The Hardgrove Grindability Index (HGI) is a measure of coal’s resistance to crushing. HGI is influenced by many factors due to the complex structure of coal. This study examines the effect of the proximate and ultimate analysis and maceral content on HGI, based on 329 samples of Polish coals. In this study, a machine learning technique XGBoost (Extreme gradient boosting regressor) was used to develop a predictive model of HGI with satisfactory accuracy (R² = 0.86). The Shapley additive explanations (SHAP) technique was used to explain the relationship between the predicted value and the input data. Studies have shown that the moisture (negative impact), carbon (positive), volatile matter (negative), vitrinite (positive) and liptinite (negative) content have the greatest impact on HGI. Additionally, three experimentally obtained coal blends that differ significantly in the degree of grindability were selected for testing the model. The results confirmed the effectiveness of the method when used also for blends. The influence of individual coal parameters on the predicted grindability of the blends has been thoroughly examined.

The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R²) of the prediction has reached satisfactory value of 0.88.

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informuj?ce swoich czytelnik?w o najnowszych osi?gni?ciach naukowych i nowoczesnych rozwi?zaniach technicznych w Polsce i na ?wiecie, popularyzuje problemy techniczne oraz poszerza wiedz? i kultur? techniczn?.