-
Yeast	kg/year	-	-	-	-	-	-	5,775,662.21
H2SO	kg/year	-	-	-	-	-	-	-
CH2O2	kg/year	-	709,162.09	-	-	-	-	-
CH3OH	kg/year	-	687,926.46	-	-	-	-	-
Scenario 3; Organosolv fractionation by CH3ONa
Cellulose	kg/year	2,797,815.00	219,068.91	201,543.40	17,525.51	2,578,746.09	515,749.22	515,749.22
Xylan	kg/year	1,504,830.00	423,760.13	72,039.22	351,720.91	1,081,069.87	1,081,069.87	1,081,069.87
Lignin	kg/year	1,731,285.00	1,497,561.53	1,362,780.99	134,780.54	233,723.48	233,723.48	233,723.48
Ash	kg/year	306,810.00	87,655.62	-	87,655.62	219,154.38	219,154.38	219,154.38
Extractives	kg/year	964,260.00	547,892.53	-	547,892.53	-	416,367.47	416,367.47
Enzyme	kg/year	-	-	-	-	-	-	-
Ethanol	kg/year	-	-	-	-	-	-	1,055,075.74
Co2	kg/year	-	-	-	-	-	-	1,007,914.91
Glucose	kg/year	-	-	-	-	-	2,292,114.12	229,211.41
Water	kg/year	-	-	-	-	-	8,536,785.02	10,599,719.80
Xylose	kg/year	-	-	-	-	-	-	-
Yeast	kg/year	-	-	-	-	-	-	5,828,214.94
H2SO4	kg/year	-	-	-	-	-	-	-
CH2O2	kg/year	-	-	-	-	-	-	-
CH3OH	kg/year	-	2,896,143.95	-	-	-	-	-
CH3ONa	kg/year	-	365,250.00	-	-	365,250.00	-	-

presents the mass balance of the organosolv fractionation process using H₂SO₄ as the catalyst. From the sugarcane bagasse conditioning step, it was found that the solid stream (Pulp), which proceeds to hydrolysis and fermentation for ethanol production, contained 2,584,341.72 kg/year of cellulose, 55,377.74 kg/year of hemicellulose (xylan), 158,931.96 kg/year of lignin, 53,139.49 kg/year of ash, and small amounts of other components. Based on these figures, the organosolv process using H₂SO₄ achieved a cellulose recovery efficiency of 92.37% and a lignin removal rate of 90.82%. When compared to the laboratory-scale results reported by [42], which documented cellulose recovery as high as 99%, the simulation results showed slightly lower performance. However, the lignin removal rate in this simulation exceeded the reported 86.4% in the same study. These differences highlight the inherent variability between laboratory-scale experiments and industrial-scale simulations, which can arise from variations in operating conditions, process scale, and technical limitations affecting biomass fractionation efficiency.

In the fermentation stage, the fermentation stream received 2,297,087.79 kg/year of glucose from hydrolysis and produced 1,057,365.16 kg/year of ethanol from the Product unit. This corresponds to a glucose-to-ethanol conversion efficiency of approximately 46.04% by mass, which is lower than the theoretical value of around 51% reported by [10]. The loss of glucose may be attributed to its conversion into by-products such as organic acids or other water-soluble compounds, reduced yeast effectiveness due to fermentation inhibitors generated during the fractionation process, or ethanol losses during separation and distillation.

Although the organosolv fractionation process using H₂SO₄ demonstrated high cellulose recovery and lignin removal efficiencies, the ethanol production outcomes suggest potential areas for improvement, particularly in enhancing fermentation performance and minimizing inhibitor formation. These improvements are essential to increase the overall glucose-to-ethanol conversion efficiency in commercial-scale biorefinery systems.

