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By Yun-tao Xin, Yu Yi, Kang Yan, Gang Li, Xiao-dong Lv

This study investigated the selective removal of arsenic from high-arsenic dust in alkaline systems and the effects of different leaching conditions. The results indicated that the liquid–solid ratio, NaOH concentration, and sulfur dosage had a significant influence on the process. The leaching efficiency of arsenic reached 99.57%, while that of lead was as low as 0.03% under appropriate conditions. In particular, the addition of sulfur can effectively promote the leaching of arsenic and reduce the dissolution of lead in the solutions. An artificial neural network was used to model the leaching process. It consisted of a back-propagation artificial neural network model with a “6–10–2” structure that could effectively simulate and predict the value with more than 99% accuracy. Based on the difference in weights of the different parameters in the neural network model, the relative importance of the parameters related to arsenic and lead leaching efficiency was obtained, which followed the order of NaOH concentration, liquid–solid ratio, sulfur dosage, temperature, time, and stirring speed.

Jul 29, 2021

By N. K. Singo, J. D. Kramers

The reprocessing of tailings resources to extract gold on an industrial scale has become common practice. While these projects are common in the Witwatersrand basin, similar low-technology processes are not operational in smaller goldfields. This study explores the possibility of reclaiming the tailings dump of the Louis Moore Mine in the Giyani Greenstone Belt, investigates potential hazards to communities in the vicinity, and identifies mitigation strategies. Auger samples were taken from the Louis Moore tailings at depths of up to 5 m. Aqua regia leach analyses show Au concentrations of up to 1 g/t. Inferred estimations based on ordinary kriging (OK) and inverse distance weighted (IDW) methods put the residual Au resource in the Louis Moore tailings dump at 0.20 t. Reworking the tailings is viable, although a potential environmental risk exists in the form of elevated arsenic concentrations. Further exploration is required to determine the mineralogical associations of Au and As. Tailings reworking would assist in raising funds for mine rehabilitation. The secondary tailings could potentially be repurposed, which would provide employment and facilitate community development, as well as deliver environmental benefits.

Jul 1, 2021

By A. K. Raina

Blast induced damage to the final wall of rockmass in any civil or engineering application is a major concern to the rock excavation engineers. There are at least four distinct techniques practised by blasting engineers to minimise the damage to the parent rock during blasting that are classified as contour blasting. One of the methods to achieve a smooth wall or rock surface in blasting is pre-splitting, where blastholes in the final row are mildly loaded with explosives and fired first in sequence before the rest of the holes in a round of blast to achieve a split in rockmass. Since blasting is one of the most adopted methods for achieving a final wall in a desired shape, while maintaining the rockmass integrity, the design of pre-split blast assumes importance. There are several variables that determine the final shape of the pre-split face including the rock type, the drill diameter, the explosive characteristics, the orientation of major joints with respect to drill hole, the linear charge density, the spacing of drill holes and inclination of the drill holes. This study attempts to determine the importance and sensitivity of variables in pre-split blasting using historical data from open excavations of a hydroelectric project in India. Keeping in view the multitude of variables, low correlation of independent variables with responses and high level of interactions between the variables that define the blast results, it was found suitable to use ANN to determine the importance and sensitivity of the variables involved. A new method to determine the blast induced damage to rockmass, namely, undamaged area (AUD%), is introduced. Half Cast Factor (HCF%) and AUD%were calculated for a high wall and trenches by digital image analysis technique using the Fragalyst software. Joint spacing and linear charge density emerged as most controlling variables in determining the final result of pre-blast. The relative importance of other variables assuming significance in the case of both the designed outputs is also discussed. A comparative analysis of HCF%and AUD%on the basis of results obtained from ANN analysis is also presented. A futuristic approach concept has also been introduced.

