ABSTRACT

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.

摘要

Hardgrove 可磨性指数 (HGI) 是衡量煤的抗破碎性的指标。由于煤的复杂结构，HGI受多种因素影响。本研究基于 329 个波兰煤炭样本，检验了近似分析和最终分析以及微晶含量对 HGI 的影响。在这项研究中，机器学习技术 XGBoost（Extreme Gradient Boosting Regressor）被用于开发具有令人满意的准确度的 HGI 预测模型（R ² = 0.86)。Shapley 加性解释 (SHAP) 技术用于解释预测值与输入数据之间的关系。研究表明，水分（负面影响）、碳（正面）、挥发物（负面）、镜质体（正面）和脂质体（负面）含量对 HGI 的影响最大。此外，选择了三种在可磨性方面显着不同的实验获得的混煤来测试模型。结果证实了该方法在用于共混物时的有效性。已经彻底检查了各个煤参数对混合物的预测可磨性的影响。