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By Christos Roumpos, Philip‑Mark Spanidis, Francis Pavloudakis

Coal mining operations are large-scale, long-term frameworks of high complexity. Mining organizations accumulate, develop, and leverage knowledge from diverse scientific and technological fields as a result of these operations. Both practical experience and existing literature indicate that knowledge management (KM) methods within the mining industry primarily target solving immediate technical challenges and operational requirements. However, they are not commonly regarded as strategic tools to enhance the performance and competitiveness of mining companies. Empirical evidence from the Greek mining industry suggests that the management of available knowledge is intricate, less effective, and dysfunctional. This paper presents a methodology based on the principles of quantitative research (QNR), collaboratively conducted with a group of mining experts, to assess the KM landscape in the Greek mining industry. The methodology involves a structured data collection framework using questionnaires, subsequent statistical analysis, a discussion on critical methodological aspects, and an interpretation of results focusing on key aspects useful for planners and designers of KM systems in the mining industry. Finally, the paper concludes by summarizing the methodology’s outcomes and proposing further perspectives for research.

Mar 16, 2024

By R. J. Quevedo, Y. A. Sari, S. D. McKinnon

The large scale of cave mines leads to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method, the effects of which are difficult to account for during the design stages. In this paper we present a case study of the application of a machine learning approach to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile. Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analysed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms, nor the calibration of associated controlling parameters. The proposed random forest model predicted pillar collapses with 80% accuracy despite limited samples to model from. The main contributing factors to collapses were found to be related to available pillar volume, cave front geometry, and time under abutment stress conditions. The effects and interactions of such factors were also studied, showing that careful and improved control over operational conditions can significantly reduce the likelihood of pillar collapses. These conclusions could not have been obtained from stress analysis alone, illustrating the complementary nature of conventional stress analysis and machine learning approaches.

Mar 14, 2024

By J. P. Gouvea

Back-analysis of two mining-induced tremors in 2011 was used to determine a modelling criterion for the entire Klerksdorp Goldfield (i.e., Vaal Reefs region). At that time, most mining operations in the region were owned by AngloGold Ashanti (AGA) and shared many geotechnical similarities. By 2018 AGA had sold off all its Klerksdorp operations to different mining companies, which continued applying the 2011 modelling criterion. As mining activities became more remote, scattered, and isolated, the extraction of isolated blocks of ground and remnants became increasingly necessary. However, the inherited criterion allowed only minimal or no mining activities at these areas. Keeping in mind that safety is the overriding consideration, an innovative numerical modelling approach, which includes the application of peak particle velocity designs, was developed to provide practitioners in the Klerksdorp Goldfield with a suitable criterion to justifiably and safely mine isolated blocks of ground and remnants. This would not always be possible if a conformist approach was used.

Mar 14, 2024

By S. Swanepoel, A. V. Cherkaev, Q. G. Reynolds, M. Erwee

Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R2 = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag chemistry. This analysis lays the foundation for implementing Si content control on a ferrochrome smelter.

Mar 14, 2024

By Yao Yevenyo Ziggah, Victor Amoako Temeng, Solomon Evans Kweku Koomson

Tyre hours/life deficit is a major operational challenge facing the mining industry which adversely affects materials production and costs. An accurate forecast of the tyre life is key in addressing this menace. This study for the first time employed the hybrid intelligent technique by utilising three metaheuristic optimisation algorithms, including particle swarm optimisation (PSO), genetic algorithm (GA), and whale optimisation algorithm (WOA), as trainers for the parametric weights and biases to optimise multilayer perceptron neural network (MLPNN) for enhancing prediction of on-site dump truck tyre life in the mine. Four hybrid models known as PSO-MLPNN, WOA-MLPNN, GA-MLPNN, and BP-MLPNN were developed using a total of 157 tyre dataset records obtained from a surface mine in Ghana. In assessing the prediction performances for the models developed, five statistical performance metrics of variance accounted for (VAF), Nash–Sutcliffe efficiency index (NASH), coefficient of determination (r2), mean absolute percentage error (MAPE), and correlation coefficient (r) were utilised. Moreover, ranking, uncertainty analysis and Bayesian information criterion (BIC) techniques were utilised to establish the most effective hybrid model, whereas sensitivity analysis was conducted on the input parameters. Results achieved showed that PSO-MLPNN was the best for prediction because it had the least MAPE value of 1.196% and relatively high values of VAF (99.642%), NASH (0.996), r2 (0.996), and r (0.998). Besides, PSO-MLPNN had the best selection criteria values of 6, 7.1725, and 444.834 for the ranking, uncertainty analysis and BIC respectively. Hence, PSO-MLPNN is recommended for the prediction of on-site dump truck tyre life for the studied mine.

