Search Documents

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

Accurate prediction of truck productivity plays a pivotal role in improving the efficiency and profitability of openpit mining operations. This paper proposes a deep neural network (DNN) model to overcome the challenge of predicting truck productivity in openpit 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 compared with other commonly used machine learning algorithms. The results showed that the proposed model outperformed traditional machine learning models by achieving higher prediction accuracy. Moreover, a single-variable sensitivity analysis showed that haul distance is the most influential variable for predicting truck productivity. This study marks a pioneering effort in employing DNN to predict truck productivity in openpit mining, signifying a notable advancement in the field.

Jul 1, 2024

By G. Akdogan, S. M. Bradshaw, A. P. Van Wyk, T. Punt

This study investigated the performance of citric acid as lixiviant for cathode material from end-of-life lithium-ion batteries (LIBs). Black mass containing 84.2 weight percent MNC (LiNi0.45Mn0.4Co0.15O2) and 15.8 weight percent LCO (LiCoO2) material was leached at solid-to-liquid ratios of 20, 50 and 100 g/L. Leaching with 1.5 M citric acid, 2 volume percent H2O2 and a solid-to-liquid ratio of 20 g/L at 95 °C extracted 90 percent Co, 95 percent Li, 94 percent Mn and 94 percent Ni within 20 min. At the highest solid-to-liquid ratio of 100 g/L with 1.5 M citric acid, 10 volume percent H2O2 and 95 °C, 84 percent Al, 84 percent Co, 87 percent Li, 86 percent Mn and 96 percent Ni were leached after 40 min, producing a leach solution containing 0.68 g/L Al, 20.0 g/L Co, 6.0 g/L Li, 13.6 g/L Mn and 19.3 g/L Ni. Using stepwise addition of H2O2, instead of initial bulk addition, did not significantly improve the leaching efficiency but did reduce the required leaching time. Temperature control of the reactor was also more manageable with stepwise addition of H2O2, and evolution of gas was less vigorous. It was observed that the leaching efficiencies of Co, Li and Mn decreased slightly at solid-to-liquid ratios of 50 and 100 g/L, while the leaching of Ni increased slightly. The solution leached at 20 g/L could be stored for six weeks without any spontaneous precipitation. However, solutions leached at 50 and 100 g/L showed changes in concentration after one month of storage, suggesting that processes that have a large material throughput and use high solid-to-liquid ratios around 100 g/L must ensure that the solutions are not stored for extended periods of time.

Apr 1, 2025

By C. Allen, H. Zhang, L. Falk

Shock loss occurs when there is a change in the area of a drift or airflow direction. These shock losses accumulate throughout the ventilation system and will increase the mine resistance as underground development deepens or becomes more complex. Hence, shock losses should be carefully considered when establishing the resistance through a series of branches as they can be a significant contributor to the estimated mine resistance. Using computer software to simulate a ventilation system is common but applying reasonable shock loss factors in the software is not a simple or direct process. Determining shock loss values includes the consideration of historical measurements, application of factors and formulas from literature based on the geometry and complexity of junctions, and the geometry variation at different locations. For example, the shock loss at the bottom of a ventilation raise can consist of a 90° bend and a sudden area change. This paper presents an approach for estimating shock loss factors near raise junctions using computational fluid dynamics (CFD). Current and future work will also be compared with field measurements. Results from this approach are compared with software defaults as well as classical techniques in published literature and standards.

