(2026) International Journal of Energy Research_Machine Learning-Based Prediction of Gaseous Fuel Generation During Co-Pyrolysis of Lignin and Red Mud

Shin Y.-U.; Yoon K.; Kwon E.E.; Song H.

(John Wiley and Sons Ltd) International Journal of Energy Research ISSN: 0363907X Vol.2026 Issue.1 Article No.9050367 DOI: 10.1155/er/9050367

This study systematically investigated the generation behavior of gaseous fuels (e.g., H₂, CO, and CH₄) during the pyrolysis of waste materials (lignin and red mud) and developed machine learning (ML)-based models for accurate prediction of gas yields. Pyrolysis experiments were carried out under varying reaction atmospheres (i.e., N₂, CO₂, and N₂/CO₂) and waste material mixing ratios to comprehensively evaluate the gas generation characteristics. Pearson correlation analysis identified reaction temperature and gas flow rate as the most influential parameters governing gas yields. Three ML models—artificial neural network (ANN), XGBoost (XGB), and Extra Trees (ET)—were developed to compare the gas generation prediction performances. The ANN model achieved superior accuracy, with coefficients of determination of R² = 0.992 (H₂), 0.989 (CH₄), and 0.994 (CO). SHapley Additive exPlanations (SHAP) analysis was subsequently applied to quantify the relative contribution of each input variable to model predictions. Building on the ANN model, two-dimensional (2D) simulations were performed to explore the combined effects of temperature, gas flow rate, and red mud content on gas generation. Simulation results revealed consistent increases in H₂, CO, and CH₄ yields under specific combinations of these conditions. Maximum predicted yields of H₂ (>3.5 mol%), CH₄ (>1.2 mol%), and CO (>7.0 mol%) were identified under their respective optimal conditions. These findings demonstrate that ML-based approaches offer a robust and interpretable framework for predicting gas generation behavior and optimizing process conditions in pyrolysis systems. Copyright © 2026 Yong-Uk Shin et al. International Journal of Energy Research published by John Wiley & Sons Ltd.

This study was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00347233) and by the research fund of Hanyang University (HY-202600000060006). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00347233) and by the research fund of Hanyang University (HY-202600000060006).