Kinetic Parameter Estimation and Optimization of Bio-Oil and Phenol Production from Mahogany Wood Via Pyrolysis and Fluid Catalytic Cracking Using gPROMS

International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319-9598 (Online), Volume-12 Issue-12, December 2025 28 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.A114013010126 DOI: 10.35940/ijies.A1140.12121225 Journal Website: www.ijies.org Kinetic Parameter Estimation and Optimization of Bio-Oil and Phenol Production from Mahogany Wood Via Pyrolysis and Fluid Catalytic Cracking Using gPROMS Samuel Ekamba, Uwem Inyang, Innocent Oboh, Kingsley Egemba Abstract: The thermochemical conversion of wood waste into high-value biofuels and chemicals for energy use represents a promising approach to clean, sustainable energy. This research investigates the modeling, simulation, and optimization of bio-oil and phenol production from mahogany wood waste (Swietenia macrophylla) using an integrated process approach of fast pyrolysis and fluid catalytic cracking (FCC). Kinetic parameters were estimated, and process conditions were optimised using the gPROMS ModelBuilder 4.0 software. The application of a Franz kinetic model during the pyrolysis stage identified an activation energy of 106.7 kJ/mol and a maximum bio-oil yield of 41.98% under optimal conditions of 558.7°C, a residence time of 1.92 s, and a heat capacity of 2.50 kJ/kg·K. The ensuing fluid catalytic cracking stage, developed with a novel nine-lump kinetic model, realised a maximum phenol yield of 37.086% at 595.28°C, a residence time of 2.48 s, a weight hourly space velocity (WSHV) of 16.58 h⁻¹, and a catalyst-to-oil (C/O) ratio of 7.2. A statistical tool, analysis of variance (ANOVA), confirmed the models' statistical significance, with R² values of 0.9984 for pyrolysis and 0.8926 for fluid catalytic cracking (FCC), respectively. Model predictions showed 70.6% accuracy when computed against actual experimental data. These outcomes highlight the efficacy of gPROMS for kinetic modeling and simulation of complex biomass conversion processes. Keywords: Modelling, Methodology, Bio-Oil, gPROMS, Kinetic Nomenclature: FCC: Fluid Catalytic Cracking PFD: Process Flow Diagram gEST: gPROMS Parameter Estimation Tool SSE: Sum of Squared Errors SQP: Sequential Quadratic Programming I. INTRODUCTION The global energy crisis, health and environmental Manuscript received on 04 December 2025 | Revised Manuscript received on 08 December 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025. *Correspondence Author(s) Engr. Samuel Ekamba, Researcher, Department of Chemical Engineering, University of Uyo, Nigeria. Email ID: [email protected] Dr. Uwem Inyang*, Associate Professor, Department of Chemical Engineering, University of Uyo, Nigeria. Email ID: [email protected], ORCID ID: 0000-0003-0731-3694 Prof. Innocent Oboh, Department of Chemical Engineering, University of Uyo, Nigeria. Email ID: [email protected] Dr. Kingsley Egemba, Associate Professor, Department of Chemical Engineering, University of Uyo, Nigeria. Email ID: [email protected] © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/ concerns associated with fossil fuel extraction, production, and consumption have intensified the search for renewable and carbon-neutral energy sources. Biomass, particularly lignocellulosic waste, presents a viable feedstock for producing biofuels and biochemicals [1]. Nigeria, as a major agricultural and wood-processing nation, generates significant quantities of wood waste, such as mahogany, which is often underutilized, leading to environmental issues [2]. Thermochemical conversion methods, notably pyrolysis, fluid catalytic cracking, and liquefaction, are effective and advanced pathways for transforming biomass and other types of waste into bio-oil, a liquid fuel that can be upgraded and converted into transportation fuels and valuable chemicals [3]. Flash pyrolysis, characterized by high heating rates, high temperature and short vapour