Sep 1, 2024

By Jodie Robertson, Isabel Barton, Cristian Caro

Prior to mining, all rocks contained in a mineral deposit must be placed in one of several categories known as ore types. Each ore type describes a different way that the rock will behave in processing. The ore type determines which process stream a particular mined unit will be sent to, sets expectations for process engineers and metallurgists, and forms the basis for recovery predictions and production estimates. At first, ore types are assigned mainly by geologists based on visible geological criteria (dominant ore mineralogy, estimated grade, rock type, alteration type). As metallurgical tests are carried out and process data accumulate, the resulting criteria may be factored into the ore typing process, typically as assays and recoverable metal using the relevant process(es). Other variables may also be incorporated into ore typing at operations if they are particularly important, such as grindability, preg-robbing, gangue reagent consumption, and the presence of especially deleterious minerals such as talc or smectite. The mine operation department may also redefine ore types and/or process destinations based on economic criteria. A “best” ore type is designated that (in theory) best describes how the rock will behave in processing. Codes representing this ore type are entered into the drill-hole database, then interpreted on cross sections and level plans, and finally added to the block model. Ore types are iteratively reexamined and adjusted over the life of a project. Major difficulties in effective ore typing are (1) lack of communication between geologists and metallurgists, (2) the imprecision inherent in imposing clean categories on messy natural systems without discrete divisions, (3) the need for ore types to provide useful information for a wide range of processes, (4) difficulties in scaling up laboratory test results to mine production, and (5) conflict among geological, metallurgical, and economic criteria for ore typing.

Jul 1, 2024

By Carter Brown, Kate Brown Requist, Eric Lutz, Moe Momayez, Athira Satheesh

New mining health and safety training modalities are becoming increasingly prevalent in the underground mining industry. Various digital training tools have been developed over the past several years, but the extent of their use remains unclear. Advances in gamified training by way of computerbased video games and virtual reality (VR) have opened a new avenue for mine rescue and self-escape training, but these methods have not been widely adopted across the mining industry. Mine rescue and self-escape situations require a mix of in-classroom training as well as live drills, but it is important to assess the extent of and effectiveness of new training modalities. The Mine Rescue and Self-Escape Survey (MRSES) was distributed to underground mining operations to collect perceptions of current training effectiveness as well as willingness to adopt new training methods as a complement to live rescue drills. Responses from the MRSES indicate that adoption of digital training technologies has been slow-going despite an interest in their use. Further, there appears to be no meaningful connection between more frequent escape and rescue training and feelings of emergency preparedness. With relatively little use of already developed digital training tools but an interest in their adoption, it may be time to reassess current emergency preparedness training philosophies.

May 1, 2025

By Kadri Dagdelen, Lee J. Zamalloa

Truck-shovel dispatch systems based on linear or nonlinear programming algorithms have been in operation since the mid-1980s, with different levels of success depending on the correct arrangement of operational resources and the complexity of the mine. These systems are known for shortcomings related to the logic behind the decision-making of the operational objectives. This makes it necessary to have a human agent (dispatcher) to guide the system when the operational parameters change, creating the possibility of suboptimal decisions due to the human factor. To address this issue, the present work discusses the implementation of a methodology based on artificial intelligence (AI) techniques to assist in the decision-making of the best optimization goals arrangement when operating a fleet management system. The focus of the analysis is on the evaluation of variable goal weights in a nonpreemptive, multigoal optimization model for multiple uncertain scenarios based on regions of optimal feasibility.

Apr 1, 2025

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

Laterite ores, rich in essential metals like nickel, iron and cobalt, are a crucial resource for modern technology. However, the extraction of nickel from laterite ores is a complex process due to their intricate mineral compositions and low nickel concentrations. This paper presents the results of a study to understand the leaching behavior of preroasted laterite ore and the impact of roasting on leaching kinetics. The ore was subjected to roasting at 280 and 610 °C for a duration of three hours, followed by leaching in sulfuric acid (H2SO4) under atmospheric conditions. The results show that preroasting the ore at 610 °C significantly improved the recovery of nickel during leaching, with recovery rate of up to 93 percent, and the leaching process of the preroasted ore was primarily controlled by diffusion through the ash layer. The activation energy values were found to be 20.97 kJ/mol for the ore roasted at 280 °C, and 18.97 kJ/mol for the ore roasted at 610°C. These findings suggest that preroasting can enhance the leaching performance of laterite ore, potentially paving the way for more efficient and sustainable nickel extraction processes.

