Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application
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 mea...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/6/10/381 |
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| author | Wei Wang Ran Liang Yun Qi Xinchao Cui Jiao Liu Kailong Xue |
| author_facet | Wei Wang Ran Liang Yun Qi Xinchao Cui Jiao Liu Kailong Xue |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-10T21:15:24Z |
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| id | doaj.art-26cea91ba7074673a16c9b75f53bc853 |
| institution | Directory Open Access Journal |
| issn | 2571-6255 |
| language | English |
| last_indexed | 2024-03-10T21:15:24Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
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| series | Fire |
| spelling | doaj.art-26cea91ba7074673a16c9b75f53bc8532023-11-19T16:27:03ZengMDPI AGFire2571-62552023-10-0161038110.3390/fire6100381Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its ApplicationWei Wang0Ran Liang1Yun Qi2Xinchao Cui3Jiao Liu4Kailong Xue5School of Coal Engineering, Shanxi Datong University, Datong 037000, ChinaSchool of Coal Engineering, Shanxi Datong University, Datong 037000, ChinaSchool of Coal Engineering, Shanxi Datong University, Datong 037000, ChinaSchool of Coal Engineering, Shanxi Datong University, Datong 037000, ChinaChina Safety Science Journal Editorial Department, China Occupational Safety and Health Association, Beijing 100011, ChinaSchool of Coal Engineering, Shanxi Datong University, Datong 037000, ChinaThe 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 R<sup>2</sup> 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 R<sup>2</sup> 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.https://www.mdpi.com/2571-6255/6/10/381coal spontaneous combustionlimit parametersgenetic algorithm (GA)support vector machine (SVM)BP neural networkprediction model |
| spellingShingle | Wei Wang Ran Liang Yun Qi Xinchao Cui Jiao Liu Kailong Xue Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application Fire coal spontaneous combustion limit parameters genetic algorithm (GA) support vector machine (SVM) BP neural network prediction model |
| title | Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application |
| title_full | Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application |
| title_fullStr | Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application |
| title_full_unstemmed | Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application |
| title_short | Study on the Prediction Model of Coal Spontaneous Combustion Limit Parameters and Its Application |
| title_sort | study on the prediction model of coal spontaneous combustion limit parameters and its application |
| topic | coal spontaneous combustion limit parameters genetic algorithm (GA) support vector machine (SVM) BP neural network prediction model |
| url | https://www.mdpi.com/2571-6255/6/10/381 |
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