Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm
In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevent...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/1287306 |
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| _version_ | 1832553734915227648 |
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| author | Zhenhua Yang Hongwei Zhang Sheng Li Chaojun Fan |
| author_facet | Zhenhua Yang Hongwei Zhang Sheng Li Chaojun Fan |
| author_sort | Zhenhua Yang |
| collection | DOAJ |
| description | In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam. |
| format | Article |
| id | doaj-art-2d07ee9bc28f4d098c3df03f7ceb7ca9 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-2d07ee9bc28f4d098c3df03f7ceb7ca92025-02-03T05:53:14ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/12873061287306Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM AlgorithmZhenhua Yang0Hongwei Zhang1Sheng Li2Chaojun Fan3College of Mining, Liaoning Technical University, Fuxin, Liaoning, ChinaCollege of Mining, Liaoning Technical University, Fuxin, Liaoning, ChinaCollege of Mining, Liaoning Technical University, Fuxin, Liaoning, ChinaCollege of Mining, Liaoning Technical University, Fuxin, Liaoning, ChinaIn order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.http://dx.doi.org/10.1155/2020/1287306 |
| spellingShingle | Zhenhua Yang Hongwei Zhang Sheng Li Chaojun Fan Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm Advances in Civil Engineering |
| title | Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm |
| title_full | Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm |
| title_fullStr | Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm |
| title_full_unstemmed | Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm |
| title_short | Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm |
| title_sort | prediction of residual gas content during coal roadway tunneling based on drilling cuttings indices and ba elm algorithm |
| url | http://dx.doi.org/10.1155/2020/1287306 |
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