For Scenario 2, Table 3 illustrates the mass balance of the organosolv fractionation process using CH₂O₂ as the catalyst. During the sugarcane bagasse conditioning step, the solid stream (Pulp) designated for subsequent hydrolysis and fermentation consisted of 2,644,494.74 kg/year of cellulose, 153,342.18 kg/year of hemicellulose (xylan), 328,771.02 kg/year of lignin, 131,498.77 kg/year of ash, and 94,979.61 kg/year of extractives. In summary, organosolv fractionation using CH₂O₂ achieved a cellulose recovery efficiency of 94.52% and a lignin removal rate of 81.01%. The simulation results indicate that the cellulose recovery rate is nearly identical to that reported in the study by [12], which observed a recovery of 94.6%. However, the lignin removal in this simulation was slightly higher, 81.01% compared to 80.4%, in the same study. These differences are likely due to process efficiency variations under simulated industrial-scale conditions.

Based on the simulation results, the fermentation unit (to Fermentation) received 2,350,554.71 kg/year of glucose from the hydrolysis stage and produced 1,081,976.34 kg/year of ethanol in the Product unit. This corresponds to a glucose-to-ethanol conversion efficiency of approximately 46.03% by mass, which is similar to that in Scenario 1 but remains below the theoretical maximum of 51.14%. The lower efficiency suggests that part of the glucose was lost during fermentation, likely due to the formation of metabolic by-products, the presence of fermentation inhibitors generated during pretreatment, or ethanol losses in downstream separation and purification processes. Notably, the solid stream (Pulp) in this scenario contained more cellulose (2,644,494.74 kg/year) compared to Scenario 1, suggesting that CH₂O₂ is more effective at preserving cellulose structure under the simulated conditions. However, the lignin content in the Pulp stream was also significantly higher (328,771.02 kg/year), indicating a lower lignin removal efficiency compared to the H₂SO₄-based process. The residual lignin may hinder enzyme accessibility and reduce hydrolysis efficiency in the subsequent steps. Therefore, while the process in Scenario 2 demonstrates improved cellulose preservation, its limitations in lignin removal and suboptimal glucose-to-ethanol conversion efficiency highlight key areas for further optimization, particularly if the process is to be scaled up for commercial biorefinery applications.

For Scenario 3, Table 3 presents the mass balance of the organosolv fractionation process using CH₃ONa as the catalyst. During the sugarcane bagasse conditioning step, the solid stream (Pulp), which proceeds to hydrolysis and fermentation for ethanol production, yielded the following component quantities: cellulose (2,578,746.09 kg/year), hemicellulose (1,081,069.87 kg/year), lignin (233,723.48 kg/year), ash (219,154.38 kg/year), and other extractive components (416,367.47 kg/year). In summary, organosolv pretreatment with CH₃ONa achieved a cellulose recovery of 92.17% and a lignin removal efficiency of 86.50%. The simulation results indicate that cellulose recovery is slightly lower than the experimental value of 93.1% reported in [11]. However, both the simulation and experimental results demonstrate the same lignin removal efficiency of 86.5%, suggestive of a consistent and stable performance of the pretreatment process under these conditions.

Based on the simulation results of Scenario 3, which utilizes CH₃ONa and methanol as the solvent system in the organosolv fractionation process, the solid stream (Pulp) contained 2,578,746.09 kg/year of cellulose and 233,723.48 kg/year of residual lignin. Notably, the hemicellulose (xylan) content reached 1,081,069.87 kg/year, which is higher than in both Scenario 1 and Scenario 2. This indicates that this solvent system offers superior preservation of total carbohydrate components, especially hemicellulose, which tends to degrade more easily in other pretreatment conditions.

Furthermore, the fermentation unit received 2,292,114.13 kg/year of glucose from the hydrolysis stage and yielded 1,055,075.74 kg/year of ethanol in the final product stream, corresponding to a glucose-to-ethanol conversion efficiency of approximately 46.04%. This value is comparable to those observed in Scenarios 1 and 2 but remains below the theoretical maximum of 51.14%, implying partial glucose loss. The reduction in fermentation efficiency could be attributed to the formation of by-products or the presence of residual chemicals, such as methanol or sodium salts, which may inhibit yeast activity during fermentation. Overall, Scenario 3 demonstrates an effective recovery of carbohydrate components, particularly hemicellulose, while maintaining high cellulose retention. However, its lignin removal efficiency is lower compared to Scenario 1. Nevertheless, the potential impact of chemical residues on fermentation and downstream ethanol separation must be carefully evaluated if this system is to be developed for industrial-scale applications.