May 30, 2021

By E. Petrakis, K. Komnitsas

The main objective of batch grinding modeling is the estimation of the product particle size distribution over time or specific energy input to the mill. So far, the developed analytical methods require often complicated calculations which are timeconsuming. Thus, more simple approaches that allow the reliable prediction of the complete product size distribution need to be developed. In the present paper, an approach, based on the population balance model (PBM), that reliably predicts the product size distribution is proposed. In order to enable this, the simplified form of the fundamental batch grinding equation was transformed into the well-known Rosin–Rammler (R-R) distribution, thus allowing the determination of the breakage rate for each grinding period. A time-dependent breakage rate framework was developed where the traditional linear theory of the PBM is considered a partial case. This approach allows the deviations from the linear theory to be categorized and the degree of the acceleration-deceleration of the breakage rate to be predicted. Modeling results were validated by laboratory grinding studies using two homogeneous materials, quartz and marble, and one heterogeneous, a limonitic laterite. The experimental data revealed that grinding exhibits non-first-order behavior and the degree of deviation from the linear theory depends on the tested material. The reliability of the proposed model was validated with the use of the distribution modulus n, the a value of the breakage rate parameter, and the optimum n values that minimize the sum of the differences between the experimental and estimated particle size distributions.

Feb 8, 2021

By Muhammet Mustafa Kahraman

Despite technological advancements and organizational adjustments, lost time accidents are major issues in occupational safety. However, there is very limited work that focuses on variables influencing days lost as a result of occupational accidents. In this study, decision tree and artificial neural network methods were used as machine learning techniques to investigate the impact of factors on accident lost day duration. Degree of injury, worker age, and worker activity were found to be the top three variables impacting loss of time from work. It was also identified that the mining method, location, and nature of injury had a moderate influence on duration lost due to occupational accidents. However, worker experience and ore type did not have any significant impact on the duration, which is an unexpected result. These results confirmed that some accident factors that might have a large influence on the number of mine accidents can be less critical when it comes to accident lost day duration.

Feb 3, 2021

By Mei Long, Tawum Juvert Mbah, Jianhua Zhang, Haiwang Ye

There have been many improvements and advancements in the application of neural networks in the mining industry. In this study, two advanced deep learning neural networks called recurrent neural network (RNN) and autoregressive integrated moving average (ARIMA) were implemented in the simulation and prediction of limestone price variation. The RNN uses long shortterm memory layers (LSTM), dropout regularization, activation functions, mean square error (MSE), and the Adam optimizer to simulate the predictions. The LSTM stores previous data over time and uses it in simulating future prices based on defined parameters and algorithms. The ARIMA model is a statistical method that captures different time series based on the level, trend, and seasonality of the data. The auto ARIMA function searches for the best parameters that fit the model. Different layers and parameters are added to the model to simulate the price prediction. The performance of both network models is remarkable in terms of trend variability and factors affecting limestone price. The ARIMA model has an accuracy of 95.7% while RNN has an accuracy of 91.8%. This shows that the ARIMA model outperforms the RNN model. In addition, the time required to train the ARIMA is than that of the RNN. Predicting limestone prices may help both investors and industries in making economical and technical decisions, for example, when to invest, buy, sell, increase, and decrease production.

Jan 6, 2021

By Jonathan K. Hrica, Brianna M. Eiter

The ability to identify hazards during a workplace examination is a critical skill for mineworkers to have in order to maintain a safe workplace. While research suggests that being able to successfully recognize hazards requires a complex set of competencies, it is unclear which competencies are most critical to adequately perform a workplace examination. This paper presents a hazard recognition competencies framework as an explanation of the competencies that affect mineworker hazard recognition. To better understand how the industry is selecting and preparing workplace examiners, researchers fromthe National Institute for Occupational Safety and Health (NIOSH) interviewed nine health and safety (H&S) professionals at surface stone, sand, and gravel mine sites throughout the USA. A primary purpose of these interviews was to verify the competencies included in the hazard recognition competencies framework and to understand why these competencies are critical. Finally, the results of this study are presented within the context of current NIOSH research findings related to hazard recognition.

