t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines
IR@CIMFR: CSIR-Central Institute of Mining and Fuel Research, Dhanbad
View Archive Info| Field | Value | |
| Title |
t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines
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| Creator |
Prasanjit , Dey
Saurabh, K. Kumar,, C. Pandit, D. Chaulya, S. K. Ray, S. K. Prasad, G. M, Mandal , S. K. |
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| Subject |
Mine Ventilation
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| Description |
A deep learning network is introduced to predict concentrations of gases in the underground coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas chamber. The air is sucked automatically at specific intervals from the sealed-off site utilizing a solenoid valve, suction pump, and programmed microprocessor. The gas sensors monitor the gas content in the underground coal mine and communicate gas concentration to the surface server room through a wireless network and cloud storage media. The t-SNE_VAE_bi-LSTM model is proposed in this study as a prediction model that combines the t-SNE, VAE, and bi-LSTM networks. The proposed model's t-SNE method aims to minimize the dimensionality of the recorded gas concentration; and VAE layer intends to retrieve the inner characteristics of low-dimensional gas concentration. Finally, the given model's Bi-LSTM layer tries to forecast the concentrations of CH4, CO2, CO, O2, and H2 gases. The proposed model's prediction accuracy is compared with the existing two models, namely auto-regressive integrated average moving (ARIMA) and chaos time series (CHAOS). The experiment findings demonstrate that the t-SNE_VAE_bi-LSTM model forecasted mean square error (MSE) is more accurate, and it has lesser MSE value of 0.029 and 0.069 for CH4; 0.037 and 0.019 for CO2; 0.092 and 0.92 for CO; 1.881 and 1.892 for O2; and 1.235 and 1.200 for H2 than the ARIMA and CHAOS models, respectively.
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| Publisher |
Springer
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| Date |
2021-10-21
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| Type |
Article
PeerReviewed |
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| Identifier |
Prasanjit , Dey and Saurabh, K. and Kumar,, C. and Pandit, D. and Chaulya, S. K. and Ray, S. K. and Prasad, G. M, and Mandal , S. K. (2021) t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines. Soft Computing, 25. 14183-14207 .
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| Relation |
http://cimfr.csircentral.net/2552/
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