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

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 flow rate, 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.