Jul 27, 2020

By SHANTAE LEE, ELAHEH TALEBI, Frank A. Drews, W. Pratt Rogers

Fatigue in mining operations is a serious issue and a significant contributor to incidents and accidents. While mine operators are using or introducing new technology to monitor operator fatigue, there is little work that focuses on the subjective experience of fatigue and the use of fatigue monitoring technology. To investigate this issue, eight focus groups with a total of 44 operators of heavy haulage equipment working for two mining companies in the Mountain West of the USA participated in 1-h focus groups. The topic of the focus groups was the experience of operator fatigue and issues surrounding fatigue at the workplace. The results of the content analyses suggest that operators are well aware of the signs of fatigue, and that they understand the severity of these signs as well. In addition, they are aware of the effect of fatigue on driving performance and are using a wide range of methods to manage fatigue. Finally, based on participants’ assessment, current fatigue monitoring systems lack face validity and reliability and are perceived as not being effective. The results of this study emphasize the importance of a socio-technical systems perspective in the context of miner health and safety management. Only such a comprehensive perspective can result in improvements that are sustainable.

Jun 17, 2020

By Sebastian Avalos, Julian M. Ortiz, Willy Kracht

Semi-autogenous grinding mills play a critical role in the processing stage of many mining operations. They are also one of the most intensive energy consumers of the entire process. Current forecasting techniques of energy consumption base their inferences on feeding ore mineralogical features, SAG dimensions, and operational variables. Experts recognize their capabilities to provide adequate guidelines but also their lack of accuracy when real-time forecasting is desired. As an alternative, we propose the use of real-time operational variables (feed tonnage, bearing pressure, and spindle speed) to forecast the upcoming energy consumption via machine learning and deep learning techniques. Several predictive methods were studied: polynomial regression, k-nearest neighbor, support vector machine, multilayer perceptron, long short-term memory, and gated recurrent units. A step-by-step workflow on how to deal with real datasets, and how to find optimum models and final model selection is presented. In particular, recurrent neural networks achieved the best forecasting metrics in the energy consumption prediction task. The workflow has the potential of being extended to any other temporal and multivariate mineral processing datasets.

Jun 16, 2020

By A. M. Anthony, A. Y. Atta, S. S. Magaji, U. Abubakar-Zaria

There are two major gold recovery methods: the hydrometallurgical technique, which employs cyanide solutions, and amalgamation method, which involves mercury (Hg). These methods present considerable health, safety, and environmental concerns. Past applications of alpha-cyclodextrin (α-CD) on gold scraps show that α-CD is not only inexpensive, but is also environmentally friendly. This research work used α-CD to recover gold from gold ores instead of gold scraps. The gold ore was sourced from Shanono gold deposit site Kano State,Nigeria. The gold ore was characterized by fire assay (FA) for the precious metal. The concentrate was digested with aqua regia (AR) for 24 h. Modified AR, where hydrochloric acid was replaced with hydrobromic acid, was used for the gold digestion due to the good match in molecular recognition between gold bromide ion (AuBr)− and α- CD in a second sphere coordination complex. The digestate was characterized using atomic absorption spectroscopy (AAS). Gold recovery using this technique is pH sensitive, and five pH values (2.0, 3.6, 4.0, 5.4, and 6.0) were investigated. FA analysis showed the Shanono gold ore deposit contains an average of 4.71 ppm of gold, AAS analysis showed highest gold recovery of 69% at pH 4.0, which is lower than recoveries from the metal scraps. This could be due to the presence of other gangue minerals closely associated with the ore. However, gold recovery using α-CD on Shanono gold ore has shown to have a higher yield of 69% compared to 35.5% yield obtainable by Artisan Small Scale Miners using mercury.

May 5, 2020

By Ankit Jha, RICHARD AMOAKO

In this paper, we examine the challenges associated with the use of empirical rock fragmentation models. We highlight key parameters omitted by these models, and propose a machine learning approach that incorporates these parameters, leading to a simple, yet more accurate approach to blast design.

Feb 1, 2020

By Barbara Rutter, Phillip Mulligan

This research investigates how changes in shock tunnel geometry affect the pressure versus time waveform. The Large Arena Test Simulator (LATS), which is composed of four different rectangular sections connected by three expansion sections, was used to determine how the changes in geometry affect the shock wave profile.

