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

Abstract

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; the presented model's VAE layer intends to retrieve the inner characteristics of low-dimension 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.

Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

“…Other unsupervised models were also tried for feature extraction such as autoencoder and a deep belief network [34,[57][58][59]. Autoencoder is a feed-forward network structure aimed at recovering input at its output by minimizing mean square error; a stacked autoencoder refers to an autoencoder with more than one hidden layer.…”

“…Compared to other dimensionality reduction methods including PCA and Kernel-PCA, feature representations from the GRU-AE were more distinguishable and effectively improved classification performance. Other unsupervised models were also tried for feature extraction such as autoe coder and a deep belief network [34,[57][58][59]. Autoencoder is a feed-forward network stru ture aimed at recovering input at its output by minimizing mean square error; a stacke autoencoder refers to an autoencoder with more than one hidden layer.…”

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

“…Meng et al [ 24 ] proposed a novel prediction method that combines classical time series analysis with these deep learning models. Dey et al [ 25 ] proposed the t-SNE_VAE_bi-LSTM prediction model that combines the t-SNE, VAE, and bi-LSTM networks. Xu et al [ 26 ] constructed a new IWOA-LSTM-CEEMDAN prediction model based on the improved whale optimization algorithm (IWOA).…”

A Spark Streaming-Based Early Warning Model for Gas Concentration Prediction

“…Since gas concentration data have strong time series characteristics [20,21], the algorithms in the above studies are able to predict gas concentration well, but most of the above studies focus on the accuracy of data prediction, and the prediction efficiency is mostly dependent on the operation efficiency of the model itself, which actually has certain limitations for improving the prediction efficiency. In this paper, we propose a gas concentration prediction model incorporating the particle swarm optimisation algorithm (PSO) and a gated recurrent unit (GRU) model under the Spark Streaming framework (SSF), which can further improve the model's operational efficiency due to the advantages of fast data processing and fault tolerance of the SSF.…”