residence times, maximises liquid bio-oil yield [4]. However, crude bio-oil is unstable, corrosive, and rich in oxygen content, necessitating further refining. Fluid Catalytic Cracking (FCC), a fireball in petroleum refining, has emerged as a promising technology for bio-oil upgrading, deoxygenating it into hydrocarbons and phenolics [5]. Pyrolysis and FCC are vital thermochemical processes extensively employed in the chemical industry [6]. The complexity of these processes, involving numerous reactions and sensitive operational parameters (temperature, residence time, catalyst type), makes experimental optimization costly and time-consuming. Although the financial implications of pyrolysis and FCC setup, operational cost, and revenue generation through bio-oil and other chemical sales make pyrolysis and FCC processes an economically competitive option [7]. Computer-aided modelling and simulation offer an efficient alternative [8]. While previous studies have modelled pyrolysis, few have focused on the integrated simulation of pyrolysis and FCC for specific feedstocks like mahogany wood, and even fewer have utilized the equationoriented capabilities of gPROMS for detailed kinetic parameter estimation and optimization of this system [9, 10]. This research fills this gap by employing gPROMS to: i. Develop and optimize a model for the rapid pyrolysis of mahogany wood to maximize bio-oil yield. ii. Model the FCC upgrading of the produced bio-oil using a nine-lump kinetic model to maximize phenol yield. iii. Estimate the kinetic parameters for both processes. iv. Validate the model predictions against literature data and conduct an economic feasibility analysis. Kinetic Parameter Estimation and Optimization of Bio-Oil and Phenol Production from Mahogany Wood Via Pyrolysis and Fluid Catalytic Cracking Using gPROMS 29 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.A114013010126 DOI: 10.35940/ijies.A1140.12121225 Journal Website: www.ijies.org II. MATERIALS AND METHODS A. Process Description and Feedstock The study simulated an integrated plant comprising feedstock pretreatment (hydrolysis), pyrolysis, char separation, and FCC upgrading. The feedstock was mahogany wood waste, with properties detailed in Table 1. The process flow diagram (PFD) was developed and simulated in gPROMS (Figure 1). [Fig.1: gPROMS PFD for the Production of Bio-Oil from Mahogany Wood Waste] Table I: Proximate and Ultimate Analysis of Mahogany Wood Waste [11] Ultimate Analysis (wt.%) Value Proximate Analysis (wt.%) Value Carbon 55.30 Moisture Content (wt.%) 9.80 Sulphur <0.60 Volatile matter 79.11 Hydrogen 4.56 Fixed Carbon 13.85 Oxygen 39.26 Ash 1.24 Nitrogen <0.34 HHV (MJ/kg) 21.26 Cellulose 28.08 Hemicellulose 29.52 Lignin 21.9 Particle size 0.8 mm B. Modeling Framework In Gproms gPROMS Model Builder 4.0 was used for all simulations, which were conducted at steady state. The key unit operations modelled were: i. Hydrolyzer: Pretreatment with NaOH was modelled using Seaman's kinetic model for lignocellulose breakdown [12]. ii. Pyrolysis Reactor: A fluidized bed reactor was modeled. The kinetics of mahogany wood decomposition were described using the Franz model, a first-order competing reaction model for the production of gas, tar (bio-oil), and char [13]. iii. Fluid Catalytic Cracking (FCC) Reactor: The biooil vapour from pyrolysis was fed to an FCC unit. A nine-lump kinetic model was developed, categorizing the bio-oil into lumps: Aromatics (C₁), Paraffins (C₂), Phenols (C₃), Carboxylic Acids (C₄), Furans (C₅), Alcohols (C₆), Ketones (C₇), Light Gases (C₈), and Coke (C₉). The model comprised 20 reaction pathways (Figure 2). [Fig.2: Bio-oil Reaction Pathways] C. Kinetic Parameter Estimation The gPROMS parameter estimation tool (gEST) was used to determine unknown kinetic parameters (pre-exponential factor A and activation energy Ea) by minimizing the sum of squared errors (SSE) between model predictions and data sourced from literature [14]. The Arrhenius equation