Simulation outcomes from the three organosolv fractionation scenarios—using H₂SO₄ (Scenario 1), CH₂O₂ (Scenario 2), and CH₃ONa with methanol (Scenario 3)—revealed distinct trade-offs in biomass recovery and ethanol production. Scenario 1 achieved the highest lignin removal (90.82%) but had lower hemicellulose retention. Scenario 2 yielded the highest cellulose recovery (2.64 million kg/year), though with slightly reduced lignin removal (81.01%). Scenario 3 offered the most balanced recovery, with the highest hemicellulose retention (1.08 million kg/year), high cellulose yield, and 86.50% lignin removal. Despite these differences, all scenarios achieved similar glucose-to-ethanol conversion efficiencies (~46%), indicating that fermentation performance may be more constrained by inhibitors than by pretreatment differences. Optimization should thus target both carbohydrate recovery and inhibitor minimization. Each scenario presents specific advantages: Scenario 1 in lignin removal, Scenario 2 in cellulose yield, and Scenario 3 in overall carbohydrate preservation.

3.2. Economic Evaluation

Table 4. TAC breakdown for the organosolv fractionation processes.

Cost Analysis	Unit	Scenario 1	Scenario 2	Scenario 3
Total Capital Cost	USD	4,358,930	3,671,550	3,640,210
Total Operating Cost	USD/Year	3,368,230	3,183,650	14,526,300
Total Raw Materials Cost	USD/Year	1,957,890	1,791,750	12,292,500
Total Product Sales	USD/Year	43,710,900	43,291,000	43,950,800
Total Utilities Cost	USD/Year	94,615	92,066	92,288
Desired Rate of Return	Year	20	20	20
Equipment Cost	USD	295,500	242,900	262,300
Total Installed Cost	USD	1,391,500	1,066,700	1,079,100
Electricity rate	kW	79.64	77.49	77.68
Electricity cost	USD/H	11.94	11.62	11.65
TAC	USD	4,263,365	3,927,396	15,273,841
TAC	million USD	4.26	3.92	15.27

presents the detailed costs of major equipment and operational expenses for ethanol production via the organosolv process under each scenario. It was found that Scenario 1 had the highest overall costs compared to the other scenarios, with a total capital cost of USD 4,358,930.00 and a total operating cost of USD 3,368,230.00 per year. The total raw materials cost was USD 1,957,890.00, which is slightly higher than that of Scenario 2 but significantly lower than Scenario 3. These figures suggest that the process in Scenario 1 may be more complex or require more energy-intensive equipment and a larger amount of raw materials than the other scenarios.

Scenario 2 exhibits slightly lower overall costs compared to Scenario 1, with raw materials and operating costs of USD 1,791,750.00 and USD 3,183,650.00, respectively. The total capital cost is USD 3,671,550.00. However, Scenario 2 has the highest utility cost among the three scenarios, at USD 92,066.00, which may indicate the presence of more energy-intensive process steps or greater requirements for environmental control. Scenario 3 incurred the highest total costs among all scenarios, with substantially elevated values across key cost categories. Specifically, the raw materials cost was USD 12,292,500.00, the operating cost reached USD 14,526,300.00, the capital investment cost amounted to USD 3,640,210.00, and the utilities cost was USD 92,288.00. As a result, TAC for this scenario was USD 15,273,841.00, equivalent to 15.27 million USD, representing the highest TAC among the three scenarios. Although Scenario 3 generated the highest total product sales at USD 43,950,800.00 per year, the notably high raw material and operating costs suggest a lower economic viability. This may be attributed to the use of costly chemicals or more resource-intensive process steps compared to the other scenarios.

