An estimation method for gas extraction meets standards based on LSTM with adaptive step

Hu Jincheng; Zhang Libin; Jiang Ze; Yao Chaoxiu; Jiang Zhilong

doi:10.1117/12.3005783

10 October 2023 An estimation method for gas extraction meets standards based on LSTM with adaptive step

Hu Jincheng, Zhang Libin, Jiang Ze, Yao Chaoxiu, Jiang Zhilong

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993D (2023) https://doi.org/10.1117/12.3005783
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

In view of the requirement that coal mining enterprises should establish the work system to evaluate the gas extraction meets standards, this paper adopts the deep learning technology——long short term memory (LSTM). The proposed method, firstly processes the outlier and missing value of the coal mine monitoring data, selects the key indicators that affect the extraction volume. Then uses LSTM network model to train and predict. Finally, based on the predicted gas extraction volume, determine whether it meets the standard and estimate the number of days required to consistently meet the standard. LSTM, RNN and GM are applied to the estimation of gas extraction volume and standard on on-site process data. The experimental results show that the constructed LSTM model has better performance estimation than RNN, GM, and can perform more accurate gas extraction compliance estimation, ensuring the safety and efficient production of coal mines.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hu Jincheng, Zhang Libin, Jiang Ze, Yao Chaoxiu, and Jiang Zhilong "An estimation method for gas extraction meets standards based on LSTM with adaptive step", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993D (10 October 2023); https://doi.org/10.1117/12.3005783

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