An approach based on an artificial neural network (ANN) for the prediction of NOx emissions from underground load–haul–dumping (LHD) vehicles powered by diesel engines is proposed. A Feed-Forward Neural Network, the Multi-Layer Perceptron (MLP), is used to establish a nonlinear relationship between input and output layers. The predicted values of NOx emissions have less than 15% error compared to the real values measured by the LHD onboard monitoring system by the standard sensor. This is considered quite good efficiency for dynamic behaviour prediction of extremely complex systems. The achieved accuracy of NOx prediction allows the application of the ANN-based “soft sensor” in environmental impact estimation and ventilation system demand planning, which depends on the number of working LHDs in the underground mine. The proposed solution to model NOx concentrations from mining machines will help to provide a better understanding of the atmosphere of the working environment and will also contribute to improving the safety of underground crews.

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.

The article evaluates the impact of blasting operations with explosives on hydrogen sulfide (H₂S) emissions from rock mass in a copper ore mine. The study showed that detonations of explosives cause increased release of H₂S and other gases from the rock mass interior into the mine workings space. Analysis of changes in H₂S concentration over a period of ±60 min relative to the moment of detonation, performed for data from 2014 and 2017, revealed significant differences in gas behavior. In 2014, the average H₂S concentration decreased after the blast, while in 2017, a marked increase was observed, although the absolute values were lower than in the previous period. The average time to reach the maximum concentration of H₂S after an explosion in 2014 was 24 min and 25 s, and in 2017, 29 min and 22 s. Stabilization of the mine atmosphere occurred in 2014 after 58 min and 15 s, and in 2017 after about 40 min and 57 s. In none of the analyzed periods did the concentration values exceed the threshold of 7 ppm, which means that the level of H₂S did not reach the values considered dangerous for the crew. The results indicate that blasting works significantly affect the dynamics of gas release from the rock mass, but do not pose a threat under the conditions studied.