The release of methane into the working area of coal mines can affect safety and production. Therefore, methane drainage reduces the risk of explosion and can be used as a fuel and energy source. One methane drainage method is cross‐measure borehole drilling, which involves drilling boreholes from tailgate roadways to the roof or floor layers of a mined seam. In this method, determining the appropriate distance between methane drainage stations has a significant impact on increasing the efficiency of operation and reducing drilling costs and the time required for drainage operations from each station. In this paper, a new 3D numerical model is developed for methane drainage from an underground coal mine to determine the suitable distance between methane drainage stations in the cross‐measure borehole method using COMSOL Multiphysics software. For this purpose, by considering the porous media flow module and the laws related to the movement of gas in the goaf, different numerical models for distances of 10, 15, 20, 25, and 30 m were simulated. The simulation results showed that by reducing the distance between methane drainage stations, the speed of gas flow in the borehole and the amount of gas removed from the boreholes in a similar period of time increase to an acceptable level. In addition, the implementation of this model in a case study showed that a distance of 10 m between drainage stations increases the efficiency of boreholes to approximately 20%. Numerical simulation in this study can provide basic support for the design of drainage boreholes and reduce the risk of gas in underground coal mines.

A large amount of gas is emitted during underground mining processes, so mining productivity decreases and safety risks increase. Efficient methane drainage from the coal seam and surrounding rocks in underground mines not only improves safety but also leads to higher productivity. Methane drainage must be performed when the ventilation air cannot dilute the methane emissions in the mine to a level below the allowed limits. The cross-measure borehole method is one of the methane drainage methods that involves drilling boreholes from the tailgate roadway to an un-stressed zone in the roof or floor stratum of a mined seam. This is the main method used in Tabas coal mine N 1. One of the effective parameters in this method is the distance between methane drainage stations, which has a direct effect on the length of boreholes required for drainage. This study was based on the measurement of ventilation air methane by methane sensors and anemometers placed at the longwall panel as well as measuring the amount of methane drainage. Moreover, in this study, the obtained and analyzed data were used to determine the suitable distance between methane drainage stations based on the cross-measure borehole method. In a field test, three borehole arrangements with different station distances in Panel E4 of Tabas coal mine N 1 were investigated. Then, the amounts of gas drained from these arrangements were compared with each other. The highest methane drainage efficiency was achieved for distances in the range of 9-12 m between methane drainage stations.

Methane release during mining exploitation represents a severe threat to miners’ safety. Methane gas can be removed from coal seams using predrainage and postdrainage techniques and used to generate electricity or for household purposes. One of the postdrainage methods is the cross‐measure borehole method, which involves drilling boreholes from the tailgate roadway to an unstressed zone in the roof or floor layers of a mined seam. To achieve high efficiency of gas draining, predicting and determining the optimum range of design elements in gas drainage operation is necessary. The distance between the methane drainage (MD) stations is one of the most important parameters for determining the amount of gas removed. This study was conducted on the basis of the measurement data of MD in the Tabas coal mine. In this study, hybrid multilayer perceptron (MLP) neural networks and gray wolf optimizer (GWO) algorithm were employed to predict and optimize the gas drainage process. Therefore, the technical parameters of MD boreholes, including panel properties, advanced speed, and joint density, were considered. Then, a hybrid artificial neural network (ANN)–GWO algorithm was developed in the MATLAB programing environment, and the performance of the model was evaluated using statistical criteria such as regression relationships, correlation coefficients between actual and predicted values, and average relative error percentage. Applying the presented model can increase the MD efficiency to an acceptable range. The results showed an average reduction in the distance between the MD stations from 20 to 10 m (assuming that other technical parameters of the boreholes remain constant in the cross‐measurer boreholes method), and the MD efficiency increases by ~20%–50%. Finally, the efficiency of gas output from the mine will be improved, and a balance will be struck between methane removed with ventilation air methane (VAM) and MD.

Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.