Prediction of hydrogen and methane yields from gasification of leather waste using machine learning and explainable...

Accurately predicting syngas composition is essential for optimizing energy production and ensuring environmental sustainability. Despite the growing use of machine learning techniques in this field, publicly available datasets remain limited, and existing datasets contain relatively few samples. To bridge this gap, we generated a comprehensive dataset of 3748 samples under controlled laboratory conditions and publicly shared it on Kaggle (https://www.kaggle.com/datasets/miracnurciner/gasification-dataset). This study aims to identify the most successful machine learning model for predicting H₂ and CH₄ gas concentrations by evaluating nine models: Random Forest (RF), Linear Regression (LR), Decision Tree (DT), Support Vector Regression (Linear and RBF), K-Nearest Neighbors (KNN), Gradient Boosting Regressor (GBR), XGBoost, CatBoost, and LightGBM. Model performance was assessed using multiple metrics, including the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and explained variance score (EVS). The Friedman test was applied to evaluate the statistical significance of performance differences among the models. The results show that the KNN model achieved the highest predictive performance for both H₂ (R² = 0.987, RMSE = 1.253) and CH₄ (R² = 0.979, RMSE = 0.920). Friedman test shows that the performance differences between the models are statistically significant (p < 0.001). By integrating Shapley Additive Explanations (SHAP) into the model, the contribution of each feature to the prediction results is clarified. SHAP analysis highlights that temperature and time are the main features affecting H₂ and CH₄ gas. This study highlights the potential of machine learning techniques for biomass gas prediction and advocates for integrating Explainable AI (XAI) methods, establishing a robust foundation for future research. Furthermore, by providing a large, publicly available dataset, this research significantly advances studies in syngas composition prediction.