DWSIM-Based Process Simulation and Exergy Analysis of Bioethanol Production from Rice Husk

Jimoh Muktar , Toyese Oyegoke ^* 

1. CAD Engineering of Processes and Reactive Interfaces (CEPRIs) Group, Chemical Engineering Department, Ahmadu Bello University Zaria, Nigeria

* Correspondence: Toyese Oyegoke 

Academic Editor: Grigorios L. Kyriakopoulos

Special Issue: Solid Waste Management for Biofuel and Biosorbent

Received: December 31, 2025 | Accepted: April 14, 2026 | Published: April 30, 2026

Adv Environ Eng Res 2026, Volume 7, Issue 2, doi:10.21926/aeer.2602008

Recommended citation: Muktar J, Oyegoke T. DWSIM-Based Process Simulation and Exergy Analysis of Bioethanol Production from Rice Husk. Adv Environ Eng Res 2026; 7(2): 008; doi:10.21926/aeer.2602008.

© 2026 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.

1. Introduction

The pursuit of Sustainable Development Goals (SDG 7 - Affordable and Clean Energy, and SDG 13 - Climate Action) has intensified global efforts toward cleaner, more efficient, and sustainable energy systems. Transforming underutilized agricultural residues into value-added bio-based products aligns directly with the goals of energy security, waste valorization, and circular economy development. Biofuels—particularly bioethanol—are central to this effort, due to their potential to reduce greenhouse gas (GHG) emissions and integrate into existing energy infrastructures [1,2,3].

However, the dominant reliance on food-based feedstocks such as corn and sugarcane in first-generation bioethanol production has raised sustainability concerns—especially regarding land use and food security [4]. As a result, attention has shifted toward second-generation (2G) bioethanol produced from lignocellulosic biomass like rice husk, an agricultural by-product generated in large quantities across rice-producing nations such as India, China, Bangladesh, and Nigeria [5,6,7]. Despite global rice husk availability of over 140 million tons annually, most of this biomass is underutilized or improperly disposed of, contributing to environmental degradation. Its rich composition in cellulose, hemicellulose, lignin, and silica makes it a promising candidate for biochemical conversion [8].

Several studies have demonstrated both the technical feasibility and economic potential of lignocellulosic bioethanol. For example, Quintero et al. [9] compared four Colombian feedstocks and found that empty fruit bunches produced the highest ethanol yields (314 L/t) and the lowest production cost ($0.49/L with cogeneration). In Nigeria, Oyegoke & Dabai [10] simulated a sugarcane juice-bagasse biorefinery that achieved 63% energy efficiency and could produce 148 million L/year of ethanol at a manufacturing cost of $0.61/L. Ajayi et al. [11] demonstrated that using sorghum bagasse, ethanol yields of 189 g/kg feedstock were achievable, though with high capital intensity (~$1.92/L). Zhou et al. [12] highlighted chemical looping gasification of corn straw as an energy-efficient (49.3%) and cost-competitive alternative. Recent work by Angannan et al. [13] showed that the open-source DWSIM platform can replicate Aspen Plus results within a 2-10% margin, while cutting capital costs by $240 million and reducing ethanol’s minimum selling price by $3/gal.

Nonetheless, cost and energy inefficiencies remain persistent barriers. Studies such as Castro et al. [14] and Kiteto et al. [15] highlight how high pre-treatment and utility costs render many 2G biorefineries economically marginal. Reviews like Oyegoke et al. [16] emphasize the gap between experimental success and commercial implementation in Nigeria, pointing to a need for robust techno-economic and thermodynamic modeling to guide future development.

Several studies have carried out exergy analysis of bioethanol production, such as Ojeda et al. [17], and Kang & Tan [18] study only talks about the exergy efficiency of simultaneous saccharification and co-fermentation of bioethanol from rice husk without analyzing the individual irreversibility contribution of all the vital stages involved and their optimization strategies. Moreover, Joseph et al. [19] carried out a similar analysis on sugar cane bagasse, focused on identifying the exergy inefficiency stages in the process, with little emphasis on the energy analysis and efficiency.