Feb 1, 2020

By E. J. Y., G. J. McLachlan, E. Amini, Koha

Inferring individual component flotation rates from recovery-time data of ore complexes is important in optimising and designing minerals processing plants. However, existing two-component first-order flotation models are computationally intensive to solve using conventional non-linear least squares regression. In this paper, a Convolutional Neural Network (CNN) is used to approximate the inverse function which predicts the two-component first-order model parameters using recovery-time data. This paper compares the accuracy and computation speed of the proposed CNN against traditional non-linear least squares regression on 1 million generated data based on a known two-component firstorder kinetic model. It is shown that using a CNN is possible to approximate the inverse function for the non-invertible two-component model with recovery-time as an input matrix for convolution operations. The CNN method is 450 times faster to compute than the Trust-Region (TR) non-linear least squares regression algorithm. However, the CNN model is less accurate compared to the TR algorithm overall. Keywords: Flotation kinetics, convolutional neural network, inverse problem, modelling and simulations

Jan 1, 2020

By E. Arghavani, S. H. Amiri, S. Zare, F. Sepehri, M. Ramezanizadeh

Mineral processing plants include different stages like crushing, grinding, classification and concentration, all of which have partially affect the final product. To identify and solve the related problems of each stage, process audition should be carried out continuously to find the major solutions of improving product and decreasing the maintenance and operating costs. The Iranian Gohar-Zamin iron ore beneficiation plant consists of equipment such as gyratory and cone crushers, high pressure grinding rolls (HPGR), ball mill, dry and wet double-deck vibrating screens, hydrocyclones cluster, magnetic drum separators, thickener and horizontal vacuum belt filters. The defects of initial design as well as current non-standard operating conditions of equipment were monitored and detected and the opportunities for process optimisation were presented as follows: establishing the control loop of power draw for cone crushers, the modification of feeding chute in cone crushers to minimise the non-uniform wear of parts, removing segregation in the fresh feed bin and screen’s circulating load chute in order to provide the proper feed to HPGR, reducing wear rate on HPGR rolls surfaces, decreasing the amount of rejected materials in screen, equalising the solid content and particle size distribution (PSD) of feed in all magnetic drum separators and equalising the PSD of hydrocyclones overflow. This paper provides a guideline for the mineral processing industry to focus its efforts on process design to deliver the efficiency with minimum capital costs. Keywords: Process audition, gyratory crusher, cone crusher, HPGR, screening, hydrocyclones, magnetic drum separators

Jan 1, 2020

By CHRISTOPHER D. HEBERT, SCOTT W. HOFFMAN

All Rights Reserved. Printed in the United States of America. Information contained in this work has been obtained by SME, Inc. from sources believed to be reliable. However, neither SME nor its authors guarantee the accuracy or completeness of any information published herein, and neither SME nor its authors shall be responsible for any errors, omissions, or damages arising out of use of this information. This work is published with the understanding that SME and its authors are supplying information but are not attempting to render engineering or other professional services. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Any statement or views presented here are those of individual authors and are not necessarily those of the SME. The mention of trade names for commercial products does not imply the approval or endorsement of SME.

Dec 1, 2019

By Anthony Garotinho Barros Assed Matheus de Oliveirar, Wagner Anacleto Pinheiro, Andreza Menezes Lima

Óxido de grafeno foi sintetizado e posteriormente reduzido pelo uso de vitamina C. Para comparação entre o óxido de grafeno (GO) e o óxido de grafeno reduzido (rGO) foram utilizadas as técnicas de difração de raios X (DRX), microscopia eletrônica de varredura (MEV) e espetroscopia Raman (ER). Na etapa da síntese do óxido de grafeno por DRX confirmou-se a efetiva oxidação do grafite precursor, enquanto por MEV observou-se a esfoliação dos flocos de grafite em folhas de GO. Após a redução, por meio de DRX e ER analisou-se a remoção dos grupos funcionais, enquanto por MEV a morfologia das folhas do material.