Mar 11, 2024

By Xin Wang, He Shao, Wenpu Li, Yiyi Zheng, Xi Chen, Yurong Gao

This study utilized experimental and numerical simulation methods to investigate and analyze the atomization characteristics of three internal air–water mixed atomizers under different operating conditions. By comparing the research results, the effective dust control working parameters for the application of the selected atomizer in the engineering field were determined. Firstly, the atomization characteristics of the three atomizers were studied using a customized experimental system. The results indicated that nozzle 2 had a wide atomization range, a small atomization distance, and small droplet size; nozzle 3 had a narrow atomization range, large droplet size, and fast atomization speed; and nozzle 1 not only had a large atomization range but also could produce droplets of dry mist size with fast atomization speed, meeting the dust reduction requirements of the Majiliang Coal Preparation Plant. Secondly, Fluent software was used to simulate the atomization process and particle size distribution of the three internal mixed atomizers, determining the working conditions of the atomizers, the required number of atomizers, and the application angle of the atomizers in the internal mixed dust removal system. Finally, the atomized dust control system was applied on-site, achieving a total dust removal efficiency of 90.72% and a respirable dust removal efficiency of 89.75%.

Mar 9, 2024

By Lingyun Zou, Xinghai Lei, Tiezheng Li, Yifei Song, Nan Wang, Yang Li

The stability of key strata is of utmost importance in ensuring safe and effective coal seam mining operations. To minimize disturbance impacts on these strata and ensure their stability, it is crucial to assess the degree of disturbance caused by mining activities. This study proposes the evaluation index “disturbance degree of key stratum (KSDD)” and establishes a quantitative KSDD criterion to measure the degree of disturbance on key strata. To facilitate the assessment process, an intelligent calculation system for KSDD is developed. The classification criteria are derived by combining the measured three-zone heights from multiple mines. The reliability and accuracy of the KSDD quantitative criterion are validated using field measurement data. Furthermore, the KSDD quantitative criterion is applied to the Meihuajing mining area No. 23, and the distribution characteristics of KSDD are analyzed. The findings of this study can serve as a valuable reference for reducing disturbance and enhancing the stability of key strata during subsequent mining operations. Overall, this work contributes to the field of ground control in coal seam mining by providing a comprehensive approach to assess and mitigate the disturbance caused by mining activities on key strata.

Mar 8, 2024

By Kasimcan Koruk, Julian M. Ortiz

A geological model accounting for uncertainties possesses important advantages for resource estimation. Machine learning algorithms (MLAs) employed on multivariate geochemical datasets open up ways to new methodologies for such geological models with ease in comparison to traditional geostatistical methods. This article proposes a two-step MLA with an ensemble implementation to define geological domains and their uncertainties based on geochemical data. The proposed workflow is applied hierarchically on a dataset from a porphyry copper deposit to perform binary classification that can be attributed to alteration domains.

Mar 1, 2024

By Tuanhui Wang, Qiwei Wang, Quanrui Liu, Jianhui Xu, Chao Wang, Yu Liu, Yuefeng Li

Rockburst is a highly destructive geological hazard that can cause casualties and equipment damage. To achieve highaccuracy discrimination of rockburst intensity, this article proposes an improved model that addresses the inefficient maximum membership principle used in traditional rockburst fuzzy evaluation models. The stress coefficient σθ/σc, brittleness coefficient σc/σt, and elastic deformation energy index Wet are selected as evaluation indicators for rockburst classification. Subjective and objective weights are obtained using the Delphi method and entropy weight method (EWM). Three types of membership function distribution forms are then used to obtain the membership degrees of each indicator to rockburst grades: trapezoidal membership function (TMF), normal membership function (NMF), and quadratic parabolic membership function (QPMF). Finally, six traditional models and six improved models are established using the maximum membership principle (MMP) and weighted average-maximum membership principle combination evaluation principle (WMP), respectively. Based on the analysis of 100 sets of rockburst field data, the accuracy, precision, recall, and F1-score of the improved evaluation model are increased by 11.3%, 0.097, 0.068, and 0.089, respectively, compared to the traditional model. The Delphi-NMF-WMP model is selected as the best model, with four performance indices reaching 97.0%, 0.979, 0.979, and 0.978. The best model is applied to evaluate the rockburst intensity of the Cangling Tunnel, Dongguashan Copper Mine, and Jiangbian Hydropower Station Diversion Tunnel, with evaluation results consistent with the actual situation, demonstrating the reliability and scientificity of the model.