In this context, thermodynamic analysis—especially second-law (exergy) analysis—has emerged as a vital diagnostic tool. Unlike first-law (energy) analyses, exergy analysis reveals where energy is lost in quality or usefulness, pinpointing inefficiencies and guiding process intensification [20]. For instance, Joseph et al. [19] found that pre-treatment and saccharification stages are major exergy sinks in bagasse and algal systems. Hu et al. [21] further demonstrated that process integration strategies like recycling waste streams can reduce water, energy, and environmental burdens by up to 80%.

Building on these insights, this study aimed to develop a detailed DWSIM-based process model for converting rice husk into fuel-grade bioethanol. This integrates biochemical conversion (pretreatment, hydrolysis, fermentation) and separation (distillation) units and performs a combined energy and exergy analysis to evaluate the system’s thermodynamic performance for heat recovery/integration improved process flowsheet is identified to identifies key sources of inefficiency for evaluation and recommendation of more sophisticated improvement strategies—such as vapor recompression, and membrane hybridization—are proposed. By quantifying the energy and quality losses in converting rice husk from waste to fuel, this study offers a practical roadmap toward cost-effective, scalable, and environmentally sustainable 2G biorefineries. The findings directly support ongoing waste-to-wealth strategies and reinforce the role of thermodynamic modeling in accelerating clean energy transitions in resource-constrained settings.

2. Computational Details and Methodology

2.1 Computational Details

This study used DWSIM, a free and open-source chemical process simulation software [13], to model, simulate, and thermodynamically evaluate the bioethanol production process from rice husk. DWSIM was chosen for its flexibility, accessibility, and comprehensive thermodynamic packages that are particularly suitable for academic research and feasibility studies. To complement the process modeling, Microsoft Excel was employed for detailed tabular analysis, result interpretation, and basic calculations, such as yield, efficiency, and entropy estimation. Additionally, Microsoft Visio was used to develop a clear and professional Process flow diagram (PFD) that visually represents the overall process architecture. For the simulation’s thermodynamic foundation, the Non-Random Two-Liquid (NRTL) activity coefficient model was selected. This model is particularly effective in systems involving polar components and non-ideal liquid mixtures, which are characteristic of bioethanol-water systems, in accordance with existing related literature [11,22,23]. The NRTL model accounts for molecular non-randomness and interaction parameters, making it well-suited for simulations involving distillation, fermentation broth separation, and aqueous-phase reactions where deviations from ideality are significant.

2.2 Study Strategy

In this study, a specific process technology previously validated through laboratory (wet) experiments was selected based on a survey of existing literature reports related to the subject of our study, as illustrated in Figure 1. The selected process was thoroughly reviewed to provide a comprehensive description. Insights gained from this detailed process presentation were used to identify and categorize the various compounds involved in the system. These compounds were sorted by their roles as feed, intermediate, side, or product streams, and further classified as either pure or hypothetical.

Figure 1 Overview of our study workflow.

The hypothetical compounds were configured to be created during the process modeling phase. In contrast, the pure compounds did not need to be created, as they were already available in the compound library of the chosen process simulation software. For the hypothetical compounds, relevant thermophysical and chemical data were sourced from literature and used for modeling. Simultaneously, operational data for each unit operation—such as temperature, pressure, and conversion parameters—were gathered from the literature and applied to develop the process model, guided by a process flow diagram (PFD) of the selected process.

Using computer-aided tools available within the chosen simulator, energy and material balance analyses were conducted across individual unit operations and the overall process plant. This included the collection of key thermodynamic properties to evaluate the system's effects on its surroundings, particularly through calculations of entropy generation and exergy destruction. The resulting data provided insight into key performance indicators, including product yield, energy efficiency, and the thermodynamic efficiency of the entire system.

2.2.1 Description and Process Flow Diagram for the Selected Process

The production process for bioethanol from rice husk is assumed to begin with pretreated biomass, in which the rice husk is dried to remove moisture and milled into fine particles suitable for enzymatic attack. The complete process is divided into three primary stages: hydrolysis of cellulose and hemicellulose to fermentable sugars; fermentation of sugars to ethanol; and product recovery and purification to obtain fuel-grade bioethanol. This process sequence is presented in the process flow diagram (PFD) in Figure 2, which outlines all unit operations and major material streams.

Figure 2 Process Flow Diagram of the Bioethanol Production Process.

