International Journal of Mining and Mineral Engineering (5 papers in press)

Regular Issues

• Optimise a Ventilation System in Underground Mines using Artificial Neural Networks  
  by Marco Antonio Cotrina Teatino, Jose Nestor Mamani Quispe, Solio Marino Arango Retamozo, Jairo Jhonatan Marquina Araujo, Eduardo Manuel Noriega Vidal, Dominga Cano Ccoa, Teofilo Donaires Flores, Joe Alexis Gonzalez Vasquez, Tomas Anticona Cueva 
  Abstract: This study aimed to optimise ventilation systems in underground mines using artificial neural networks (ANNs) to reduce temperature and relative humidity while improving airflow. A dataset of 66 samples was used to train the ANN model, which included 4 input parameters, 128 and 64 hidden neurons, and 3 output parameters. The model was tested with 70% of data in training and 30% in validation. The results demonstrated that the ANN showed strong predictive performance for temperature and humidity, achieving R2 values of 0.88 and 0.96, respectively, in the validation data. Additionally, the model achieved an R2 of 0.81 for airflow data, indicating reliable predictions. The ANN also successfully optimised the ventilation parameters, resulting in a temperature reduction of 6.13
  Keywords: Ventilation; underground mines; artificial neural networks.
  DOI: 10.1504/IJMME.2025.10068388
   
  
• Reinforcement by Sections Control Technology of Dynamic Condition of Roadway Driven Heading for Adjacent Advancing Coal Face Surrounding Rock  
  by Wensong Xu, Cuian Liu, Guangming Zhao, Wenqing Meng, Chongyan Liu, Qingheng Gu, Xinwen Wu 
  Abstract: In order to ensure the integrity of roadway roof and improve the stability of roadway, the evolution characteristics of rock pressure in roadway surrounding rock are analysed. By establishing the mechanical model of surrounding rock structure, the mechanical characteristics of rock layer that covers the coal seam are researched, and the influence range of surrounding rock in roadway is analysed. Based on the control theory of roadway surrounding rock, the control principle of roadway surrounding rock is analysed, the difficulties of roadway surrounding rock control are summarised and the corresponding measures are given respectively. According to the interaction degree of dynamic pressure during con-struction of large and small structures, the control technology of roadway surrounding rock is proposed and the industrial test is carried out in view of the deformation of roadway surrounding rock in full-mechanised caving face.
  Keywords: Drive in the direction of the coal face ; Surrounding rock ; Stability ; Reinforcement by sections ; Optical fiber monitoring.
  DOI: 10.1504/IJMME.2025.10070273
   
  
• Prediction of Thermal Conductivity of Quartz Chlorite Schist Rocks: a Comparative Study of MLR and Ridge Regression  
  by Anup Tripathi, Samir Kumar Pal, Gurram Dileep, Aman Raj 
  Abstract: Thermal conductivity is a key physical property with broad applications in engineering and geosciences, particularly in energy-efficient building design, geothermal energy systems, and subsurface geological studies. Accurate determination of thermal conductivity is essential for understanding heat transfer mechanisms in rock materials. However, direct in-situ measurement is often impractical due to technical and logistical constraints. As a result, indirect estimation methods, which establish empirical relationships between thermal conductivity and various physico-mechanical properties, have gained attention. This study investigates the thermal conductivity of Quartz Chlorite Schist through laboratory experiments, alongside measuring key physico-mechanical properties, including P-wave velocity, porosity, density, and uniaxial compressive strength (UCS). The objective is to analyse the correlations between thermal conductivity and these properties to develop a reliable predictive model. Multiple regression and ridge regression analysis are employed to derive an empirical equation for estimating thermal conductivity based on the measured parameters. The findings of this study contribute to improving indirect assessment techniques, which are valuable for geotechnical and geological applications where direct measurements are challenging.
  Keywords: Thermal Conductivity; P-wave velocity; machine learning; predictive analysis.
  DOI: 10.1504/IJMME.2025.10070497
   
  
• Machine Learning Tool to Minimise and Predict Airblast During Blasting and to Optimise the Design of Blasting Operations  
  by Onalethata Saubi, Rodrigo. S. Jamisola Jr, Raymond Suglo, Oduetse Matsebe 
  Abstract: We present a method to minimise and predict airblast in blasting operations in an open-pit Debswana diamond mine. Blast engineers can use this tool to optimise their blast design to achieve desired blasting operation effect, i.e., airblast. The major novelty of this study is on the creation of a ninedimensional solution space, optimisation of the blast design parameters, and minimisation of airblast using gradient descent method. We develop a solution surface using artificial neural network (ANN). This is our best-performing machine learning model compared to the three other models used, namely, support vector machine (SVM), k-nearest neighbour (k-NN), and random forest (RF). The computed nine-dimensional solution space has eight input parameters: stemming, distance from the blast face to the monitoring point, burden, powder factor, hole diameter, maximum charge per delay, spacing, and hole depth. Sensitivity analysis revealed that stemming is the most sensitive input parameter while spacing is the least sensitive. The minimum value of airblast computed in this study through unconstrained optimisation is around 40 dB, which is approximately equivalent to the sound of a whisper. This framework is adaptable to various geological and operational settings, highlighting its broader applicability in improving environmental compliance and blasting efficiency.
  Keywords: Airblast; machine learning; blast design; optimisation; sensitivity analysis; open-pit diamond mine.
  DOI: 10.1504/IJMME.2025.10070547
   
  
• Investigating the Performance of a Multilayer Capillary Barrier in Mitigating Acid Mine Drainage  
  by Mahsa Sadeghi Vazin, Ali Akhtarpour, Mohsen Karrabi, Farzad Daliri, Mohammad Saleh Baradaran 
  Abstract: Mining operations generate vast quantities of tailings, often leading to AMD, a major environmental concern. This study evaluates the performance of a multilayer capillary barrier system in controlling AMD at the Sungun copper mine through both experimental and numerical approaches. A glass column experiment was conducted with a 50cm fine-grained layer sandwiched between two 30cm coarse-grained layers. Results demonstrated that the fine-grained layer retained more than 85% saturation even under a 40day drought period, reducing moisture loss by only 3%. The capillary barrier effectively restricted oxygen diffusion, maintaining oxygen penetration levels below 1020 kg/day, significantly reducing sulphide oxidation. Numerical modelling using SEEP/W and CTRAN/W confirmed the experimental findings, with deviations of less than 2%. The study highlights the long-term effectiveness of capillary barriers in AMD prevention, making them a sustainable alternative for tailings management. These results provide critical insights for designing cost-effective and environmentally friendly mine closure strategies.
  Keywords: capillarity barrier coating; acid mine drainage; oxygen release; unsaturated soil; mine tailings; coating performance; multilayer capillary barrier; environmental impacts.
  DOI: 10.1504/IJMME.2025.10071359