Feb 28, 2024

By Ebrahim Ghasemi, Mohammad Hossein Kadkhodaei, Mosareza Jalilian

Entry-type mining methods, such as cut and fill method, play a significant role in underground mining, and they are still suitable methods for extracting deposits with irregular shapes and with weak host rocks. In these mining methods, the stability of the open spans is important for improving worker safety and increasing ore recovery. This study focuses on the application of six data mining classification methods, namely multinomial logistic regression (MLR), classification and regression trees (CART), logistic model tree (LMT), decision tree (J48), multivariate adaptive regression spline (MARS), and gene expression programming (GEP), in order to develop predictive models for stability assessment of underground entry-type excavations. For this purpose, a database containing 399 case histories of entry-type excavations from seven Canada underground mines was used. These models predict the stability status of entry-type excavations based on two input parameters, dimensions of the span and rock mass rating (RMR). The performance of developed models was evaluated using six performance metrics namely accuracy (ACC), sensitivity (SE), precision (PR), Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic (ROC) curve (AUC), and performance index (PI). The results showed that all developed models can be used to predict the excavation stability, but the comparison of models indicated that the J48 model provides the highest performance with ACC = 0.900, SE = 0.869, PR = 0.922, MCC = 0.835, AUC = 0.891, and PI = 4.417. Thus, the proposed J48 model can be applied to identify the susceptibility of span instability in the entry-type mining, and if instability has been predicted, the necessary measures, including the use of suitable support systems, can be considered to reduce the casualties resulting from span instability.

Feb 26, 2024

By O. K. Olawale, G. O. Edaogbogun, B. N. Ben‑Festus, F. Ben, F. R. Amodu

The environmental and health implications of artisanal gold mining activities at mining sites across Nigeria have rekindled research interest. This study aimed to assess the scalability of naturally occurring radioactive materials in gold mining sites in Iperindo, Nigeria. Soil samples were collected from three different mining sites and control locations and analyzed for natural radionuclides, mainly 238U, 232Th, and 40K, using gamma spectrometry having a sodium iodide detector. The average activity concentration values of 61.55 ± 3.71, 72.65 ± 4.45, and 1134.99 ± 38.12 Bqkg− 1 obtained from within the mining sites for 238U, 232Th, and 40K, respectively, were above the world permissible values of 33.0 Bqkg− 1 for 238U, 45.0 Bqkg− 1 for 232Th, and 420.0 Bqkg− 1 for 40K. The average activity concentration values of 15.26 ± 3.19, 21.46 ± 4.27, 381.04 ± 23.36 Bqkg− 1 estimated for 238U, 232Th, and 40K, respectively, for the control location were, however, lower than the global permissible values. The study also evaluated other radiological parameters such as radium equivalent, dose rate, annual effective dose rate, internal and external hazard indices, and alpha and gamma indices. The obtained values were 252.83 Bqkg− 1, 119.98 nGyh− 1, 147.15 mSvy− 1, 0.85 Bqkg− 1, 0.68 Bqkg− 1, 0.31 Bqkg− 1, and 1.89 Bqkg− 1 respectively. Except for radium equivalent, external and internal hazard risks, and alpha index, which were significantly lower than the recommended threshold, all other radiological parameters were higher than the recommended global limits. Moderate data reproducibility and a p-value of 0.000574 further reinforced the robustness and reliability of the results obtained in this study.