In the alkaline delignification process, 1.5% (v/v) NaOH was added to rice husk solutions at 5% (m/v) at 393.15 K [24]. Enzymatic Hydrolysis of Cellulose and Hemicellulose After the alkaline pretreatments, the enzymatic hydrolysis of cellulose and hemicellulose present in the rice husk waste was carried 10% w/v solution of pre-treated rice husk. Prior to enzyme application, the pH was adjusted to 4.8 by adding 20% hydrochloric acid (v/v). The laboratory study used enzymatic hydrolysis with Celluclast 1.5 L (β-glucosidase) at a loading of 30 FPU per gram of carbohydrates, though this was not considered in the simulation. This conversion pathway was chosen for its selectivity and lower energy demand compared to acid hydrolysis. The optimum condition of this process is 50°C (323.15 K), which maintains enzyme activity while ensuring process stability [24].

In the fermentation stage, the hydrolyzed sugars—glucose and xylose—are converted into bioethanol and carbon dioxide using encapsulated yeast strains under anaerobic conditions. The process operates optimally at 30°C (303.15 K) [8,24] to ensure maximum microbial activity and ethanol yield. Encapsulation offers improved stability and reusability of yeast, which is critical for process economics.

Then the purification process was carried out using several pieces of purification equipment to obtain fuel-grade bioethanol from the azeotropic mixture. As shown on the process flow diagram, two rigorous distillation columns in series were used to reduce the water content of the mixture, and an absorption column was used to capture the carbon dioxide (with the use of 4500 kg/hr of Glycerol as a solvent) present before achieving the pure bioethanol through a molecular sieve.

The stoichiometric equations and conversion efficiencies of the hydrolysis and fermentation reactions are summarized in Table 1. These values were programmed into DWSIM using conversion reactor models with defined yields for glucose, xylose, ethanol, and carbon dioxide. The final stage is bioethanol purification, in which ethanol is separated from the fermentation broth, which contains significant amounts of water and minor impurities. Due to the azeotropic nature of the ethanol-water mixture, conventional distillation alone cannot achieve fuel-grade ethanol (≥99.7%). Therefore, a molecular sieve unit (compound separator) was included in the process flowsheet to achieve the desired product purity, despite the higher energy and capital investment required.

Table 1 Reaction sets and reactions.

2.3 Compounds and Feedstock Modeling

2.3.1 Compounds

The modeling process incorporated both pure compounds, readily available in the DWSIM database, and hypothetical compounds, which were manually created using molecular property estimation and group contribution methods such as UNIFAC and Joback. This methodology closely followed the approach outlined in the study by Anganna et al. [13], which investigated the use of sorghum-based feedstock for bioethanol production. Table 2 summarizes the relevant compounds, including their CAS numbers, molecular formulas, and the modeling approach used.

Table 2 Compounds Used in the Simulation.

2.3.2 Feedstock

In contrast to other literature [25,26], which assumes moisture-laden biomass, this study considers rice husk to be completely dried and milled, enhancing consistency and reaction predictability. Table 2 compares typical literature compositions with modeled values used in this study.

Table 3 presents the literature [25,26] and modeled data in the report, along with their respective compositions and stream conditions. Though variation in rice husk composition is tied to rice husk origin and type, it was not accounted for in the simulation (i.e., the dynamic simulation was not considered).

Table 3 Feedstock chemical composition and thermodynamic conditions.

2.4 Process Flowsheeting and Unit Operations

All unit operations described in the process flow diagram were modeled in DWSIM using suitable simulation equipment. Each operation was configured with realistic process conditions drawn from literature or assumed for simulation purposes. Table 4 provides an overview of each unit operation, the simulation model used, and the key parameters.

Table 4 Unit Operations and Simulation Parameters.

Table 4 presents details of the existing unit operations or equipment shown in the process flow diagram, and the models selected to model the production process flowsheet in the chosen simulator, using the provided relevant parameters and/or conditions.

2.5 Additional Process Performance Analysis

The following parameters were calculated post-simulation to evaluate system performance and efficiency of the bioethanol production process plant in items (a) to (c).

2.5.1 Bioethanol Yield

This was calculated as the ratio of the mass of ethanol produced to the mass of rice husk fed into the system. It reflects the effectiveness of the conversion process in generating ethanol per unit biomass.

2.5.2 Energy Efficiency

Defined as the ratio of the lower heating value (LHV) of the ethanol output to the sum of the LHV of the biomass input and all external energy supplied. This parameter, expressed as a percentage, represents the overall energy utilization efficiency of the system.

