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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 Christopher Mark

Six decades ago, the most dangerous job in the USA was mining coal underground. Roof falls were responsible for half of the deaths, killing about 100 miners every year. Fast forward to 2016 and zero roof fall fatalities. Just three miners were killed by roof falls during the following 6 years. How did the mining community achieve this historic goal? This paper starts by analyzing the roof fall fatalities in 1968, categorizing them by their fundamental cause. Then, it shows how each type of roof fall was reduced over time, using snapshots of the fatalities occurring in subsequent decades. Along the way, it evaluates the influence of the regulatory environment, changing mining methods, and better ground control technology. The study found that in 1968 more than half of roof fall fatalities at large mines were attributable to an inadequate safety culture. The immediate effect of the 1969 Coal Mine Health and Safety Act was to reduce the riskiest activities, like needlessly going under unsupported roof. Other hazards, like large roof falls, required technological developments before they were brought under control. Roof Control Plans, which the US Bureau of Mines had been advocating since the 1920s, played a significant role throughout the process.

Mar 12, 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 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 Iga Trisnawati, Fitria Puspita, Himawan Tri Bayu Murti Petrus, Gyan Prameswara, Idi Amin, Andi Nurfaadhilah Ulfah

Laterite ore is a valuable resource due to its high content of metals essential for modern technology, including nickel, iron, and cobalt. However, extracting nickel from laterite ores is challenging due to their complex mineral composition and low nickel concentrations. This study examined the leaching behavior of pre-roasted laterite ore and the effect of roasting on leaching kinetics. The ore was roasted at temperatures of 280°C and 610°C for 3 h, followed by sulfuric acid atmospheric leaching. The results demonstrated that pre-roasting at 610°C significantly enhanced nickel leaching recovery, with up to 93% nickel successfully recovered. The leaching process of pre-roasted laterite ore was found to be controlled by diffusion through the ash layer, with activation energy values of 20.97 and 18.97 kJ/mol for laterite roasted at 280°C and 610°C, respectively. These findings indicate that pre-roasting can improve the leaching performance of laterite ore, potentially leading to more efficient and sustainable nickel extraction processes.

Feb 27, 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 Fouad Faraj

For every blast pattern, zones of unique material types need to be classified before mining. Most material types can be summarized into different kinds of ore and waste. Blocks generated from blasthole assays are too small to be selectively mined so areas with a single material classification meeting the mine’s selective mining unit must be defined. Current industry standard practices are to manually draw ore/waste dig limits but this process is subjective and results in quantities of dilution and ore loss which can be improved upon to enhance the profitability of mining operations. Two weeks production data from a homogeneous Cu porphyry and heterogeneous Manto-type Cu deposit are used to assess the variability in the hand-drawn dig limits for 20 geologists, mining engineers, or ore control specialists and are compared with the optimal ore/waste delineation. The profit for 20 distinct hand-drawn dig limits for the homogeneous mine ranged by 3.7% from $10.5 million to $10.9 million averaging at $10.8 million. The percent range nearly doubled for the more heterogeneous mine at 5.9% from $17.9 million to $19.0 million averaging at $18.6 million. Using the optimal ore waste delineation, the profit increased to $11.0 million (1.9% increase) and $19.3 million (7.0% increase) for the homogeneous and heterogeneous deposits respectively. The natural variability, diggability requirement, and selectivity are identified as the main drivers of ore loss and dilution. Recommendations are provided for reducing the subjectivity in manual hand-drawn dig limits by integrating dig limit optimization algorithms as a tool to assist practitioners.

Feb 23, 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 Yisa Liu, Lili Xu, Shina Li, Kang Li, Jiong Dong, Zichen Yin, Ruixin Ma

With the evolving landscape of gold mineral resource development and utilization, the easily treatable gold resources are continuously decreasing, while refractory gold minerals are becoming the most important resources in the field of gold extraction. In these original ores, most of the gold is encapsulated in the lattice of pyrite and arsenopyrite, resulting in difficulty of gold transfer into the solution during the leaching process. The most effective method to solve the problem of insufficient contact between gold particles and leaching reagent is oxidation pretreatment before leaching. Different from traditional high-temperature roasting, concentrated sulfuric acid was used as both an oxidant and leaching agent in a sulfideencapsulated refractory gold ore to decompose the pyrite in the curing environment to expose the gold and improve the leaching efficiency. The experimental results showed that under the conditions of liquid solid ratio 3:1, curing temperature 240 °C and the reaction time 1.5 h, the weight loss rate and iron removal rate of the ore sample reached the highest value of 60.01% and 99.68%, respectively. The main phase of curing residue was Fe2( SO4)3, and silica was nearly the only phase after washing. At the same time, by combining the sodium thiosulfate system gold leaching method, the gold leaching rate increased from 3.67% to 81.30% before and after pretreatment with concentrated sulfuric acid.