Feb 22, 2024

By Edward Holloway

The basis for the selection of the ultimate pit for an open pit mining operation is generally opaque. Some form of value-based analysis will generally guide the final decision; however, the value will often gradually plateau whilst the ultimate pit size continues to increase. There is no established framework to guide specialists in the selection of the ultimate pit, with such decisions regularly made on a somewhat arbitrary basis. The key component that is missing from the analyses typically used to select the size of the ultimate pit is risk. Once risk is quantified within the analysis framework, the decision-making range is significantly reduced, and the process for selecting the ultimate pit becomes more consistent, replicable, and defensible. This paper provides an approach to including risk in the process used to select the ultimate pit.

Feb 21, 2024

By Zhibiao Guo, Jinglin You, Dongshan Yang, Yuanxin Zhao, Yuhui Li, Jingwei Gao

By utilizing collapsed gangue to create gob ribs, roof cutting and roadway maintenance (RCRM) technology enables coal pillar-free mining, presenting a novel approach. Due to their inherent structure, faults, which are common geological formations, can lead to various hazards, including fault sliding and significant deformation of roadways. In this study, a selfdesigned underground engineering simulation test device is utilized to imitate the cross-fault mining of the 11,101 working face in the Qipanjing Coal Mine (East). The objective is to examine the behavior of the surrounding rock and stress evolution. Generally, the experimental findings demonstrate that compared to the return air roadway, the RCRM roadway experiences a 3.3% decrease in maximum coal rib stress. This suggests that the implementation of this technique can effectively alleviate the stress on the surrounding rock of the roadway. The maximum coal rib stress, lateral pressure of the gob rib, and maximum deformation of the roof near the fault of the RCRM roadway are 11.3%, 25.6%, and 86.1% greater than those of the conventional geological conditions, respectively. The roadway deformation is negatively impacted by the fault structure. Based on experimental data and field measurements, a discrepancy in the maximum roof deformation between the test and field monitoring results under typical geological conditions is 8%. This indicates a favorable alignment between the test results and the real-life scenario. In conclusion, the proposed targeted control technique is implemented in the field by integrating the mining invariant equation and gangue volume expansion equation. This not only validates the effectiveness of the technology but also offers valuable insights for the future utilization of RCRM technology under comparable circumstances.

Feb 20, 2024

By Bernd Friedrich, Elif Emil‑Kaya, Merve Papakci

In the production of SmCo permanent magnets with excellent temperature stability, corrosion resistance, and oxidation resistance, samarium (Sm), one of the rare earth elements (REEs), and cobalt (Co) are employed. Cobalt (Co) is a crucial component in tool materials, nickel-based alloys, tablet and smartphone batteries, and electric car batteries. REEs and Co have been listed as critical raw materials by the European Union Commission for many years. Due to the ever-growing demand for Co and REEs in technological applications, the recovery of these elements from secondary sources has garnered significant interest. There are two types of SmCo magnets, one of which contains a high amount of iron, approximately 15.2%. This paper focuses on the recycling of Fe-bearing SmCo. In this study, an oxidative leaching process with nitric acid was developed to eliminate iron through in situ hydrolysis and to dissolve REEs and Co. The influence of experimental conditions on the formation of an amorphous iron compound through the hydrolysis of Fe3+ in a nitric acid environment was thoroughly examined based on a Taguchi orthogonal array. The optimal parameters for oxidative leaching were determined to be an acid concentration of 3 mol/L, a solid-to-liquid ratio of 1/10, and a process temperature of 60 °C.

Feb 15, 2024

By J. P. Gouvea, T. R. Stacey

A research project was carried out on a gold mine in the Vaal River region of South Africa (Witwatersrand Basin) to evaluate the effect of two particular aspects of rockbolt installation – the angle of inclination of the bolt relative to the orientation of the rock surface, and the protruding length of the bolt – on the mine’s gully support system. A keyblock analysis approach indicated that these factors do not have an adverse impact on the stability of the strike gullies. This was confirmed by extensive empirical data collected from the case study mine. While safety is the primary concern, cost saving opportunities can be realized through reconsidering historically inherited support standards that are perceived to be correct and appropriate. The research described in this paper sets forth a practical approach, that is easy to implement and repeatable, to evaluate the role of rockbolts in stoping gullies.