2.5.3 Thermodynamic (Exergy) Efficiency

This was determined by dividing the useful exergy output by the total exergy input required for bioethanol production. Expressed as a percentage, this metric indicates how effectively the system converts available energy into useful work while accounting for irreversibility.

3. Results and Discussions

This section presents and analyzes the simulation outcomes of bioethanol production using rice husk as a lignocellulosic feedstock. The performance of the integrated process was evaluated based on bioethanol yield, energy efficiency, thermodynamic efficiency, and insights from mass and energy balances. The simulation results also revealed the most energy-intensive and thermodynamically inefficient stages in the process, providing direction for potential optimization.

3.1 Modeled Process Flow Diagram

The process flow diagram simulated using the DWSim package, based on the configuration outlined in Figure 2, is shown in Figure 3. The diagram illustrates the detailed process layout, highlighting the specific unit operations and equipment employed to model each corresponding step in the process flow diagram.

Figure 3 DWSim Process flowsheet diagram.

3.2 Material Balance and Requirement Analysis

The material balance for the proposed bioethanol production plant is summarized in Table 5, detailing the mass flow rates of key input and output streams. The simulation was based on a feed rate of 10,000 kg/h of hydrothermally pre-treated and dried rice husk. Additional water was incorporated to support the hydrolysis, fermentation, and separation stages.

Table 5 Overall material balance.

According to the simulation results, the process yielded 2,802.7 kg/h of fuel-grade bioethanol with a purity of 99.90%. This corresponds to a product-to-feed ratio of 280.3 g/kg (0.355 ml bioethanol/g husk), indicating that approximately 28.03% of the biomass feed was successfully converted into high-purity ethanol. This yield is considered significant and offers a strong foundation for process scaling, capacity design, and techno-economic feasibility assessments. Compared with existing studies, the reported yield is substantially higher than the 14.22% conversion achieved using rice straw [27]. Yet, it remains lower than the 86.7% yield reported by Omidvar et al. [28] and the 35.3% yield obtained by Arismendy et al. [29] for rice husk. These comparative insights highlight both the promise and the room for further optimization of the present process.

The simulation achieved a negligible residual error of 0.0002%, confirming an excellent mass closure and underscoring the reliability of the modeling framework, which is also prone to variations in industrial settings. The observed bioethanol yield aligns well with values reported in the literature for enzymatic hydrolysis and fermentation of lignocellulosic biomass. This consistency indicates that converting rice husk under simulated conditions is both technically feasible and industrially promising.

3.3 Energy Balance and Requirement Analysis

The energy balance of the bioethanol production process was assessed by quantifying the energy inputs and outputs across all major unit operations and process streams. These values, obtained from the DWSIM simulation, are summarized in Table 6 and are expressed in kilojoules per hour (kJ/h).

Table 6 Overall Energy Balance (kJ/h).

The plant’s total energy input comprises contributions from major energy-intensive units, including reboilers (used in solvent recovery and distillation), heating systems (hydrolyzer and process heaters), pumping units, and auxiliary operations such as molecular sieves, fermenters, and coolers. The energy output accounts for the energy contained in all product and by-product streams, as well as heat removed through various cooling duties. The overall energy balance error was calculated to be 0.08% of the total input energy. This minor discrepancy is likely attributable to the property estimations of the hypothetical components employed in the simulation, in line with the previous studies [10,11] that deployed a different simulator (Aspen HYSYS) for a similar bioethanol production process that records similar observations. Nevertheless, the near-perfect agreement between total input and output energy values indicates a robust and internally consistent simulation, falling well within acceptable limits for complex thermodynamic systems.

The energy efficiency of the process was evaluated using the ratio of the lower heating value (LHV) of the bioethanol product, byproduct, and the power generated from the hot wastewater to the sum of the LHV of the biomass feedstock and the total energy input to the process. Table 7 presents the energy content of the key material streams required for the energy efficiency calculation. The calculation is based on the lowest heating values (LHVs) of the stream composition.

Table 7 Steam Energy content values.

