Review on Machine Learning-Based Underground Coal Mines Gas Hazard Identification and Estimation Techniques

Sharma, Mayank; Maity, Tanmoy

doi:10.1007/s11831-023-09982-1

Cited by 3 publications

(1 citation statement)

References 88 publications

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“…The conducted research was focused on two powerful ensemble methods: bootstrap aggregation and boosting, both relying on random forests as the base models. Ensemble methods are well suited for enhancing prediction accuracy and reducing overfitting by combining multiple models to produce a robust and generalized result of gas emission [36].…”

Section: Introductionmentioning

confidence: 99%

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

Kotyla,

Banasiewicz,

Krot

et al. 2024

Electronics

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The mining industry faces persistent challenges related to hazardous gas emissions. Diesel engine-powered wheeled vehicles are commonly used during work shifts and are a primary source of nitrogen oxides (NOx) in underground mines. Despite diesel engine manufacturers providing gas generation data, mining companies need to predict NOx emissions from numerous load-haul-dumping (LHD) vehicles operating under dynamic conditions and not always equipped with gas sensors. This study focused on two ensemble methods: bootstrap aggregation (bagging) and least-square boosting (boosting) to predict NOx emissions. These approaches combine multiple weaker statistical models to yield a robust result. The innovation of this research is in the statistical analysis and selection of LHD vehicles’ working parameters, which are most suitable for NOx emission prediction; development of the procedure of source data cleaning and processing, model building and analyzing factors, which may influence the accuracy; and the comparison of two ensemble methods and showing their advantages and limitations for this specific engineering application, which was not previously reported in the literature. For datasets obtained from the same LHD vehicle and different operators, the more efficient bagging method gave a coefficient of determination R2 > 0.79 and the RMSE (root mean square error) was under 30 ppm, which is comparable with the measurement accuracy for transient regimes of physical NOx sensors available in the market. The obtained insights can be utilized as input for mine ventilation systems, enhancing mining transport management, reducing workplace air pollution, improving work planning, and enhancing personnel safety.

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Section: Introductionmentioning

confidence: 99%

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

Kotyla,

Banasiewicz,

Krot

et al. 2024

Electronics

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Discovering the underground coal mining accident patterns in Spain from 2003 to 2021: Insights through machine learning techniques

Li,

Sanmiquel,

Zhang

et al. 2025

Safety Science

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Review of Major Influencing Factors Contributing to Persisting Safety Problems in Coal Mines: Addressing Systemic Challenges

Wang,

Zheng,

Shahani

et al. 2024

Applied Sciences

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The coal mining industry in Pakistan faces recurring fatal accidents due to data scarcity, lack of research, and technology adoption. This paper reviews the current status of coal mining, the ongoing energy crisis, the utilization of indigenous coal resources, and the prevailing safety challenges. By comparing coal mining safety standards in Pakistan with global benchmarks, this study proposes advanced mining technologies to improve productivity and safety. This study emphasizes the importance of investigating the underlying factors causing mining accidents in order to devise effective strategies to mitigate them. The lack of relevant data and the reluctance to adopt technology in the industry are identified as major obstacles to improving safety conditions. The proposed strategies for overcoming safety issues include improving data collection and analysis, increasing research efforts, and promoting the adoption of advanced technology. Thus, this paper highlights the urgent need to address safety concerns in Pakistan’s coal mining industry to avoid further loss of lives and resources.

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Review on Machine Learning-Based Underground Coal Mines Gas Hazard Identification and Estimation Techniques

Cited by 3 publications

References 88 publications

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

NOx Emission Prediction of Diesel Vehicles in Deep Underground Mines Using Ensemble Methods

Discovering the underground coal mining accident patterns in Spain from 2003 to 2021: Insights through machine learning techniques

Review of Major Influencing Factors Contributing to Persisting Safety Problems in Coal Mines: Addressing Systemic Challenges

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