Feb 19, 2024

By Richard A. Thomas, Liming Yuan, Wei Tang, JOHN SOLES

Lithium-ion battery applications are increasing for battery-powered vehicles because of their high energy density and expected long cycle life. With the development of battery-powered vehicles, fire and explosion hazards associated with lithium-ion batteries are a safety issue that needs to be addressed. Lithium-ion batteries can go through a thermal runaway under different abuse conditions including thermal abuse, mechanical abuse, and electrical abuse, leading to a fire or explosion. The NIOSH Mining program is conducting research to prevent and respond to lithium-ion battery fires for battery electric vehicles in the mining industry. In this study, experiments were conducted to investigate the effectiveness of different suppression systems including dry chemical, class D powder, and water mist for lithium iron phosphate battery pack fires. The effects of activation time and release time of the water mist system on the suppression of lithium-ion battery fires were studied. The results of this study may be helpful for developing strategic firefighting and response plans for battery-powered vehicles used in mining.

Feb 17, 2024

By Zhongwei Wang, Dong Wang, Feng Ju

The coal seam in the southern coal field of the Ordos Basin serves as the main aquifer, while the region is known for its high-intensity coal mining activities. The coupling effect of mine water and the strong disturbance caused by high-intensity mining have brought serious challenges to the stability of coal pillars. To address this, a formula for determining the reasonable width of coal pillars under the aforementioned coupling effect and establish a criterion for coal pillar instability in this paper. A case study was on the 11,215 working face at the Xiaojihan coal mine in the area to investigate coal pillar stability. Results indicate that the strength and stability of the coal pillar are affected by the water content, with a small water content increasing its strength and large water content weakening it. In high-intensity exploration of watery coal seams, the best stability of the coal pillar is achieved at optimal water content and mining rates. For the Xiaojihan coal mine, the optimum water content and mining rate are 3.31% and 20 m/d, respectively, ensuring the efficient mining of watery coal seams. Furthermore, this study's findings can provide valuable references for the mining design of coal mines with similar geological conditions in the region.

Feb 13, 2024

By S. W. Robertson, M. T. Golela, S. Mxinwa

South Africa has one of the largest Cr-related industries in the world and generates large amounts of Cr-containing smelter dusts. It is normal practice to scrub Cr from the furnace off-gas in water, and to recover the Cr by adsorption, followed by recycling of the loaded adsorbent into the furnace. A study was conducted to find a low-cost adsorbent that is capable of removing sufficient Cr from the solutions generated by scrubbing. The test work focused on the evaluation of different clay minerals (attapulgite, bentonite, and kaolinite) as adsorbents for Cr. Cr(VI) could only be adsorbed after prereduction with e.g. ascorbic acid (ASA). An 80% removal of Cr(VI) could be achieved from a solution containing 20 mg/L Cr using bentonite clay after reduction with ASA. Attapulgite and kaolinite adsorbed less than 55% Cr after reduction with ASA. Both ASA and ferrous salts were found to be suitable reducing agents. The use of acid mine drainage (AMD) was also investigated as a low-cost alternative reducing agent, as AMD usually contains iron. The Cr(VI) reduction potential of the AMD was determined by redox titration against a solution containing 50 mg/L Cr(VI). Studies show that AMD can potentially be used as a reducing agent in a Cr removal process if it is available on site.