Based on the data in Table 4, Scenario 2 demonstrates the lowest TAC, making it the most economically favorable option, despite having the highest utilities cost. Scenario 1 shows the highest capital investment, indicating a more complex process. In contrast, Scenario 3 has the highest TAC due to significantly higher raw material and operating costs, even though it yields the highest total product revenue.

Figure 2 illustrates the percentage contributions of each cost component to TAC for the three organosolv fractionation scenarios. The analysis shows that the capital cost is the largest contributor in Scenarios 1 and 2, accounting for 44.6% and 42.0%, respectively, while it is significantly lower in Scenario 3 at only 11.9%. In contrast, the operating cost becomes the dominant component in Scenario 3 (47.5%) and also contributes substantially in Scenarios 1 and 2 at 34.4% and 36.4%, respectively. The raw materials cost is highest in Scenario 3 at 40.2%, compared to 20.0% and 20.5% in Scenarios 1 and 2, respectively. The utilities cost has the least impact in all scenarios, contributing less than 1.1% across the board.

These findings confirm that operating and raw materials costs are significant contributors to TAC. Notably, the capital cost contribution observed in this work varies significantly among the scenarios, ranging from 11.9% in Scenario 3 to 44.6% in Scenario 1. In comparison, previous studies reported relatively consistent and higher capital cost shares. For example, Cheng et al. reported a capital cost share of 28.17% for the Liquid Hot Water pretreatment of sugarcane bagasse [43]. Similarly, Sganzerla et al. noted that approximately 35% of the fixed capital investment in subcritical water hydrolysis was attributed to the reactor system [44], and a 34.7% capital cost share was reported for organosolv pretreatment of olive leaves [45]. These comparisons suggest that the capital intensity in the organosolv processes evaluated in this study can vary widely depending on the specific process configuration, solvent system, and plant design, with Scenario 3 demonstrating a notably lower capital burden.

Regarding operating costs, the results show a substantial share ranging from 34.4% to 47.5%, which is slightly lower than the 54.78% previously reported for organosolv pretreatment [45]. In contrast, the raw material costs are notably higher, accounting for 20.0% to 40.2%, compared to approximately 30% reported in earlier research [46]. The increased share may be attributed to variations in solvent systems, process design, or cost assumptions applied in the simulation. In particular, the high raw material cost in Scenario 3 reflects the greater influence of input chemical prices and usage rates in that configuration.

3.3. Assessment of Sensitivity Parameters

A sensitivity analysis was conducted to evaluate the potential impacts of future technological changes on the economic viability of the organosolv biomass fractionation process, with a particular focus on TAC, which serves as a key indicator of economic feasibility at the industrial scale. In general, sensitivity analyses in process simulation commonly adopt variation ranges of ±10–30% for operating and utility costs, ±15–40% for capital investment, and up to ±50% for raw material and product prices [46,47]. However, to ensure a consistent and comparable assessment across all scenarios, a fixed variation of ±10% was uniformly applied.

As shown in

Table 5. Sensitivity analysis of price fluctuations on TAC across different scenarios.

