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Mineral production is the mainstay of the global economy. Pakistan has huge mineral resources potential, but the contribution of minerals to the gross domestic product is not commensurate with its potential. The main reasons for the unregulated nature of the mining sector are poor organizational structure, inadequate implementation of legislation, insufficient investment, and lack of technology. Due concerns and priority of introducing advanced and safe mining methods are still awaited in Pakistan. Therefore, the role of policy makers at this stage is crucial for assessing and reviewing the current strategies for developing future strategies in order to overcome these problems. This article reviews the current situation of the mining industry in standards of statutory compliance, highlights the key issues in health and safety practices, and analyses loopholes in policies. In addition, some future implications are also put forward for the steady development of the mining sector in Pakistan. This study will help the government and mining enterprises from different perspectives in order to ensure mine safety and steady development. © 2019 American Institute of Chemical Engineers Process Saf Prog: e12041 2019

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Purpose. Gas explosion in the underground coal mines of Pakistan is the main source of coal miners' mortalities. The purpose of this article is to analyze the main causes of gas explosion in the underground coal mines of Pakistan.Methods. The study employs the Fault Tree Analysis (FTA) to understand the key root causes that lead to system failure. Particularly, this research has articulated the fault tree model in case of gas explosion in underground coal mines to analyze the root causes of this dangerous accident.Findings. This analysis has revealed that most of the root causes (4/7) with 5/10 accidents, 49/53 fatalities and 28/35 injuries resulted from primary failure of the gas explosion that poses a major threat to lives of mine workers. Similarly, the accumulation of gases and ignition are leading causes of gas explosion.Originality. FTA has been employed for the first time to understand the underlying root causes with the corresponding number of accident, fatalities and injuries of gas explosion in underground coal mines of Pakistan. This original application of FTA to the problem under discussion presents some important underlying factors which should be considered to reduce the risk of gas explosion and its related fatal and non-fatal accidents. Practical implications.The study proposes preventive strategies to lessen the fatal and non-fatal accidents resulting from gas explosions. Explicitly, Pakistan has to conduct major structural and safety management reforms.

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The traditional anchoring method of bolts has insufficient control over the surrounding rock of the coal roadway. Based on this background, full-stress anchoring technology of bolts was proposed. Firstly, a mechanical relationship model of a bolt-drawing, anchoring interface was established to obtain the equations of the axial force and obtain shear stress distribution as well as the decreasing-load transfer law of the anchoring section of bolts. Through studying the prestress-loading experimental device of bolts, we found that increasing the initial preload could increase the axial force under the same conditions and the retarded anchoring section could control the axial-force loss of bolts in the middle of the anchoring section. Under the full-stress anchoring mode, the effect of applying a pre-tightening force was better than that of applying a pre-tightening force under traditional anchoring methods. Moreover, FLAC3D (Fast Lagrangian Analysis of Continua 3D; ITASCA (Ita sca International Inc), Minnesota, USA) numerical simulation calculation was performed. Under the full-stress anchoring mode of bolts, the increased anchoring length reduced the damage of the anchoring section, with a wider control range of the rock formation and higher strength of the compressive-stress anchoring zone. Based on the above research, four methods for applying the full-stress anchoring technology of bolts in engineering were proposed. The full-stress anchoring technology of bolts in the coal roadway has been applied in the support project of the return-air roadway at working face 3204 of the Taitou Coking Coal Mine of the Xiangning Coking Coal Group, Shanxi. The maximum moving distance of the roof and floor of the roadway was reduced from 200 to 42 mm, and the maximum moving distance on both coal sides was reduced from 330 to 86 mm. The full-stress anchoring technology of bolts was able to control the surrounding rock in the coal roadway.

The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. Then, 106-point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.