^ Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm | Structurae
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Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm

Auteur(s):



Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-8
DOI: 10.1155/2020/1287306
Abstrait:

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.

Copyright: © Zhenhua Yang et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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  • Reference-ID
    10421182
  • Publié(e) le:
    02.05.2020
  • Modifié(e) le:
    02.06.2021