was used for all rate constants. D. Optimization and Statistical Analysis The Sequential Quadratic Programming (SQP) algorithm in gPROMS was used to optimise the yields of bio-oil and phenol. The influence of process variables (temperature, residence time, heat capacity, WSHV, C/O ratio) was analyzed using Analysis of Variance (ANOVA) and 3D response surface plots. International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319-9598 (Online), Volume-12 Issue-12, December 2025 30 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.A114013010126 DOI: 10.35940/ijies.A1140.12121225 Journal Website: www.ijies.org III. RESULTS AND DISCUSSION A. Pyrolysis Process Optimization Table 2 shows the simulated yields of bio-oil, char, and syngas at different temperatures. Bio-oil yield increased with temperature, peaking at 550°C (41.3%) before declining at 600°C due to secondary cracking, while char yield consistently decreased. Table II: Simulated Product Yields from Pyrolysis Temperature (°C) Bio-Oil Yield (%) Char Yield (%) Syngas Yield (%) 450 27.5 29.5 38.7 500 33.5 23.0 43.0 550 41.3 20.6 38.0 600 37.8 16.9 44.2 ANOVA (Table 3) confirmed that temperature (p < 0.0001) and residence time (p = 0.0002) were the most significant factors affecting bio-oil yield. The model was highly important with an excellent R² of 0.9984. Table III: ANOVA for Pyrolysis Bio-oil Yield (Response 1) Source Sum of Squares F-value p-value Model 476.28 337.41 < 0.0001 A-Heat Capacity 0.0023 0.0146 0.9087 B-Temperature 329.30 2099.52 < 0.0001 C-Residence Time 15.70 100.08 0.0002 Residual 0.7842 The 3D response surface plots in Figures 3, 4, and 5 illustrate the interactive effects among the variables. The optimization process found a maximum bio-oil yield of 41.98% at 558.7°C and a residence time of 1.92 s. [Fig.3: 3D Plot of Temperature and Heat Capacity on Response 1] The three-dimensional surface plot in Figure 3 shows the combined effect of heat capacity (A) and temperature (B) on bio-oil yield (%). The plot reveals that bio-oil yield increases with both parameters up to a specific optimal region, beyond which it begins to decline. The maximum yield of 41.98% is achieved at approximately 2.4–2.5 kJ/kg·K for heat capacity and 540–550 °C for temperature. This indicates that moderate thermal parameters and biomass properties favour the formation of bio-oil. However, excessive temperature or heat capacity can lead to secondary cracking or gasification, lowering the bio-oil yield and increasing the biochar yield. The contour map below further supports this trend, showing a distinct peak region surrounded by decreasing-yield zones, which helps identify optimal operating conditions for maximum efficiency. [Fig.4: 3D Plot of Residence Time and Heat Capacity on Response 1] The three-dimensional surface plot in Figure 4 shows the influence of heat capacity (A) and residence time (C) on biooil yield (%). The yield increases with both variables up to an optimal point, peaking at 41.98%. The maximum yield occurs at around 2.4–2.5 kJ/kg·K for heat capacity and 1.7 seconds for residence time. The contour plot shows a curved region of high values, displaying a defined optimal zone. This relationship suggests that controlling the thermal parameters and retention time maximises and minimises bio-oil yield. [Fig.5: 3D Plot of Residence Time and Temperature on Response 1] The three-dimensional surface plot in Figure 5 illustrates how temperature (450 °C – 600 °C) and residence time (1.5 s –2.0 s) affect bio-oil yield (%) from wood waste pyrolysis. The yield increases with both factors, reaching about 42% at higher temperatures and longer residence times. The smooth and convex response surface suggests a strong combined effect of these variables. The contour plot at the base highlights this gradient, with the red region indicating the optimal conditions, as also shown in Figure 6. Kinetic Parameter Estimation and Optimization of Bio-Oil and Phenol Production from Mahogany Wood Via Pyrolysis and Fluid Catalytic Cracking Using gPROMS 31 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.A114013010126 DOI: 10.35940/ijies.A1140.12121225 Journal Website: www.ijies.org [Fig.6: Optimized Solution] B. Fluid Catalytic Cracking (FCC) Optimization The FCC simulation results (Table 4) showed that product distribution was susceptible to temperature. Phenol yield was targeted for optimization. Table IV: FCC Product Yields (wt.