Aug 1, 2024

By Michael Forbes, Rick Jeuken

The challenge of managing geological uncertainty in mine planning is central to optimizing resource extraction while minimizing risks and costs. This paper introduces a novel approach to coal mine planning by integrating chance-constrained programming to address the uncertainty inherent in mining operations. The main takeaway from this work is the demonstration that focusing on reducing the variance of high-quality coal blocks, rather than low-quality ones, can lead to better blending outcomes, higher profitability and more efficient resource utilization. The study presents a case study of a coal mining operation, using chance-constrained programming to manage the uncertainty surrounding coal property predictions, specifically the predicted coke strength after reaction (CSR).

Jan 1, 2025

By John Thella, Edward McNew, Christina Thella, Mends Emmanuel, Pengbo Chu

Ore sorting is an emerging technology in mineral processing that can produce a significant impact on the total mining and processing economics. The technology is capable of preconcentrating an ore based on differences in the mineralogical characteristics, which can include color, brightness, transparency, size, shape, density or near-infrared response. The current practice of conducting an ore sorting test typically requires large amounts of materials to be sent to a commercial testing facility, which is expensive and time-consuming. Here, we present a novel method to simulate commercial ore sorting on a laboratory scale using a rare earth ore.

Jan 1, 2025

By Jaeheon Lee, Junmo Ahn, Jiajia Wu

To reduce the environmental footprint of hydrometallurgical processing of black mass from spent lithium-ion batteries (LIBs), a green leaching system based on glycine and sodium metabisulfite (Gly-SMS) was proposed. The leaching study demonstrated that the highest cobalt and lithium recoveries of 100 percent and 99.8 percent were achieved under optimal conditions. The leaching mechanism revealed that the dissolution of LiCoO2 in the Gly-SMS solution followed the shrinking core model. The apparent activation energies for cobalt and lithium were determined as 48.05 and 41.51 kJ/mol, respectively, indicating a surface chemical reaction controlling mechanism. The leachate was then processed by an acidification- precipitation technique with oxalic acid as the precipitant to remove cobalt. Glycine complexes with metal ions by zwitterionic ligand and recycles in the leaching-precipitation circuit, reducing the reagent cost. Compared to other studies, this leaching system has near-neutral operating conditions and is cost-effective, making it an economically viable alternative for treating cathode materials from spent LIBs.

Dec 1, 2024

By Arif Emre Dursun

The aim of this study is to present hazard classifications of location, type and causes of accidents in the Turkish coal-extracting industry using the analytic hierarchy process (AHP). In this study, fatal mine accidents were analyzed by pairwise comparison and weighting, and the most important hazards were identified by determining the effective main and sub-criteria among location, type and causes of accidents. For this purpose, first-, second- and third-level hazards consisting of location, type and causes of accident were determined by using the data of fatal mine accidents in underground and openpit coal mining in Turkey between 2010 and 2020.

May 1, 2024

By Isabel Barton

This article describes and analyzes the history of research into the origins of the uranium-vanadium(-radium) deposits of the Paradox Basin. For several decades after their discovery in 1881, research was desultory and almost entirely descriptive, fluctuating with demand for vanadium and radium. Systematic geologic descriptions, accumulated between 1910 and 1950, supported rapid progress during a government-backed surge of research in the 1950s, when uranium was the chief commodity of interest. Most of the major theoretical innovations date from this decade, including the use of concepts from solution chemistry to identify the conditions and constrain the processes of deposit formation and alteration. Research from this time was highly influential in geology at large, providing the model for sandstone-hosted uranium deposits worldwide and numerous new minerals and mineral structures. After about 1960, mine production remained high but research dwindled, and most new advances were to the details of the hydrothermal model of ore formation rather than its core concepts. By the end of the 1960s, the deposits were understood to have formed at the mixing interface of an oxidized, metal-bearing water with a reducing fluid of debated origin. Research was desultory over the ensuing decades; the source of the metals and the nature of the chemicals constituting the trap were not clearly established. They remain questions today, as do factors like the relationship between ore mineralization and the geologic history of the basin. (More in the full paper.)