Feb 12, 2024

By M. C. Bekker, T. Govindasamy

The potential benefits of implementing the Last Planner System (LPS) on mining infrastructure projects were explored through two case studies in the South African mining industry. The first part of the study involved measurement of the impact of LPS through earned value techniques on secondary project data. The second part sought to establish the lean construction success factors leading to improvements in construction performance. This was done by means of a research questionnaire distributed to the project owner’s teams, contracted parties, and the LPS facilitators. The results revealed a positive correlation between LPS application and planned percentage complete. However, the performance achieved during the LPS pilot was not sustained. The success factors and benefits considered most evident in the case study pilot differed among the three stakeholder groups. The findings are expected to guide construction stakeholders to better define performance measures and focus on factors required to make LPS implementation more effective in the South African mining sector.

Feb 12, 2024

By Wei Victor Liu, Omer Faruk Ugurlu, Chengkai Fan, Bei Jiang

The accurate prediction of truck productivity plays a pivotal role in improving the efficiency and profitability of open-pit mining operations. However, predicting truck productivity is challenging owing to the complex nature of the working conditions of the mine site. This paper proposes a deep neural network model to overcome the challenge of predicting truck productivity in open-pit mines. The prediction model was built using eight variables and was optimized by considering different train-test split ratios, numbers of hidden layers and neurons, and activation functions. The proposed model's performance was evaluated using various metrics and was compared with other commonly used machine learning algorithms. According to the results, the proposed model outperformed traditional machine learning algorithms by achieving higher accuracy and lower error rates, with the best-performing model having four hidden layers with 70 neurons per layer and a scaled exponential linear unit activation function, resulting in a coefficient of determination value of 0.89. This demonstrates the potential of deep neural network models for predicting truck productivity in open-pit mine sites. Moreover, a single variable sensitivity analysis was conducted to investigate the impact of input variables on truck productivity. The results show that haul distance is the most influential variable for the prediction of truck productivity.

Feb 12, 2024

By Ewan Wingate, Ranjit Prasad, Yanjun Liu

The phosphorus journey begins with the extraction of primary phosphorus in the form of phosphate in apatite through openpit mining and, in some rare cases, underground mining methods. The ores are processed in beneficiation plants to remove impurities before the phosphate rock concentrate is either sold on the open market or converted into phosphoric acid in downstream chemical production facilities by the wet acid process or thermal phosphorus routes. These can in turn be converted into fertilizers, as well as used in the food and electronic industries. The advent of the EV battery has given rise to a new use for purified phosphoric acid in the production of lithium ferro phosphorus (LFP) cathode material for EV battery cathode assemblies. The production of LFP relies on raw materials such as lithium carbonate, lithium hydroxides, iron salts and purified phosphoric acid.

Feb 1, 2024

By Hakan Uyguçgil, Beril Bayraktar, Adnan Konuk

In mining, which is one of the industries with the highest risk of occupational accidents, a primary goal is to enhance safety through the thorough investigation of accidents using reliable methodologies. In this research, the data from 13,653 accidents reported in the Karadon Hard Coal Enterprise between 2000 and 2011 were analyzed and compared using the life-table and Kaplan-Meier methods. Accident records were classified and analyzed into four categories: environment-related, equipment-related, person-related and other accidents. Findings in terms of survival time indicate that environmentrelated and person-related accidents had the highest risk in Karadon, with equipment-related accidents having the lowest. This study established the reliability of using survival analysis methods to investigate occupational accidents in the mining sector. Given that environment-related and person-related accidents occur in a relatively short period, such as one year after starting work, it is recommended that research be done to improve workplace and occupational safety measures and broaden the training given to miners. Additionally, it is determined that increasing the use of automation and mechanization in underground coal mines, combined with developing a thorough equipment safety training program to raise awareness of the danger, can help prevent person-related occupational accidents caused by the interaction between equipment.

Feb 1, 2024

By Jenna Wundrack, Evan Thibaud

The advanced cryogenic treatment is a post-manufacturing metallurgical process that involves subjecting off-theshelf materials to extremely low temperatures, significantly improving wear resistance and reliability. These case studies explore whether the claims of Enrich Bits & Tools benefit metals and their specific in-field application to drill bits in the blasting industry. This paper reviews the application of the treatment to button drill bits for enhanced wear resistance in two operations. The first is a civil construction project with highly abrasive rock. To confirm the remarkable benefits of the treatment, a second case study was performed. This case study utilized measure-while-drilling technology in consistent rock characteristics to reduce variables in the field study.

Feb 1, 2024