%) at Varying Temperatures Temp (°C) Aro Phe Ket Lg Cok 450 6.51 14.7 18.0 12.0 8.56 500 7.33 15.5 18.7 12.4 9.80 550 8.20 15.6 18.2 12.8 10.2 600 7.10 13.1 17.6 13.0 11.9 For the FCC stage, ANOVA (Table 5) showed that residence time (p = 0.0012) and C/O ratio (p = 0.0003) were the most critical linear factors for phenol yield. The model R² was 0.8926. Table V: ANOVA for FCC Phenol Yield (Response 3) Source Sum of Squares F-value p-value Model 159.95 8.90 < 0.0001 A-Temperature 1.50 1.17 0.2967 B-Residence Time 20.17 15.17 0.0012 D-C/O Ratio 28.17 21.95 0.0003 Residual 19.25 Optimization of the FCC process yielded a maximum phenol yield of 37.086% at 595.28°C, a residence time of 2.48 s, a WSHV of 16.58 h⁻¹, and a C/O ratio of 7.2. C. Kinetic Parameter Estimation The Franz model for pyrolysis provided a good fit to the data (R² = 0.7875), yielding a rate constant *k* of 0.4808 s⁻¹ and an activation energy Ea of 106.7 kJ/mol as shown in Table 6. For the FCC process, the 20 kinetic parameters for the nine-lump model were successfully estimated [15]. The pre-exponential factors and activation energies (e.g., k₁: A=1.0×10⁶ s⁻¹, Ea=180 kJ/mol) were consistent with the highly reactive and complex nature of bio-oil cracking, as demonstrated in Table 7 Table VI: Franz model Parameters Outcome General model: t) = 𝐵[ 1 − exp(−𝑘𝑡)] Coefficients (with 95% confidence bounds): B = 16.71 (16.89, 17.54) k = 0.4808 Ea = 106.7 KJ/mol Goodness of fit: SSE: 23.67 R-square: 0.7875 Adjusted R-square: 0.7774 RMSE: 12.47 Table VII: Kinetic Parameter Estimation of FCC Kn Pathway A (s⁻¹) Ea (kJ/mol) Calculated K @ 450 OC 500 OC 550 OC 600 OC k1 k2 k3 k4 k5 k6 k7 k8 k9 k10 k11 k12 k13 k14 k15 k16 k17 k18 k19 k20 C₁ → C₂ C₁ → C₃ C₁ → C₈ C₂ → C₃ C₂ → C₈ C₃ → C₁ C₃ → C₉ C₄ → C₂ C₄ → C₈ C₄ → C₉ C₅ → C₃ C₅ → C₈ C₅ → C₉ C₆ → C₁ C₆ → C₈ C₆ → C₉ C₇ → C₁ C₇ → C₂ C₇ → C₈ C₇ → C₉ 1.0 × 10⁶ 1.0 × 105 1.0 × 107 5.0 × 105 5.0 × 106 1.0 × 105 2.0 × 105 2.0 × 106 2.0 × 107 5.0 × 105 1.0 × 105 1.0 × 10⁶ 2.0 × 105 5.0 × 105 5.0 × 106 1.0 × 10⁶ 5.0 × 105 5.0 × 105 5.0 × 106 1.0 × 10⁶ 180 200 220 170 190 200 230 190 210 240 200 220 240 200 220 250 200 210 230 260 9.95E-08 6.90E-07 3.78E-06 1.70E-05 3.57E-10 3.07E-09 2.03E-08 1.08E-07 1.28E-09 1.37E-08 1.09E-07 6.89E-07 2.62E-07 1.63E-06 8.14E-06 3.38E-05 9.43E-08 7.28E-07 4.38E-06 2.15E-05 3.57E-10 3.07E-09 2.03E-08 1.08E-07 4.86E-12 5.77E-11 5.07E-10 3.48E-09 3.77E-08 2.91E-07 1.75E-06 8.59E-06 1.35E-08 1.30E-07 9.43E-07 5.47E-06 2.30E-12 3.05E-11 2.94E-10 2.19E09 3.57E-10 3.07E-09 2.03E-O8 1.08E-07 1.28E-10 1.37E-09 1.09E-08 6.89E-08 9.22E-13 1.22E-11 1.18E-10 8.77E-10 1.79E-09 1.54E-08 1.02E-07 5.42E-07 6.42E-10 6.84E-09 5.47E-08 3.45E-07 8.74E-13 1.29E-11 1.37E-10 1.11E-09 1.79E-09 1.54E-08 1.02E-07 5.42E-07 3.39E-10 3.24E-09 2.36E-08 1.37E-07 1.22E-10 1.44E-09 1.27E-08 8.69E-08 1.66E-13 2.71E-12 3.17E-11 2.97E-10 D. Model Validation A comparison between the gPROMS simulation and experimental data from literature [14] showed a strong correlation (Figure 7), with a prediction accuracy of 70.6% [16]. International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319-9598 (Online), Volume-12 Issue-12, December 2025 32 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number: 100.1/ijies.A114013010126 DOI: 10.35940/ijies.A1140.12121225 Journal Website: www.ijies.org [Fig.7: Experimental Vs Simulated Comparison Plots] IV. CONCLUSION This study demonstrates the successful use of gPROMS for kinetic modelling and optimisation of an integrated pyrolysisFCC process to convert mahogany wood