Jan 1, 2025

By Firat Atalay

Estimating the spatial distribution of ore grade is one of the most critical steps to proceed with an investment decision on a deposit. Kriging is the most widely used method to estimate the ore grade, but alternative techniques are being developed. Machine learning algorithms can be used as alternative methods to classical kriging. In this paper, the iron (Fe) grade of a deposit is estimated with the XGBoost algorithm, and the results are compared with kriging estimation results. For the estimations, samples collected from drillholes were used. Due to the different natures of the estimation methods, different steps were taken to perform the estimations. The results show that XGBoost produced higher-range estimates, which is a desired result in ore grade estimation, and the minimum and maximum of the estimates were lower and higher than the kriging estimates, respectively. However, like kriging, the estimation results were smoother than composites, and the variance of the XGBoost estimates was lower than the variance of composites. This means that even though estimation with XGBoost mitigates the smoothing effect, the estimation results suffer from smoothing like kriging.

Dec 1, 2024

By Yi-Hsuan Chen, Jürgen Brune, Candace S. J. Tsai, Alejandro Munoz, Connor Krause

This study highlights the significant presence of submicrometer- sized airborne particles in underground coal mines, especially around the belt conveyor drift areas. Using realtime instruments and gravimetric samplers, the assessment revealed that particle concentrations were notably higher in the belt entry compared to other locations, with a strong presence of submicrometer- and nanoparticle-sized dust. The study’s findings emphasize the importance of addressing small particle exposure, as these particles, composed of harmful substances like calcium, copper and iron, can pose serious health risks to miners. This research emphasizes the importance of improving air exposure estimation through more effective monitoring and characterization techniques to better understand and reduce workers’ exposure to hazardous airborne particles in the mining environments.

Apr 1, 2025

By Katherine Hynard, Colin Eustace

Suboptimal decisions introduced during the mine planning process are generally a consequence of both a myopic view of planning objectives as well as time and resources available to convert longer-term plans into execution plans. Erosion of strategic mine plan value in conversion to execution plans is likely to amount to billions of dollars each year. When integrated with operations data feeds, simulation and optimization tools can be used to preserve the value in the strategic mine plan. Optimization models are capable of both automating production of execution plans and improving performance. This reduces the manual burden of revising plans while improving alignment with long-term production objectives. Simulation models can also support the process of developing effective execution plans by providing forward visibility of mine operations performance. They can also be used to test the robustness of optimized execution plans and response to operational variability and disturbances.

May 1, 2025

By Mona M. Fawzy, Ahmed A. Eliwa, Ebrahim A. Gawad, Ahmed H. Orabi, Amal E. Mubark

The growing demand for rare earth elements (REEs) has led to numerous recent studies to recover these elements from various bearing ores and wastes. Therefore, the recovery of REEs from Ras Baroud polymetallic concentrate has been investigated in the current study. Physical beneficiation for the Ras Baroud pegmatite sample was carried out, yielding a concentrate for euxenite (Y), fergusonite (Y), xenotime (Y), monazite (Ce), allanite, thorite, uranothorite and Hf-zircon, which resulted in raising the concentrations of REEs, Th, Zr, U and Ti in the sample. Fusion digestion processes with sodium hydroxide were studied using the conceived predictive diagonal (CPD) technique. The three experimental digestion groups proved the dissolution of 99.9, 95.6, 99.9, 52.5 and 0.47 percent for REEs, Th, U, Ti and Zr, respectively, under fusion conditions of 723 K, 120 min, 1/1.5 ore-to-alkali ratio, and smaller than 100-μm particle sizes. Fusion kinetics, isotherms and thermodynamics were investigated using several suggested models, namely, pseudo reversible first order, uptake general model and shrinking core model, which matched well with the experimental digestion results. Selective recovery of actinide content from the REE content of the digested concentrate chloride solutions was accomplished using solvent extraction with di-2-ethyl hexyl phosphoric acid. About 99.9, 99.9 and 4.2 percent extraction efficiencies for Th, U and REEs were realized, respectively, using 0.3 mol/L solvent concentration in kerosene as a diluent, 1/2 organic-to-aqueous ratio, aqueous pH of 0.2 and contact time of 15 min. Thorium and uranium ions were stripped with 2.5 mol/L sulfuric acid solution with 94 and 98 percent stripping efficiency, respectively. A highly purified REE precipitate was obtained from the raffinate solutions. Zircon mineralization tailings were obtained as a byproduct through the alkaline digestion process.

