Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application

被引:5
|
作者
Wang, Wei [1 ]
Liang, Ran [1 ]
Qi, Yun [1 ,2 ]
Cui, Xinchao [1 ]
Liu, Jiao [2 ,3 ]
Xue, Kailong [1 ]
机构
[1] Shanxi Datong Univ, Sch Coal Engn, Datong 037000, Peoples R China
[2] China Occupat Safety & Hlth Assoc, China Safety Sci Journal Editorial Dept, Beijing 100011, Peoples R China
[3] China Univ Min & Technol, Sch Emergency Management & Safety Engn, Beijing 100083, Peoples R China
来源
FIRE-SWITZERLAND | 2023年 / 6卷 / 10期
关键词
coal spontaneous combustion; limit parameters; genetic algorithm (GA); support vector machine (SVM); BP neural network; prediction model;
D O I
10.3390/fire6100381
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R2 as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R2 is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters.
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页数:15
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