Parameter Changed		Min	Baseline	Max	Unit	TAC (Million US$/Year)
						Min	Change (%)	Baseline	Max	Change (%)
Scenario 1; Organosolv fractionation by H2SO4 catalyst
Raw material cost	Case 1	12.84	14.00	15.70	US$/ton	4.25	−0.01	4.26	4.27	0.01
Chemicals cost	Case 2	different for water, ethanol and H2SO4				4.25	−0.01	4.26	4.27	0.01
Utilities Cost	Case 3	0.14	0.15	0.17	US$/kWhr	4.25	−0.01	4.26	4.27	0.01
Utilities consumption	Case 4	75.71	84.12	92.53	KW	4.26	0.00	4.26	4.26	0.00
Temperature change	Case 5	153.00	170.00	187.00	°C	4.26	0.00	4.26	4.26	0.00
Pressure change	Case 6	18.00	20.00	22.00	bar	4.26	0.00	4.26	4.26	0.00
Scenario 2; Organosolv fractionation by CH2O2 catalyst
Raw material cost	Case 1	12.84	14.00	15.70	US$/ton	3.91	−0.01	3.92	4.02	0.01
Chemicals cost	Case 2	different for water, ethanol and CH2O2				3.74	−0.18	3.92	4.10	0.18
Utilities Cost	Case 3	0.14	0.15	0.17	US$/kWhr	3.91	−0.01	3.92	3.93	0.01
Utilities consumption	Case 4	69.74	77.49	85.24	KW	3.92	0.00	3.92	3.92	0.00
Temperature change	Case 5	143.10	159.00	174.90	°C	3.92	0.00	3.92	3.92	0.00
Pressure change	Case 6	18.00	20.00	22.00	bar	3.91	−0.01	3.92	3.93	0.01
Scenario 3; Organosolv fractionation by CH3ONa catalyst
Raw material cost	Case 1	12.84	14.00	15.70	US$/ton	15.26	−0.01	15.27	15.28	0.01
Chemicals cost	Case 2	different for water, ethanol and CH3ONa				15.15	−0.12	15.27	15.15	0.12
Utilities Cost	Case 3	0.14	0.15	0.17	US$/kWhr	15.26	−0.01	15.27	15.28	0.01
Utilities consumption	Case 4	69.91	77.68	85.45	KW	15.27	0.00	15.27	15.27	0.00
Temperature change	Case 5	135.00	150.00	165.00	°C	15.27	0.00	15.27	15.27	0.00
Pressure change	Case 6	18.00	20.00	22.00	bar	15.27	0.00	15.27	15.27	0.00

and Figure 3, changes in chemical costs (Case 2) exerted the most substantial influence on TAC. Scenario 2, which utilized CH₂O₂, showed the highest TAC fluctuation at ±0.18%, followed by Scenario 3 (CH₃ONa) at ±0.12% and Scenario 1 (H₂SO₄) at ±0.01%. In contrast, fluctuations in raw material costs (Case 1), utility costs (Case 3), utility consumption (Case 4), operating temperature (Case 5), and pressure (Case 6) resulted in negligible changes in TAC, typically within ±0.01% or even 0.00% in several cases. Among the scenarios, Scenario 1 exhibited the highest cost stability, as all parameters resulted in TAC variation of no more than ±0.01%. These results confirm that the chemical cost is the most sensitive and economically influential parameter in organosolv fractionation, particularly when employing CH₂O₂ and CH₃ONa as catalysts.

These findings align with previous studies such as Parascanu et al. [48], who identified chemical and energy inputs as key cost drivers in lignocellulosic biorefineries, and Gadkari et al. [49], who emphasized the significance of chemical cost control in reducing the minimum product selling price. To improve the economic viability of organosolv-based biorefineries, it is recommended to adopt strategies such as low-cost or free lignocellulosic feedstock sourcing, solvent recovery optimization, and scaling up production capacity to exploit economies of scale.

Table 6. Value-based distribution of TAC for ethanol and lignin recovery.

Scenario	Ethanol Production	Lignin Production	TAC	Total Product Sales	Cost of Lignin Production	Cost of Ethanol Production
	(kg/Year)	(kg/Year)	USD/Year)	(USD/Year)	(USD/kg)	(USD/kg)	(USD/L) *
1	1,057,365	1,430,841	4,263,365	43,710,900	1.88	1.49	1.20
2	1,081,976	1,276,287	3,927,396	43,291,000	1.84	1.45	1.14
3	1,055,075	1,362,780	15,273,841	43,950,800	6.96	5.49	4.33

Note: * Data are calculated based on an ethanol density of 0.789 kg/L [27].

presents the annual ethanol production, TAC, total product sales, and fractionation costs of cellulose, lignin, and ethanol across three organosolv process scenarios. Among these, Scenario 2 demonstrates the most favorable economic performance. It achieves the highest ethanol yield at 977,555.63 kg/year and the greatest total product sales revenue of 88,138,800.00 USD/year. Although TAC of Scenario 2 (23,493,394.47 USD/year) is slightly higher than that of Scenario 3, it remains lower than Scenario 1, resulting in a competitive ethanol unit cost.