Oct 1, 2024

By Adrianus Halim, Jenni Hooli, Bartłomiej Skawina, Fredrik Sundqvist

Transitioning from diesel-powered loading and hauling machinery to battery-electric vehicles (BEVs) responds to the need for deepening underground mines as regulations become tighter. When using BEVs, exhaust gases and diesel particulate matter (DPM) emissions can be eliminated, and the energy required for the machines is used more effectively. Unlike their diesel counterparts, which are refueled, BEV loading and hauling machines — specifically, load haul dump (LHD) machines — can use battery swapping. This brings additional aspects to the operation that need to be considered, which have not yet been studied extensively. This study used discrete event simulation (DES) to analyze how the different numbers of BEV LHDs affect queueing and productivity when using different numbers of batteries and different swapping times in an underground block cave mine. The results identified the formation of working groups, where the machines swapped their batteries concurrently within each group, and their relation to the queueing time. Additionally, the minimum number of batteries per machine was identified to maximize productivity while minimizing queueing time. This study demonstrates DES as a powerful tool to assess and analyze conceptual systems and identify their underlying structures, which can enhance the deployment of BEV LHDs in underground block cave mine operations.

Apr 1, 2025

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 while 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-mak-ing range is significantly reduced and the process for select-ing the ultimate pit becomes more consistent, replicable and defensible. The technique outlined in this paper provides an approach to including risk in the process used to select the ultimate pit.

Jun 1, 2024

By Jonathan K. Hrica, CASSANDRA L. HOEBBEL, Jennica L. Bellanca

Accidents involving powered haulage and mobile equipment like haul trucks are a major safety concern in the U.S. mining industry. They account for more than 50 percent of surface mine fatalities each year [1]. A recent National Institute for Occupational Safety and Health (NIOSH) analysis of 91 fatal incidents identified loss of vehicle control and situational awareness as key contributors [2]. However, the first-hand perspectives of operators are often missing, emphasizing the need for deeper insights into operators’ decisionmaking, lessons learned, sensory cues and mental strategies used during near-miss events. The findings in this analysis will help inform the development of training and safety solutions to prevent or reduce haul truck accidents.

May 1, 2025

By Connor Meinke, Shaihroz Khan, Kevin Erwin, Rajiv Chandramohan, Muhammad Usman Siddiqui

A geometallurgy study aims to link metallurgy and geology to reduce technical risk and enhance the economic performance of a mineral-processing plant. It does so by accounting for variability in a deposit to develop cash-flow models with variable throughput rates. High-quality sample selection for metallurgical test work that is representative of the deposit is an essential component of a geometallurgy study, but the large multidimensional data set makes sample selection a daunting task, as classifying the data set while respecting its heterogeneity is difficult. This paper presents a streamlined approach for sample selection, using statistical analysis techniques in Python. It cuts down time to select samples from around 1,200 s per drillhole to about 60 s for data classification and from 12 h to 8 h for handpicking samples from the classified data set, translating to cost savings. The cumulative sum method and k-means clustering method are used in the methodology to elegantly classify the data and select representative samples. The effectiveness of the methodology is demonstrated by presenting data from a prefeasibility study of a copper-iron mine in which 40 samples were selected for flotation test work.

Dec 1, 2024