Abstract
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.
Highlights
The harsh and confined working conditions in underground coal mines (UCMs), have led to the listing of the mining industry as the most dangerous profession [1]
The results of Principal component analysis (PCA) were fed into the artificial neural network (ANN) model in Azure Machine Learning (AML) studio, which enabled the prediction of mine environment index (MEI)
Structures, the optimum number of neurons were determined based on mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), relative square error (RSE), and
Summary
The harsh and confined working conditions in underground coal mines (UCMs), have led to the listing of the mining industry as the most dangerous profession [1]. According to the Mine Safety and Health Administration (MSHA), faulty equipment, negligence of labor towards explosions, structure failure, and gas accumulation are the most common causes of underground mine accidents [2]. During the economic year of 2014, in the salt range coal mine in Punjab, Pakistan, more than 35% of accidents occurred due to the accumulation of toxic gases [3]. Advancements in the fields of wireless sensor network (WSNs), radio frequency identification (RFID), and cloud computing have led the way toward the development of internet of things (IoT) in the areas of Smart Grids, e-health services, home automation, and environment monitoring. With reference to UCMs, the introduction of WSNs and the concept of smart
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