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Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN

Author

Listed:
  • Shuang Song

    (College of Energy, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Shugang Li

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Tianjun Zhang

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Li Ma

    (College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Shaobo Pan

    (College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Lu Gao

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

Suggested Citation

  • Shuang Song & Shugang Li & Tianjun Zhang & Li Ma & Shaobo Pan & Lu Gao, 2021. "Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN," Energies, MDPI, vol. 14(5), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1384-:d:509579
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    References listed on IDEAS

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    1. Wu Xiang & Qian Jian-sheng & Huang Cheng-hua & Zhang Li, 2014. "Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, July.
    2. Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
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    Citations

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    Cited by:

    1. Xiangqian Wang & Ningke Xu & Xiangrui Meng & Haoqian Chang, 2022. "Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
    2. Xiangrui Meng & Haoqian Chang & Xiangqian Wang, 2022. "Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis," Energies, MDPI, vol. 15(6), pages 1-15, March.

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