Advanced Machine Learning and Gene Expression Programming Techniques for Predicting CO_(2)-Induced Alterations in Coal Strength  

作　　者：Zijian Liu Yong Shi ChuanqiLi Xiliang Zhang Jian Zhou Manoj Khandelwal 

机构地区：[1]School of Resources and Safety Engineering,Central South University,Changsha,410083,China [2]Changsha Institute of Mining Research Co.,Ltd.,Changsha,410012,China [3]State Key Laboratory of Safety and Health for Metal Mines,Ma’anshan,243000,China [4]Institute of Innovation,Science and Sustainability,Federation University Australia,Ballarat,VIC 3350,Australia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期153-183,共31页工程与科学中的计算机建模(英文)

基　　金：partially supported by the National Natural Science Foundation of China(42177164,52474121);the Outstanding Youth Project of Hunan Provincial Department of Education(23B0008).

摘　　要：Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.

关 键 词：CO_(2)-induced coal strength meta-heuristic optimization algorithms XGBoost gene expression programming model interpretability 

分 类 号：TD315[矿业工程—矿井建设]