The total energy requirement by the simulated equipment was shown in Table 8 above, highlighting the energy-intensive unit with its energy, where energy for molecular sieve activation was calculated using the latent heat of vaporization of water due to the improvisation (modeled with a compound separator).

\[ \begin{aligned}Energy\,efficiency&=\frac{main\,product\,energy\,+\,byproduct\,energy}{feed\,energy+total\,energy\,used}*100\\&=\frac{74,832,090\,+\,54,163,750}{(136,430,000\,+\,58,244,466.75)}*100\%\end{aligned} \]

\[ Energy\,efficiency=\frac{128,995,840.0}{194,674,466.7}*100\%=66.26\% \]

Table 8 Total energy requirement.

This energy efficiency result indicates that only 66.26% of the total energy invested in the process is retained in the ethanol product and the residue byproduct. This efficiency is within the typical range for second-generation lignocellulosic bioethanol production, where energy-intensive unit operations—such as distillation, hydrolysis, pretreatment, and molecular sieving—account for a significant share of the overall energy demand.

3.4 Thermodynamic Analysis

3.4.1 Entropy Generation Calculation

The entropy (S) across each unit was used to compute the overall entropy generation (S_G) [33] using equation (1) while equation (2) was used in the estimation of the reversible exergy (Ex_rev).

\[ S_{GEN}=\Sigma(S_2-S_1)-\frac{Q}{T_0} \tag{1} \]

\[ Ex_{input}=Ex_{reversible}+\sum Ex_{destroyed} \tag{2} \]

The irreversibility across each unit was evaluated using equations (3) and (4) adapted from the literature [33,34]:

\[ Ex_{destroyed}\,or\,I\,=\,T_0\,*\,S_{GEN} \tag{3} \]

\[ Ex_{destroyed}\,in\,\%=\frac{Ex\,destroyed}{\Sigma\,{Ex\,destroyed}}*100 \tag{4} \]

Ex_destroyed or I is the exergy that was destroyed or not converted into useful work due to irreversibilities within a process such as friction, heat transfer with a finite temperature difference or chemical reaction, and Ex_in is the exergy associated with the resources supplied to the process or system. In the calculation of entropy generation, the surrounding temperature T_surr in equation (1) is equal to the boundary temperature assumed as the outlet temperature of the heat source for the heat intensive processes. While reference temperature T₀ = 298.15 was used throughout in equation (3).

In Table 9, the numerical computation of entropy changes and entropy generation is presented by substituting the total entropy of both input and output streams with the external heat added to or removed from each unit. Several units—such as the pumps, heat exchangers, solid separators, flash columns, and absorption columns—were modeled as adiabatic; therefore, their entropy generation depends solely on the entropy change across them. The distillation columns 1 and 2 exhibit the highest degree of irreversibility, with an entropy generation of 23,643.78 kJ/K and 20,593.62 kJ/K respectively, signifying the intensive energy requirement for component separation. The two reactive units (17,132.94 kJ/K and 15,723.35 kJ/K respectively) indicate a relatively high entropy generation due to enzymatic activities in bond breaking and formation. Then, the pretreatment unit (9,385.70 kJ/K) is a lignin-removing process that operates under high-temperature conditions as an energy-intensive unit, leading to some irreversibilities. Moreover, the solvent recovery unit and the three heat integration devices (heat exchangers) show a substantial contribution to the entropy generation. In contrast, the flash column, Pumps, and solid separators show zero or negligible irreversibilities.

Table 9 Entropy generation across the unit operations (Note that Q is the heat flow, S is the entropy, and SG is the entropy generation).

3.4.2 External Exergy

The exergy associated [34] with work and quantity of heat in each unit is shown in Table 10 below, representing the useful work (quality) that can be extracted from each quantity.

\[ T_0=298.15K;\quad Ex_Q=Q\left(1-\frac{T_0}{T}\right) \tag{5} \]

Ex_w is the exergy in a mechanical work, Ex_Q is the exergy in a thermal energy, Q_c is the condenser duty, Q_b is the reboiler duty, T₀ is the reference temperature, and T is the stream (or boundary) temperature.

Table 10 External Exergy across each unit.

The numerical results of the model in Equation (5), as presented in Table 10, illustrate the exergy or the external useful work achievable from the thermal and mechanical duties within the plant. Energy intensification (energy integration) was performed by looping energy streams to subsidize the total energy requirement of the energy-intense unit processes, which the total exergy input of 11,304,540.10 kJ/h.

