International Journal of Mining and Mineral Engineering (8 papers in press)

Regular Issues

• 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
   
  
• Impact of Infrared Drying on Strength and Storage Characteristics of Iron Ore briquettes  
  by Rishi Sharma, Devidas Sahebraoji Nimaje 
  Abstract: The demand for steel products has surged in our infrastructure dependent 21st century world. With iron ore reserves depleting rapidly, it is crucial to utilize iron ore fines and metallurgical waste effectively in the steel industry. To address this, the agglomeration process is employed to make proper use of these fines and metallurgical wastes, such as flue dust and LD sludge, by combining them with bentonite and cement as binders to form cylindrical briquettes. The drying was used in the production of iron ore agglomerates achieving sufficient initial strength which is crucial to prevent breakage during transportation, handling, and storage. Unlike traditional processes such as sintering and nodulizing which require heating the agglomerates to high temperatures (around 1100
  Keywords: Infrared; briquette; drying; storage characteristics; agglomeration; palletization.
  DOI: 10.1504/IJMME.2025.10071673
   
  
• Acid Mine Drainage and Metal Leaching Potential of a Waste Rock Dump  
  by James Kusi, Gordon Foli, Yaw Michael Peasah, Osei Akoto 
  Abstract: Acid mine drainage (AMD) is identified as one of the key contributors to environmental hazard in the gold mining towns. This issue is particularly related to deposits (mine waste rocks) containing sulphide minerals, which are prone to oxidation under the influence of atmospheric oxygen and water. This study therefore assesses Sansu waste rock dumps acid generating capacity and its metal leaching potential. Fifteen (15) waste rock samples were obtained from various areas of the dump and analysed, using ICP-MS and ICP-AES devices, while XRD analysis was used to evaluate the mineralogy of the samples. The acid base accounting (ABA) test was used to determine the dumps potential AMD. To further establish the dumps AMD potential and determine the metal leaching potential, a column leaching test was conducted. Correlation and principal component analysis was determined. One notable finding was the oxidation-neutralisation curve, which indicated a high neutralisation capacity at the site.
  Keywords: Acid base accounting test; major elements; heavy metals; oxidizing-neutralizing curve; acid neutralisation capacity; sulphidic minerals.
  DOI: 10.1504/IJMME.2025.10071834
   
  
• Thermo-hydrodynamic behaviour of a Newtonian fluid in a backward facing step including a fixed cylinder equipped with an elastic fin  
  by Chaoufi Ali, Mohamed Bouanini, Abderrahim Mokhefi, Badraoui Ahmed 
  Abstract: In this paper, a numerical study of the thermo-hydrodynamic behaviour of a Newtonian fluid in the presence of an elastic fin subjected to a laminar flow of a fluid inside a backward-facing step tube has been presented. The solid fin is fixed to a cylindrical obstacle right next to the abrupt widening part of the backward-facing step. The lower part of this tube has been exposed to a hot temperature, while the fluid at the tube inlet is subjected to a cold temperature. Our aim is to highlight the influence of the inertia and elasticity of the solid fin on the hydrodynamic and thermal structures of the fluid on the one hand, and on its deformation on the other hand. The differential system describing the physical phenomena including the equations governing forced convection and deformation of the structure has been solved by using the finite element method taking into account the Eulerian-Lagrangian formulation (ALE). Numerical results indicated that the heat transfer can be relatively affected by the variation of the elastic modulus of the fin material. The effect of these control parameters on the Nusselt number is studied.
  Keywords: backward facing step; BFS; fluid-structure interaction; FSI; elastic fin; heat transfer; laminar flow.
  DOI: 10.1504/IJMME.2025.10071674
   
  
• Optimise a ventilation system in underground mines using artificial neural networks  
  by Marco Cotrina, José Mamani, Solio Arango, Jairo Marquina, Eduardo Noriega, Dominga Cano, Teofilo Donaires, Joe Gonzalez, Tomas Anticona 
  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°C, a 5.61% decrease in humidity, and an increase of 110.80 CFM in airflow. In conclusion, the study confirms that artificial neural networks can effectively optimise a ventilation system in mines.
  Keywords: ventilation; underground mines; artificial neural networks; ANNs.
  DOI: 10.1504/IJMME.2025.10068388
   
  
• Machine learning tool to minimise and predict airblast during blasting and to optimise the design of blasting operations  
  by Onalethata Saubi, Rodrigo S. Jamisola Junior, Raymond S. 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 nine-dimensional 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
   
  
• 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: Under the dual action of the excavation disturbance and the stopping advance influence of the working face, the roadway of driving in the direction of the coal face has different degrees of influence of the advance support pressure and the lateral support pressure of the goaf at different intersection positions; the mine pressure appears violently, the roof of the roadway is incomplete, the surrounding rock is broken, and it is difficult to control. 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.
  Keywords: drive in the direction of the coal face; surrounding rock; stability; reinforcement by sections; optical fibre 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 A.K. Tripathi, S.K. 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