Study on the temperature prediction model of residual coal in goaf based on ACO-KELM
Existing studies on the temperature prediction of residual coal in goafs have mainly focused on the relationship between temperature and gas concentration, with limited attention given to the complex nonlinear relationships between the residual coal temperature in the goaf, the distance from the wor...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2024-12-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18226 |
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| author | ZHAI Xiaowei WANG Chen HAO Le LI Xintian HOU Qinyuan MA Teng |
| author_facet | ZHAI Xiaowei WANG Chen HAO Le LI Xintian HOU Qinyuan MA Teng |
| author_sort | ZHAI Xiaowei |
| collection | DOAJ |
| description | Existing studies on the temperature prediction of residual coal in goafs have mainly focused on the relationship between temperature and gas concentration, with limited attention given to the complex nonlinear relationships between the residual coal temperature in the goaf, the distance from the working face, and the air leakage velocity. To address this gap, a prediction model based on ant colony optimization (ACO) and kernel extreme learning machine (KELM) (ACO-KELM) was proposed. In the 21404 working face goaf of Hulususu Coal Mine, beam tubes and distributed fiber optics were arranged to collect data on O2 concentration, CO concentration, CO2 concentration, and temperature within the goaf. Simultaneously, the air leakage intensity and horizontal distances from the working face were incorporated to construct the KELM model. ACO was employed to optimize the regularization coefficients and kernel parameters in the KELM model, thereby obtaining the best-performing hyperparameter combination and generating the optimal KELM model. Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. The coefficient of determination (R2) for the ACO-KELM model on the test set was 0.9635, which was only 0.01 lower than that of the training set, indicating that the model was not overfitted and demonstrated a high degree of accuracy. |
| format | Article |
| id | doaj-art-6b03c7df10e74a4a9ae6b81b4fa498a6 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-6b03c7df10e74a4a9ae6b81b4fa498a62025-01-23T02:17:44ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2024-12-01501212813510.13272/j.issn.1671-251x.18226Study on the temperature prediction model of residual coal in goaf based on ACO-KELMZHAI XiaoweiWANG ChenHAO LeLI XintianHOU QinyuanMA TengExisting studies on the temperature prediction of residual coal in goafs have mainly focused on the relationship between temperature and gas concentration, with limited attention given to the complex nonlinear relationships between the residual coal temperature in the goaf, the distance from the working face, and the air leakage velocity. To address this gap, a prediction model based on ant colony optimization (ACO) and kernel extreme learning machine (KELM) (ACO-KELM) was proposed. In the 21404 working face goaf of Hulususu Coal Mine, beam tubes and distributed fiber optics were arranged to collect data on O2 concentration, CO concentration, CO2 concentration, and temperature within the goaf. Simultaneously, the air leakage intensity and horizontal distances from the working face were incorporated to construct the KELM model. ACO was employed to optimize the regularization coefficients and kernel parameters in the KELM model, thereby obtaining the best-performing hyperparameter combination and generating the optimal KELM model. Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. The coefficient of determination (R2) for the ACO-KELM model on the test set was 0.9635, which was only 0.01 lower than that of the training set, indicating that the model was not overfitted and demonstrated a high degree of accuracy.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18226goaf residual coalcoal spontaneous combustionresidual coal temperature predictionkernel extreme learning machineant colony optimizationair leakage intensitygas analysis methodair leakage velocity |
| spellingShingle | ZHAI Xiaowei WANG Chen HAO Le LI Xintian HOU Qinyuan MA Teng Study on the temperature prediction model of residual coal in goaf based on ACO-KELM Gong-kuang zidonghua goaf residual coal coal spontaneous combustion residual coal temperature prediction kernel extreme learning machine ant colony optimization air leakage intensity gas analysis method air leakage velocity |
| title | Study on the temperature prediction model of residual coal in goaf based on ACO-KELM |
| title_full | Study on the temperature prediction model of residual coal in goaf based on ACO-KELM |
| title_fullStr | Study on the temperature prediction model of residual coal in goaf based on ACO-KELM |
| title_full_unstemmed | Study on the temperature prediction model of residual coal in goaf based on ACO-KELM |
| title_short | Study on the temperature prediction model of residual coal in goaf based on ACO-KELM |
| title_sort | study on the temperature prediction model of residual coal in goaf based on aco kelm |
| topic | goaf residual coal coal spontaneous combustion residual coal temperature prediction kernel extreme learning machine ant colony optimization air leakage intensity gas analysis method air leakage velocity |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.18226 |
| work_keys_str_mv | AT zhaixiaowei studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm AT wangchen studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm AT haole studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm AT lixintian studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm AT houqinyuan studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm AT mateng studyonthetemperaturepredictionmodelofresidualcoalingoafbasedonacokelm |