3.4.3 Exergy Destruction Analysis

Exergy destruction within a system represents the loss of potential to perform useful work due to irreversibilities caused by entropy generation, and it serves as a measure of system inefficiency. Using the mathematical model described in Equation (3), the total exergy destroyed across the entire plant was calculated to be approximately 34.0 million kJ/h (33,996,624.7 kJ/h). Figure 4 presents a bar chart illustrating the percentage distribution of exergy destruction—arising from system irreversibilities—across the major unit operations in the modeled bioethanol production process.

Figure 4 Exergy destroyed in % across different control volumes.

This visual representation reinforces the preceding thermodynamic analysis by clearly identifying the distillation columns 1 and 2 as the most thermodynamically inefficient units, accounting for 22.74% and 19.81% respectively, of the total system irreversibilities. This substantial share highlights the high energy demand associated with ethanol-water separation, driven by their azeotropic nature. The pretreatment unit (9.03%), hydrolyzer (16.48%) and fermenter (15.12%) closely follow, with their combined entropy generation reflecting the inherent irreversibility of biochemical transformations typical of second-generation lignocellulosic bioethanol production. Also illustrated on the chart, the solvent recovery unit and the heat exchangers are responsible for exhibiting little contribution to exergy destruction.

Auxiliary units, the heat exchangers contribute moderate levels of exergy irreversibilities, as expected in components involving extensive heat exchange and the solvent recovery unit is a low-energy-intense separation with little inefficiency. In contrast, units such as water regulator, residue separator, pumps and the flash drum record high thermodynamic efficiency through either adiabatic or nearly isentropic behavior. Consistent with the literature [19,33,34], this analysis confirms that the distillation, pretreatment, fermentation, and hydrolysis units are the principal contributors to energy losses in the system, as evidenced by their disproportionately high exergy destruction relative to the other units with minimal entropy generation.

3.4.4 Overall Thermodynamic Analysis

Second law analysis: The second law efficiency, also known as exergy efficiency, was evaluated using equation (8) as an energy requiring process computed with reference to the literature [34].

\[ Ex_{in}=\sum_{in}^j(Ex_Q)+\sum_{in}^j(Ex_W)+\sum_{input}^j(Ex_M) \tag{6} \]

\[ \sum_{input}^j(Ex_M)=\alpha LHV_{rice\,husk}+\sum_{in}^j(m.ex_{chem}^0) \tag{7} \]

Where, LHV = lowest heating value, α = exergy factor, $ex_{chem}^0$ = molar chemical exergy (chemical potential), and Ex_M = material stream exergy. And do note that the physical exergy of the inputs taken to be 0 the reference or surrounding conditions were taken to be P = 1 atm and T = 25°C in the computations carried out.

Substituting relevant data computed within Table 11 into the model presented in equation 6 yield:

\[ \begin{aligned}Ex_{in}&=(3,847,251.8+5,159,855.15+1,963,070.15)\\&+(27,020.00+16,212.00+29,063.90)+(289,888.60)+(137,555,096.0)\\&=148,795,133.3\,kJ\end{aligned} \]

\[ Ex_{Efficiency}=\left(1-\frac{Ex_{destroyed}}{Ex_{in}}\right)*100\% \tag{8} \]

Table 11 Standard Chemical exergy of pure components (@25°C, 1 atm).

And computation of the exergy efficiency gives:

\[ Ex_{Efficiency}=\left(1-\frac{30,996,624.74}{148,795,133.30}\right)*100\% \]

\[ Ex_{Efficiency}=79.17\% \]

In this study, the total exergy input (Ex_in) across all energy-consuming equipment was calculated to be approximately 148.8 million kJ/h (148,795,133.30 kJ/h), based on Equations (6) and (7). The exergy destroyed (Ex destroyed), derived from the corresponding entropy generation, was also determined to be approximately 31 million kJ/h (30,996,624.74 kJ/h).

Using Equation (8), the exergy efficiency of the process was found to be 79.17%, indicating that approximately one-fifth of the input energy is irreversibly lost due to system imperfections—mainly from entropy generation and non-ideal heat and mass transfer phenomena. A breakdown of individual exergy destruction per equipment is provided in Figure 5. As with the entropy analysis, the distillation columns combined were the most significant contributor to exergy destruction, responsible for 8.87% of the total exergy input. This underscores the inefficiencies associated with azeotropic separation in ethanol-water systems. The pretreatment unit, hydrolyzer and fermenter, contributed 1.88%, 3.43% and 3.15% respectively, and displayed considerable exergy destruction—primarily due to the irreversible nature of biochemical conversions and their thermal demands. Furthermore, the entire plant shows a total irreversibility of 20.83% achieved by process intensification. The findings from the study were found to be similar to the literature report [19], which also confirms greater exergy losses around the distillation and reactor units in a similar sugarcane-bagasse-based bioethanol production process.

