Research on the Optimization of Agricultural Waste Gasification Process by Integrating DoE, SVR, GA, PSO Machine Learning Technologies

Shih-Hsing  Chang; Shun-Chun  Chou; Jr-Syu  Yang

doi:10.6180/jase.202609_32.001

Research on the Optimization of Agricultural Waste Gasification Process by Integrating DoE, SVR, GA, PSO Machine Learning Technologies

Electrical Engineering Mechanical Engineering

Shih-Hsing Chang, Shun-Chun ChouThis email address is being protected from spambots. You need JavaScript enabled to view it., and Jr-Syu Yang

Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City 251301, Taiwan

Received: October 8, 2025
Accepted: December 23, 2025
Publication Date: April 4, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202609_32.001

Agricultural waste is increasingly recognized as a sustainable feedstock for clean energy generation through thermochemical gasification. This study develops a comprehensive AI-driven simulation and optimization framework to improve the efficiency of a fixed-bed gasification system. Support Vector Regression (SVR) was employed to construct a robust predictive model with high accuracy (R² > 0.90), while the Taguchi method was applied to identify and optimize critical operational factors. Key variables, including reactor temperature, air flowrate, feedstock moisture content, and catalyst ratio, were systematically evaluated for their effects on syngas yield and overall efficiency. The integrated model not only demonstrated excellent predictive performance but also revealed strong nonlinear interactions among parameters influencing gasification outcomes. Results indicate that optimal efficiency can be achieved by carefully balancing reactor temperature and feedstock moisture, in conjunction with appropriate catalyst dosing. This research provides a data-driven pathway toward intelligent optimization of biomass gasification, contributing to sustainable energy production and practical deployment of Taguchi–SVR–GA/PSO enabled clean energy technologies.

Keywords: agricultural waste; gasification; support vector regression (SVR); genetic algorithm (GA); particle swarm optimization (PSO); design of experiments (Taguchi method).

[1] A. V. Bridgwater, (2012) “Review of Fast Pyrolysis of Biomass and Product Upgrading" Biomass and Bioenergy 38: 68–94. DOI: 10.1016/j.biombioe.2011.01.048.
[2] P. Basu. Biomass Gasification and Pyrolysis: Practical Design and Theory. Burlington: Academic Press, 2010. DOI: 10.1016/B978-0-12-374988-8.00001-3.
[3] C.Cortes and V. Vapnik, (1995) “Support-Vector Net works" Machine Learning 20(3): 273–297. DOI: 10. 1007/BF00994018.
[4] Y.Pan,Q.Xu,andZ.Li,(2021)“Data-Driven Modeling of Biomass Syngas Production: A Comparison between SVR and Random Forest" Energy 214: 119053. DOI: 10.1016/j.energy.2020.119053.
[5] J. Fan and C. Yin, (2020) “Prediction of Syngas Com position and Cold Gas Efficiency Using SVR and ANN Models" Journal of Cleaner Production 271: 122558. DOI: 10.1016/j.jclepro.2020.122558.
[6] G. Taguchi, S. Chowdhury, and S. Taguchi. Robust Engineering. New York: McGraw-Hill, 2000.
[7] Y. Zhang, J. Chen, and J. Wang, (2018) “Optimization of Syngas Production from Biomass Gasification Using Response Surface Methodology" Energy Conversion and Management157:388–400. DOI:10.1016/j.enconman. 2017.12.010.
[8] D.C.Montgomery.DesignandAnalysisofExperiments. Hoboken: Wiley, 2017.
[9] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009. DOI: 10.1007/978-0-387-84858-7.
[10] T. Aung, R. Abdul Razak, and A. Rahiman, (2025) “Artificial Intelligence Methods Used in Various Aquaculture Applications: A Systematic Literature Review" Journal of the World Aquaculture Society 56(1): e13107. DOI: 10.1111/jwas.13107.
[11] Y. Liu and H. Zhou, (2022) “Hybrid SVR–PSO Approach for Energy Forecasting" Applied Energy 328: 120145. DOI: 10.1016/j.apenergy.2022.120145.
[12] M. Liao and Y. Yao, (2021) “Applications of Artificial Intelligence-Based Modeling for Bioenergy Systems: A Review" GCBBioenergy13(5):774–802. DOI:10.1111/ gcbb.12816.
[13] F. W. Tina and K. Chan, (2025) “Integrating A IoT Technologies in Biomass and Aquaculture: A Systematic Review" Future Internet 17(5): 199. DOI: 10.3390/ f i17050199.
[14] Y.-P. Huang and J. Lin, (2025) “Advances in A IoT Enabled Biomass Gasification Systems" Processes 13(1): 73. DOI: 10.3390/pr13010073.
[15] J. Shi and X. Wang, (2025) “Hydrogen Enhancement in Syngas through Biomass Catalytic Gasification" Energies 18(5): 1200. DOI: 10.3390/en18051200.
[16] S. S. Raza, R. Tariq, and M. A. Khan, (2021) “Multi Objective Optimization of Biomass Gasification via GA and PSO Integrated with Machine Learning" Applied Energy 301: 117453. DOI: 10.1016/j.apenergy.2021.117453.
[17] X. Li, Y. Zhao, and Z. Wang, (2020) “Multi-Objective Optimization of Energy and Emission in Biomass Gasification Systems" Journal of Cleaner Production 265: 121763. DOI: 10.1016/j.jclepro.2020.121763.
[18] R. Zhang, H. Sun, and Y. Wang, (2019) “Optimization of Fluidized Bed Gasification Process Parameters" Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 41(6): 729–738. DOI: 10.1080/15567036.2018.1523001.
[19] Y. Liu, H. Zhou, and K. Wang, (2022) “Energy System Performance Forecasting Using Hybrid SVR-GA Model" Energy Reports 8: 1234–1245. DOI: 10.1016/j.egyr.2022.02.001.
[20] H.ZhangandJ. Wang, (2020) “Data-Driven Optimization of Bioenergy Systems Using Machine Learning and Evolutionary Computation" Renewable and Sustain able Energy Reviews 131: 109993. DOI: 10.1016/j.rser.2020.109993.
[21] S. Sikarwar, Y. Zhao, et al., (2023) “Artificial Intelligence Models for Predicting Syngas Composition from Biomass Gasification" Energy 263: 125678. DOI: 10.1016/j.energy.2022.125678.
[22] H.YangandM.Chen, (2022) “AI-Driven Gasification Modeling: A Review of Machine Learning Applications" Renewable and Sustainable Energy Reviews 167: 112716. DOI: 10.1016/j.rser.2022.112716.
[23] M.CuiandZ.Zhang, (2023) “Data-Driven Optimization of Bioenergy Processes Integrating SVR and GA for Predictive Modeling" Energy Conversion and Management 280: 116900. DOI: 10.1016/j.enconman.2023.116900.
[24] C. ZhouandY. Li, (2023) “Recent Progress in Biomass Catalytic Gasification for Clean Energy" Journal of Cleaner Production 410: 137158. DOI: 10.1016/j. jclepro.2023.137158.

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