Feb 12, 2024

By N. V. Moraka

Calls for transformation in the mining sector are legislated. Whilst the composition of boards in terms of gender diversity and equal representation has improved, little is known on how women directors experience boardrooms. This research investigated the experiences of South African women in male-dominated mining boards. It questions why women remain marginalised in boardrooms, even when they are qualified as the men in their respective fields. The experiences of South African women on male-dominated mining boards were investigated by means of a multiple case study using a qualitative research methodology. Interviews were conducted with 16 women and 12 men serving across six JSE-listed South African mining companies. The findings show that irrespective of their qualification and experience, women,s, contributions are often ignored, and they are assigned to more social roles. Regardless of race, all women experienced gender stereotyping. Strategies are required to ensure that board cultures embrace female talent, where women can effectively contribute to decision-making and maximize their talent. It is argued that gender and identity should be studied in relation to post-colonial history in African countries. Women have multiple, varying identities, and their experiences require a unique investigation that would enable strategies to overcome barriers, through the implementation of an appropriate diversity management programme.

Feb 12, 2024

By B. P. von der Heyden, T. Blenkinsop, A. Mamuse

Zimbabwe hosts a varied array of coloured gemstones. With the exception of emerald deposits and several world-class pegmatites, few of the gemstone occurrences have received detailed attention from the scientific or mineral exploration communities. In the present contribution we summarize the status of knowledge of the gemstone deposits and occurrences in Zimbabwe, paying particular attention to the geological settings in which they were formed. Synthesis of this regional geological approach reveals that there may be significant exploration potential for further gemstone occurrences, particularly in the extensive pegmatite fields and in Al-enriched orogenic belts that have undergone greenschist to granulite facies metamorphism. Further socio-economic and developmental initiatives will aid in optimizing the value generation from this important sub-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 Manoj Khandelwal, Bright Akuinor Tsidi, Newton Amegbey, Daniel Mireku‑Gyimah

One of the ambient effects of production blasting is flyrock. To effectively manage flyrock throw distance in mining, there is the necessity to successfully envisage blasting output without sacrificing the hazardous impact of flyrock which may result in fatality and operational shutdown. For flyrock throw distance prediction, velocity of detonation (VOD) and charge per bank cubic meter (CPBCM) are not usually included. This paper focuses on the use of support vector machine (SVM) regression to ascertain the impact of VOD and CPBCM on flyrock throw predictions. The machine learning models were linear support vector machine (LSVM), quadratic Gaussian support vector machine (QGSVM), fine Gaussian support vector machine (FGSVM), medium Gaussian support vector machine (MGSVM), and cubic Gaussian support vector machine (CGSVM). The outcome indicates that FGSVM was the most sensitive with a 4% improvement when VOD and CPBCM were included. As a result, the LSVM model provides a suitable AI competitive alternative tool for flyrock throw prediction in mining operations by incorporating VOD and CPBCM.

Feb 10, 2024

By Kwadwo Osseo‑Asare, Isehaq Al‑Nafai

The hydrometallurgical processing of bastnasite is a well-known process that is used to extract rare earth metals. However, more accurate measures are required to enhance the efficiency of extracting and recycling rare earths. In this work, Pourbaix (Eh–pH) diagrams for La-, Nd-, and Pr-(F)-(CO3)-(SO4)-(Cl)-(NO3)-(C2O4)-H2O systems were theoretically derived to investigate the stability relations during the hydrometallurgical processing of bastnasite. The diagrams for RE-H2O systems show similar trends to the original ones drown by Pourbaix. The stability domains of RE carbonates were presented by RE-CO3-H2O systems with pH range of 4.5–11. In contrast, the diagrams for RE fluorides showed a large stability region of insoluble REF3 (pH ~ − 1.7–12). The decomposition behaviors of REFCO3 were studied by constructing Eh–pH diagrams for RE-F-CO3-H2O system. The stability regions of bastnasite species extend from nearly neutral to basic media (pH ~ 6.5–12). The acid treatment (using H2SO4, HCl, and HNO3) of bastnasite was investigated by constructing Eh–pH diagrams for REF- CO3-(SO4)-(Cl)-(NO3)-H2O systems. According to these systems, REFCO3 can be decomposed at pH ~ 6.5 when treated with H2SO4 and at pH ~ 1.7 when treated with HCl and HNO3 acids. On the other hand, the alkaline treatment of REFCO3 converts it to RE(OH)3 at pH ~ 11. The recovery of rare earths ions was studied with the aid of RE-C2O4-H2O systems which shows a wide stability region for RE oxalates in pH range of − 2–11. This theoretical approach explained the aqueous relations in the hydrometallurgical processing of bastnasite ore and it can improve the practical hydrometallurgical processing of such ores.

Feb 9, 2024