Predicting the hardgrove grindability index using interpretable decision tree-based machine learning models

Abstract

• Six decision tree-based models were used to predict HGI, demonstrating their effectiveness in coal grindability prediction. • All models achieved high predictive accuracy, with Optuna-NGBoost performing best on the test set (R²=0.9715). • Interpretability analysis revealed the specific impact of each input feature on the model's prediction outcomes. • Provided an efficient and interpretable machine learning framework for HGI prediction in the coal industry. The Hardgrove grindability index (HGI) is a crucial indicator for assessing the grindability of coal, and accurate prediction of HGI is essential for improving the production efficiency and economic benefits of the coal industry. This study employed six decision tree-based machine learning models to predict the HGI values of 129 coal samples, with hyperparameter optimization performed using Optuna, and model interpretability analyzed using SHapley Additive exPlanations (SHAP). The results showed that the optimized natural gradient boosting (NGBoost) model outperformed all other models, which achieved the highest performance on the test set with a coefficient of determination (R 2 ) of 0.9715, a mean absolute error (MAE) of 1.1507, and a root mean squared error (RMSE) of 1.4735. SHAP analysis further revealed that volatile matter ( VM ) contributed the most to the model’s predictions, while pyrite ( FeS 2 ) had the least contribution. This study provides an efficient machine learning approach for accurate HGI prediction, offering excellent predictive performance, interpretability, and application value.