The value-based allocation approach was applied to distribute TAC between the main products, ethanol and lignin. This method allocates costs according to the economic value of each product rather than their mass proportions (as in mass-based allocation). The economic value of each product was first calculated by multiplying its annual production quantity (kg/year) by its respective market price (USD/kg). The total value was then used to determine each product’s value fraction, which was subsequently multiplied by TAC to derive the allocated cost for each product. Finally, the allocated cost was divided by the product’s annual production to obtain the unit production cost. This method improves economic accuracy, especially in cases where the co-products differ significantly in market value. It has been widely recognized in techno-economic assessments, such as the work by [50], and in life cycle analysis (LCA) studies, including that of [51], as a standard and appropriate method for cost allocation in biorefinery systems and product life cycle evaluations [52].

Table 6 presents the value-based allocation of TAC for ethanol and lignin production across three scenarios. Scenario 2 exhibits the lowest production costs for both ethanol (1.45 USD/kg) and lignin (1.84 USD/kg), while maintaining comparable total product sales to the other scenarios, indicating superior economic performance. In contrast, Scenario 3, despite achieving the highest total product sales, incurs a significantly higher TAC, resulting in markedly elevated unit production costs. Scenario 1 shows intermediate values in terms of both costs and revenues. Overall, the value-based allocation approach highlights Scenario 2 as the most cost-effective option, offering the lowest unit production costs for both primary products.

Priadi et al. [53] reported an ethanol production cost of ~1.11 USD/L using enzymatic hydrolysis, notably lower than the 1.14–4.32 USD/L range observed in Table 6. The discrepancy likely arises from differences in feedstock, process scale, and regional costs. Similarly, Gubicza et al. [54] and Kautto et al. [55] achieved lower MESPs (~1.03 and 0.81 USD/L, respectively) through optimized organosolv pretreatment and lignin valorization. These findings emphasize the impact of feedstock type, pretreatment efficiency, and co-product recovery on production economics. Using a value-based cost allocation approach, Scenario 2 showed the highest economic feasibility, with the lowest ethanol 1.14 USD/L (1.45 USD/kg) and lignin costs (1.84 USD/kg). Further cost reductions could be achieved through lignin valorization (e.g., eugenol production) and the fermentation of residual sugars such as xylose, enhancing overall resource utilization and process viability in the Thai context.

A strategic evaluation of ethanol production efficiency under the three proposed scenarios was conducted using Normalized 3D Vector Analysis in combination with heatmap visualization. This integrated approach serves as a systematic method for assessing multidimensional process performance. It applies Min–Max normalization to rescale variables with differing units into a common range (0–1), enabling equitable comparisons. The normalized values are then represented as three-dimensional vectors, $\overrightarrow{v}=\left(\acute{x,}\acute{y,}\acute{z}\right)$, typically corresponding to ethanol yield, TAC, and total product sales. The direction and magnitude of each vector reflect the overall efficiency and balance among key indicators. Simultaneously, the heatmap visualizes the normalized values using a color gradient to highlight the strengths and weaknesses of each scenario. Together, these tools facilitate a comprehensive techno-economic comparison, supporting strategic decision-making in complex biorefinery systems.

Figure 4 presents a comparative techno-economic analysis of ethanol production from sugarcane bagasse under three scenarios (Scenario 1–3). The assessment incorporates both normalized three-dimensional vector plots (ranging from 0 to 1) and heatmaps to evaluate strategic indicators, including ethanol and lignin yields, TAC, total sales revenue, lignin production cost, and ethanol production cost per unit. Based on the 3D vector analysis, Scenario 2 demonstrates the most outstanding performance, characterized by the longest vector length, indicating an optimal balance between high ethanol yield and reasonable production cost. In contrast, Scenario 1, while achieving the highest lignin output, exhibits a higher TAC and ethanol cost. Scenario 3, although offering the lowest TAC, yields minimal ethanol output, resulting in the weakest overall economic performance. The heatmap further corroborates this trend. Scenario 2 achieves superior values in key performance indicators (e.g., ethanol production, revenue, and low unit costs), whereas Scenario 3 records the lowest values in several critical dimensions. From a strategic perspective, Scenario 2 emerges as the most favorable option for ethanol production from sugarcane bagasse using the organosolv process.

