INTELLIGENT MODEL FOR PREDICTING METHANE CONCENTRATION IN INDUSTRIAL ENVIRONMENTS BASED ON IoT GAS ANALYZERS AND HYBRID MACHINE LEARNING ALGORITHMS
DOI:
https://doi.org/10.37943/APTT7109Keywords:
Methane monitoring, IoT sensors, gas analyzer, predictive analytics, machine learning, LSTM, XGBoost, industrial safety, gas environment monitoringAbstract
Ensuring industrial safety at enterprises that use combustible and toxic gases requires continuous monitoring of the gas environment and timely identification of potentially hazardous situations. One of the most serious threats in the mining and industrial sectors is the accumulation of methane, which has a high explosion hazard and can lead to technogenic accidents. Traditional monitoring systems based on threshold alarm algorithms allow detection of exceedances of permissible gas concentrations; however, they do not provide the capability to forecast the development of hazardous situations.
This paper proposes an intelligent model for predicting methane concentration based on data obtained from IoT gas analyzers of the SENSOR - Mine 4GN series. The monitoring system integrates a distributed network of gas sensors, wireless data transmission, and machine learning algorithms for time-series analysis of gas concentration measurements.
Experimental data were obtained under laboratory conditions over a ten-day period using certified calibration gas mixtures simulating methane release events under different temperature conditions. For time-series analysis, a hybrid machine learning model was applied, combining a Long Short-Term Memory (LSTM) recurrent neural network and the ensemble algorithm XGBoost.
The obtained results demonstrate that the proposed model improves the accuracy of methane concentration dynamics prediction and enables early detection of hazardous trends in the gas environment. The use of predictive analytics methods combined with Internet of Things (IoT) gas analyzers significantly increases the effectiveness of industrial safety systems and reduces the risk of accidents at industrial facilities. The proposed methodology can be applied as a foundation for the development of intelligent predictive monitoring systems for mining, energy, and other hazardous industrial environments.
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