Figure 5 Thermodynamic Analysis of the entire plant.

Small exergy destructions were observed in utility and auxiliary equipment, including Pump 1 (0.02%), heat exchanger 1 (0.79%), heat exchanger 2 (0.96%), and heat exchanger 3 (1.07%), reflecting relatively higher exergy efficiencies in these components. Equipment such as the pump 2, water regulator, residue remover, flash drum, and absorption column exhibited negligible irreversibilities (<0.01%), suggesting that they operate under nearly isentropic and adiabatic conditions. This second-law assessment [19,33,34] not only reinforces the insights from entropy generation analysis but also provides a quantitative measure of the system’s energy utilization effectiveness. Focusing optimization efforts on units with the highest exergy destruction—particularly the distillation column, pre-treatment unit and reactive unit — could yield substantial improvements in the overall thermodynamic performance of the process.

4. Conclusions and Recommendations

This study conducted a comprehensive thermodynamic evaluation of second-generation bioethanol production from rice husk, using process modeling and simulation in DWSIM. By integrating material and energy balances with second-law thermodynamic analysis, the work provided critical insights into the performance and sustainability constraints of the process. At a feed rate of 10,000 kg/h of hydrothermally pretreated rice husk, the simulation achieved 2,802.7 kg/h of high-purity bioethanol (99.90%), corresponding to a conversion yield of 28.03% (w/w). These results affirm the potential of rice husk as a sustainable and underutilized feedstock for renewable biofuel production.

The plant's energy efficiency was determined to be 66.26%, reflecting significant energy demand, particularly during distillation and heating stages. Second-law analysis revealed an exergy efficiency of 79.17%, underscoring notable irreversibilities across the system. The distillation columns, hydrolyzer, fermenter, and pretreatment unit were identified as the most exergy-intensive units despite the heat integration, jointly accounting for over half of the total entropy generation. These findings emphasize the critical need for thermodynamic optimization within these core units.

On the other hand, auxiliary equipment such as pumps, the solid separators, and flash drums showed near-ideal performance, with negligible entropy generation. Heat exchangers and solvent recovery systems experienced moderate exergy losses due to inherent heat rejection processes. This detailed thermodynamic mapping not only validates earlier entropy-based evaluations but also offers a roadmap for targeting energy inefficiencies within the system. Despite the study focusing on minimizing this inefficiency by integration strategies such as heat recovery loops were employed.

To enhance sustainability and energy performance, the future study should focus on advanced process integration strategies such as mechanical vapor recompression and hybrid distillation-membrane technologies. Additionally, optimizing the biochemical stages through improved enzyme formulations, precise thermal control, and innovative reactor designs may significantly increase conversion efficiency and reduce irreversibility. These combined measures provide a viable pathway toward scalable, low-emission, and energy-efficient bioethanol production, contributing meaningfully to global decarbonization and circular economy initiatives.

Acknowledgments

The authors wish to acknowledge the support of the developer of the freeware process simulator employed in the study, which made such computing resource accessible to the communities that could not afford the commercial process simulators. In addition, we do appreciate the motivational support of the other members of Class-2025-CHEN309, CEPRIs Group and Pencil Team.

Author Contributions

Jimoh Muktar.: Formal analysis, Methodology, Investigation, Software, Visualization, Resources, Validation, Writing - original draft, Writing - review & editing. Toyese Oyegoke.: Conceptualization, Software, Investigation, Supervision, Methodology, Resources, Validation, Visualization.

Funding

This research received no external funding.

Competing Interests

The authors do declare no any conflict of interest in the report.

Data Availability Statement

All data employed in the study are provided therein the manuscript and supplementary materials.

AI-Assisted Technologies Statement

During the preparation of this work, the author(s) used Grammarly and OpenAI to improve readability and language, eliminating grammatical or language-related errors. After utilizing these tools/services, the author(s) reviewed and edited the content as necessary and assumed full responsibility for the publication’s content.

Additional Materials

The following additional materials are uploaded as a file.

1. Supplementary information.