4. Discussion

The evaluation of organosolv fractionation for ethanol production from sugarcane bagasse underscores the pivotal influence of catalyst and solvent selection on biomass deconstruction efficiency, ethanol yield, and overall process economics at an industrial scale. Among the three catalytic scenarios examined, distinct trade-offs were observed in terms of biomass component recovery, conversion efficiency, and economic performance.

Scenario 1, employing sulfuric acid (H₂SO₄), achieved the highest lignin removal efficiency at 90.82%, alongside a glucose-to-ethanol conversion efficiency of 46.04%, which was comparable to the other scenarios. However, the corrosive nature of H₂SO₄ and the associated environmental burdens, including intensive wastewater treatment, contributed to elevated operational and maintenance costs, resulting in a higher TAC. In contrast, Scenario 2, utilizing formic acid (CH₂O₂), demonstrated superior cellulose preservation with the highest recovery rate of 94.52%. When evaluated using a value-based cost allocation approach, Scenario 2 yielded the most economically favorable outcome, with unit production costs of 1.45 USD/kg for ethanol and 1.84 USD/kg for lignin, corresponding to approximately 1.14 USD/L of ethanol. These results, combined with moderate capital requirements, position Scenario 2 as the most viable candidate for large-scale deployment. Scenario 3, based on sodium methoxide (CH₃ONa) in methanol, exhibited the strongest performance in retaining total carbohydrates, particularly hemicellulose. Nevertheless, its economic viability was undermined by the highest TAC of 15.27 million USD/year, primarily driven by elevated chemical and operational expenditures, despite achieving the greatest total revenue from product sales.

Parametric sensitivity analysis revealed that chemical costs were the most influential drivers of TAC variability across all configurations, particularly in Scenarios 2 and 3. In contrast, other factors such as feedstock price, reaction temperature, and pressure exerted minimal economic impact. These insights reinforce the necessity of stringent chemical cost management to improve process economics. Strategic performance benchmarking using Normalized 3D Vector Analysis and heatmap visualization further validated Scenario 2 as the most balanced and robust configuration. It excelled across key performance indicators, including ethanol yield, revenue generation, and production cost minimization, thus affirming its suitability for industrial-scale implementation within Thailand’s emerging bioeconomy.

A comparative analysis (

Table 7. Comparison of minimum ethanol selling price from various biomass feedstocks and conversion technologies.

Biomass	Main Process	Technical Notes	MESP (USD/L)	Reference
Sugarcane bagasse	Liquefaction + SSF + Co-Ferm	Recombinant E. coli (LY01); pH 6.0; no detoxification required	0.50–0.63	[54]
Hardwood (generic)	Organosolv + enzymatic hydrolysis	Ethanol-water 50:50; no catalyst or organic solvent recovery reported	0.81 (base), 0.53 (lignin @1000 USD/t)	[55]
Wheat straw	Organosolv + lignin valorization (eugenol)	H2SO4 used as catalyst in organosolv; lignin valorized into eugenol	0.53	[50]
Eucalyptus residues	Steam explosion + enzymatic hydrolysis + fermentation	Steam explosion at 200 °C for 10 min; enzyme: Cellic CTec2; fed-batch fermentation	2.37	[56]
Corn stover	Steam explosion + enzymatic hydrolysis + fermentation	Similar to eucalyptus; low yield from corn stover contributes to high MESP	2.65	[56]
Sugarcane bagasse	Organosolv + value-based allocation	Organosolv fractionation by H2SO4	1.20	The current research (Scenario 1)
Sugarcane bagasse	Organosolv + value-based allocation	Organosolv fractionation by CH2O2	1.14	The current research (Scenario 2)
Sugarcane bagasse	Organosolv + value-based allocation	Organosolv fractionation by CH3ONa	4.33	The current research (Scenario 3)

