Detecting the development of explosive methane–air mixtures on a longwall face remains a difficult task. Even when atmospheric monitoring systems and computational fluid dynamics modeling are used to inspect methane concentrations, they are insufficient as a real-time warning system in crucial areas, such as near cutting drums. In our previous work, the long short-term memory algorithm has been adopted in order to predict and manage explosive gas zones in longwall mining operations prior to explosions. It was found that faster and more prominent coverage of accurate real-time explosive gas accumulation predictions is possible using the proposed algorithm. However, the data that was used for training and testing was more than 12TBs. In this work, we evaluated the prediction accuracy of the proposed methodology with a smaller data set. The data was developed and extracted based on CFD model outputs with similar locations and operational characteristics. The results indicate that the algorithm can forecast explosive gas zones in 3D with an overall accuracy of 82.3 percent for a small size dataset (n=1000).

Blasting produce pollutants such as toxic fumes and dust. In underground development operations, these pollutants are diluted using the auxiliary ventilation system, which can account for up to 50% of the total electricity usage. Accurate prediction of post-blast dilution is critical as overestimation results in production loss, and underestimation results in health and safety risks for the miners. The literature review suggests that most underground mines simply standardize post-blast times based on experiences and observations. There is still a lack of detailed studies on the mechanisms and the efficiency of different ventilation systems. Previous studies have not considered the effect of the type of explosives, humidity, porosity, and muckpile profile on the post-blast dilution time. Understanding how these factors affect the post-blast dilution time is key to optimizing the efficiency of the auxiliary ventilation system. This paper addresses these issues and make the prediction of post-blast dilution time more accurate and realistic. To achieve this objective, we used the CFD techniques to simulate the blast fume dispersion and clearance in an underground development heading. The study results show that the explosives type and muckpile porosity have significant impact on the blast fume dilution time.

Reducing Diesel Particulate Matter has a direct impact on improving air quality and thus workforce health and safety. PT Freeport Indonesia has selected a multi-stage reduction strategy to reduce DPM emissions in its underground operations. This paper provides an update on challenges, success and lessons learned during the first two stages of the Emission Assisted Maintenance (EAM) program, including mobile equipment selection for trials and instrumentation used for tailpipe measurements. The first stage consists of various Diesel Particulate Filter (DPF) trials to measure the effectiveness and feasibility of a large scale roll out. Use of new engine technologies was trialed in the second stage. Additionally, the PT Freeport Indonesia's EAM program includes wet and traditional DPF cleaning stations and a Digital Data Management dashboard to control, monitor and measure success. Lastly, the paper summarizes the impact of high altitude in engine emissions and DPF life as well as providing insight regarding the opportunity to scale up the EAM program.

Prediction of methane gas concentrations in underground coal mines has been a topic of interest for academia and the mining industry for decades as methane gas is considered a significant hazard to mining safety and productivity. This study utilizes multivariate time series forecasting approaches to predict methane emissions in an active mine. The multivariate method incorporates the past values of methane gas concentrations as a predictor of its future values but also integrates the correlations between gas concentration and barometric pressure as well as barometric pressure and coal production rate in predictive models of methane gas emissions. The data used in this study were collected from the atmospheric monitoring systems of an underground coal mine operation in the eastern USA.
The data were stored, pre-processed, and statistically analyzed to develop multivariate forecasting models based on the Vector Autoregression (VAR) and Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) methods. The optimal model for each method was selected based on the Akaike Information Criterion. The forecasting performance and accuracy of each model were assessed using cross-validation to establish the best overall model. It was concluded that both VAR and ARIMAX could, in most cases, adequately forecast methane gas concentrations in underground coal mines.