waste into bio-oil and phenol. Key findings include the optimal conditions for maximising bio-oil yield (41.98%) from fast pyrolysis: 558.7°C and a residence time of 1.92 s. The optimal conditions for maximising phenol yield (37.086%) from FCC upgrading are 595.28°C, a residence time of 2.48 s, a WSHV of 16.58 h⁻¹, and a C/O ratio of 7.2. The Franz model and the novel nine-lump FCC model effectively describe the reaction kinetics, with estimated parameters providing a strong basis for reactor design. The high statistical significance of the models and the positive economic assessment highlight the potential for industrial application. The use of gPROMS proved highly effective, conserving time and resources that would otherwise have been spent on extensive experimental work. This work provides a robust framework for simulating and optimising advanced biomass conversion processes. DECLARATION STATEMENT As the article's author, I must verify the accuracy of the following information after aggregating input from all authors. ▪ Conflicts of Interest/ Competing Interests: Based on my understanding, this article has no conflicts of interest. ▪ Funding Support: This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted objectively and without external influence. ▪ Ethical Approval and Consent to Participate: The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. ▪ Data Access Statement and Material Availability: The adequate resources of this article are publicly accessible. ▪ Author’s Contributions: The authorship of this article is contributed equally to all participating individuals. REFERENCES 1. IEA. (2023). World Energy Outlook 2023. Paris: IEA. https://www.iea.org/reports/world-energy-outlook-2023 2. Akinwale, O. 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Luo, Z.; Wang, S. and Cen, K, (2005) A model of wood flash pyrolysis in fluidized bed reactor, Renewable Energy, 30(3):377 – 392. DOI: https://doi.org/10.1016/j.renene.2004.03.019 AUTHOR’S PROFILE Engr. Samuel Akpan Ekamba is a researcher in the Department of Chemical Engineering at the University of Uyo, Nigeria. He specialises in process modelling, renewable energy, and environmental engineering, with notable work on optimising methane production from anaerobic digestion of pig waste using advanced techniques such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Dedicated to innovation and sustainable development, Engr. Ekamba combines academic excellence with practical engineering experience, contributing to research, teaching, and capacity building in the energy and environmental sectors. Dr. Uwem Ekwere Inyang Obtained his PhD in Chemical Engineering in 2019. He is an Associate Professor in the Department of Chemical Engineering at the University of Uyo, Nigeria. He has supervised numerous undergraduate and postgraduate’ students, making significant contributions in the field of Chemical Engineering. He has published several scientific and technical papers in reputable journals. His research interests include drying, separation processes, waste management, Environmental Engineering, modelling, and Simulation. Prof. Innocent Oserihbo Oboh is a professor of Chemical Engineering at the University of Uyo, Nigeria. He has several technical papers in learned journals. His research areas are Environmental and waste management, Biochemical Engineering, modelling, and Simulation. Dr. Kingsley Chibuzor Egemba is an Associate Professor of Chemical Engineering in the Department of Chemical Engineering of the University of Uyo, Nigeria. Have several scientific and technical papers in both local and international Journals. Have good administrative and engineering skills. His research areas are in environmental engineering, petroleum Refining Engineering and separation processes. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of the Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)/ journal and/or the editor(s). The Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.