) with the existing literature highlights the variation in minimum ethanol selling price (MESP) across different studies, shaped by feedstock type, pretreatment strategy, and co-product valorization potential. This work emphasizes the economic impact of catalyst selection in the organosolv fractionation process. The catalyst CH₂O₂ yielded the most favorable MESP at 1.14 USD/L, followed by H₂SO₄ at 1.20 USD/L and CH₃ONa at 4.33 USD/L. These findings indicate that organic acids present a more cost-effective pathway for lignocellulosic ethanol production in Thailand. Gubicza et al. [54] and Martínez-Hernandez et al. [50] reported MESPs as low as 0.50–0.63 USD/L through advanced fermentation techniques and lignin valorization. Kautto et al. [55] demonstrated further cost reduction with assumptions of high lignin market prices. Conversely, Correia et al. [56] reported higher MESPs (2.41–2.70 USD/L) due to less favorable biomass and lower conversion yields. Within this context, the MESP achieved in Scenario 2 (1.14 USD/L) is competitive, while Scenario 3 reflects cost limitations despite technical strengths. In conclusion, the findings emphasize that the integration of optimal catalyst-solvent systems, effective co-product recovery (particularly lignin valorization), mitigation of fermentation inhibitors, and comprehensive process optimization are essential for enhancing the economic sustainability of lignocellulosic biorefineries. Such strategic advancements are imperative for the successful transition of Thailand toward a high-value, bio-based economy.

5. Conclusions

A comprehensive techno-economic evaluation was conducted to determine the industrial viability of ethanol production from sugarcane bagasse via organosolv fractionation, utilizing three catalytic systems: sulfuric acid (H₂SO₄), formic acid (CH₂O₂), and sodium methoxide (CH₃ONa). Each catalytic configuration exhibited specific advantages with respect to biomass deconstruction, ethanol and lignin recovery, and cost efficiency. Among the evaluated scenarios, the CH₂O₂-catalyzed process demonstrated superior performance, achieving the highest cellulose recovery (94.52%), a competitive ethanol output of 1,081,976 kg/year, and the lowest production costs—1.14 USD/L for ethanol and 1.84 USD/kg for lignin. Capital investment remained moderate at 3.67 million USD, while TAC was the lowest among all scenarios at 3.93 million USD/year. Despite a slightly lower lignin removal efficiency (81.01%) compared to the H₂SO₄-based configuration (90.82%), the CH₂O₂ pathway maintained higher cellulose integrity and more favorable economic indices, which offset its minor limitations. In contrast, the CH₃ONa-based system retained a larger proportion of hemicellulose but incurred substantial raw material and reagent expenses, resulting in the highest ethanol (4.33 USD/L) and lignin (6.96 USD/kg) production costs and TAC exceeding 15 million USD/year. Chemical input costs emerged as the dominant sensitivity parameter, particularly for CH₂O₂ and CH₃ONa configurations, while variations in feedstock prices, temperature, and pressure had negligible impacts on overall cost structure. The sulfuric acid route, though efficient in delignification, was constrained by corrosion risks and the need for extensive wastewater treatment, thereby raising operating and maintenance costs. Strategic performance evaluation using normalized 3D vector analysis and heatmap visualization revealed that the CH₂O₂-based process offered the most balanced and economically viable solution, with favorable alignment across ethanol yield, revenue, and unit cost metrics. These findings underscore the potential of formic acid-catalyzed organosolv pretreatment as a scalable and sustainable approach for lignocellulosic ethanol production in Thailand. To enhance long-term viability, future process development should prioritize solvent recovery optimization, inhibitor mitigation during fermentation, and lignin valorization into higher-value bio-based chemicals. Such advancements will further strengthen the role of sugarcane bagasse valorization in supporting Thailand’s transition toward a circular, bio-based economy.