Abstract
The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superior predictive accuracy. The SVR, enhanced by principal component analysis (PCA), significantly improved performance, achieved R² values of 0.879 for H2, 0.856 for CO, 0.859 for CO2, and 0.744 for CH4 on the testing dataset. It also outperformed other models in terms of RMSE, achieving exceptional precision for CH4 (0.0087), CO (0.0193), and H2 (0.0194). In contrast, RTM exhibited moderate accuracy with minimal deviations, while TEM showed the highest RMSE values across all predictions, indicating its limited reliability for real-world applications. This study advances gasification technologies by demonstrating the advantages of ML models, particularly SVR, in predicting syngas composition and supporting the global transition toward sustainable energy systems.