{"title":"基于BO-LightGBM的煤自燃温度预测与风险分类双预警模型","authors":"Lihua Long, Quanlin Shi, Qingjie Zhang, Jundong Hu, Hemeng Zhang","doi":"10.1016/j.psep.2025.107624","DOIUrl":null,"url":null,"abstract":"This study develops a dual-warning model for coal spontaneous combustion temperature prediction and risk classification based on BO-LightGBM. Five gas features (O₂, CO, C₂H₄, CO/ΔO₂, and C₂H₄/C₂H₆) were selected via Pearson correlation analysis, and six temperature-based risk levels were defined to characterize different combustion stages. The BO-LightGBM model was constructed using five-fold cross-validation and early stopping, with hyperparameters optimized separately for regression and classification tasks. Compared with grid search and random search, Bayesian Optimization significantly improved efficiency—reducing parameter optimization time by 91.2% and 60.0% in regression, and by 97.9% and 85.1% in classification, respectively. Performance comparisons further validate the superiority of BO-LightGBM. The model yielded a MAE of 10.9844, a MAPE of 7.57, a RMSE of 22.5728, and an R² of 0.9129 in regression tasks, and achieved an accuracy of 90.00 percent, a recall of 0.9000, a precision of 0.8920, and an F1 score of 0.8959 in classification tasks. These results confirm that BO-LightGBM offers notable advantages in both predictive accuracy and computational efficiency, providing a reliable early warning tool for spontaneous combustion risk assessment in coal mines.","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"9 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dual-Warning Model for Coal Spontaneous Combustion Temperature Prediction and Risk Classification Based on BO-LightGBM\",\"authors\":\"Lihua Long, Quanlin Shi, Qingjie Zhang, Jundong Hu, Hemeng Zhang\",\"doi\":\"10.1016/j.psep.2025.107624\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study develops a dual-warning model for coal spontaneous combustion temperature prediction and risk classification based on BO-LightGBM. Five gas features (O₂, CO, C₂H₄, CO/ΔO₂, and C₂H₄/C₂H₆) were selected via Pearson correlation analysis, and six temperature-based risk levels were defined to characterize different combustion stages. The BO-LightGBM model was constructed using five-fold cross-validation and early stopping, with hyperparameters optimized separately for regression and classification tasks. Compared with grid search and random search, Bayesian Optimization significantly improved efficiency—reducing parameter optimization time by 91.2% and 60.0% in regression, and by 97.9% and 85.1% in classification, respectively. Performance comparisons further validate the superiority of BO-LightGBM. The model yielded a MAE of 10.9844, a MAPE of 7.57, a RMSE of 22.5728, and an R² of 0.9129 in regression tasks, and achieved an accuracy of 90.00 percent, a recall of 0.9000, a precision of 0.8920, and an F1 score of 0.8959 in classification tasks. These results confirm that BO-LightGBM offers notable advantages in both predictive accuracy and computational efficiency, providing a reliable early warning tool for spontaneous combustion risk assessment in coal mines.\",\"PeriodicalId\":20743,\"journal\":{\"name\":\"Process Safety and Environmental Protection\",\"volume\":\"9 1\",\"pages\":\"\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Process Safety and Environmental Protection\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.psep.2025.107624\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Process Safety and Environmental Protection","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.psep.2025.107624","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Dual-Warning Model for Coal Spontaneous Combustion Temperature Prediction and Risk Classification Based on BO-LightGBM
This study develops a dual-warning model for coal spontaneous combustion temperature prediction and risk classification based on BO-LightGBM. Five gas features (O₂, CO, C₂H₄, CO/ΔO₂, and C₂H₄/C₂H₆) were selected via Pearson correlation analysis, and six temperature-based risk levels were defined to characterize different combustion stages. The BO-LightGBM model was constructed using five-fold cross-validation and early stopping, with hyperparameters optimized separately for regression and classification tasks. Compared with grid search and random search, Bayesian Optimization significantly improved efficiency—reducing parameter optimization time by 91.2% and 60.0% in regression, and by 97.9% and 85.1% in classification, respectively. Performance comparisons further validate the superiority of BO-LightGBM. The model yielded a MAE of 10.9844, a MAPE of 7.57, a RMSE of 22.5728, and an R² of 0.9129 in regression tasks, and achieved an accuracy of 90.00 percent, a recall of 0.9000, a precision of 0.8920, and an F1 score of 0.8959 in classification tasks. These results confirm that BO-LightGBM offers notable advantages in both predictive accuracy and computational efficiency, providing a reliable early warning tool for spontaneous combustion risk assessment in coal mines.
期刊介绍:
The Process Safety and Environmental Protection (PSEP) journal is a leading international publication that focuses on the publication of high-quality, original research papers in the field of engineering, specifically those related to the safety of industrial processes and environmental protection. The journal encourages submissions that present new developments in safety and environmental aspects, particularly those that show how research findings can be applied in process engineering design and practice.
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