Journal
ADVANCES IN CIVIL ENGINEERING
Volume 2020, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2020/1287306
Keywords
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Funding
- National Natural Science Foundation of China [51674135, 51674132]
- Basic Research Project of Key Laboratory of Liaoning Provincial Education Department [LJZS004]
- Research Fund of State Key Laboratory Cultivation Base for Gas Geology and Gas Control (Henan Polytechnic University) [WS2018B05]
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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.
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