Nov 16, 2019

By Luiz Bruno de Oliveira Araujo, Luciano Morais Teixeira, Luiz Gustavo Pedrosa de Melo, Altair Lúcio de Souza

The off-line simulation of the metallurgical process in an industrial line brings several benefits such as: evaluation of the influence of the main parameters that affect the results of the process, comparison between different set ups, greater assertiveness in operational adjustments, process optimization and reduction tests for development of new products. With this objective, a software for offline simulation of the hot strip mill process of Gerdau Ouro Branco was developed based on the logic of calculations implemented in the online model. The software results in important process variables such as mill pass scale, load, torque, and power at each pass, temperature and the hot mean flow stress of the simulated material. During rolling mill process the strip presents different temperatures and time between passes for different points along the length, which directly influences the hot strength of the product and consequently in the calculated variables, thus the calculations executed by the software were divided to three points of the strip, head, body and tail bringing the individual results to each of these positions.

Oct 1, 2019

By M. Schreiner, J. Brune, D. Theisen, C. S. -J. Tsai

This study reports that routine mining activities could produce a high number of nanometer sized particles which have not been well characterized and may represent an unacknowledged exposure present in the mining environment. A preliminary survey of the particle emission from the feed-leg drilling activity was conducted in the excavations of an experimental mine. The level of particulate exposure was monitored by real time direct reading instruments include a NanoScan scanning mobility particle sizer (measurement range 10-420 nm) and an optical particle sizer (measurement range 0.3-10 μm). Particulate morphological and structural examination was conducted through transmission and scanning electron microscopy and x-ray dispersive analysis of samples collected by a thermophoretic sampler, and a novel nanoparticle sampler. The real time instrument data revealed high concentrations (> 3.5 x 106 particles/cm3) of ultrafine/nanoparticles generated from the drilling activity. Analysis of particle sampler specimens with energy-dispersive x-ray spectroscopy showed a large amount of submicron silica, spherical primary particles and agglomerated soot particles rich in carbon. Exposure to particles in the nanometer size from various sources within the mining environment may be associated with the poorly understood rise in respiratory and systemic disease among miners. INTRODUCTION Exposures to mine dust have been measured and evaluated for decades. Exposures to aerosolized dust such as coal or silica particles can cause lung and other diseases (1-7). One way that The Mine Safety and Health Administration (MSHA) characterizes inhalation exposures within the mining environment is through measuring the respirable dust mass concentration within the breathing zone of workers (8). Respirable particulate matter (RPM) is of great concern due to its ability to deposit in the alveolar region of the lungs (9-10) where it can affect respiratory function. Interventions championed by MSHA have greatly reduced the prevalence of coal workers pneumoconiosis (CWP) and progressive massive fibrosis (PMF). Although great progress has been made, the prevalence of CWP and PMF appears to be trending upwards and the measured increase in prevalence is not elucidated in RPM measurements (11-12). Though the presence and risk associated with ultrafine particles such as emission from diesel engine within the mining environment has been previously established (13-14). There is suspicion within the mining industry that this resurgence of lung disease may be due to very fine particulate matter from various source that is not properly characterized by current sampling methods. Nanometer sized particles, in the range of ultrafine particles, fall within the RPM size range and have been found to deposit in all regions of the respiratory tract (15). Nanoparticles are commonly accepted as particles that are greater than 1 nm in two or three dimensions but smaller than 100 nm (16). Once in the lungs, nanoparticles can enter the blood stream (17) and selectively affect other target organs such as the heart, liver, bone marrow, lymph nodes, spleen, and brain (15,18-19). The size of nanoparticles has been shown to correlate with their translocation behavior on secondary target organs (20-21). There is strong evidence that the clearance mechanisms used to rid the lungs of particulate matter are not as effective on nanometer sized particles, leading to a greater fraction of never-cleared particles which may enhance disease development (20). Research has demonstrated that nanometer sized particles have unique properties (22) whose adverse health impact is greater than what has been found with micrometer sized particles of the same type (23-26). The large ratio of surface area to mass compared to larger particles is believed to make nanoparticles a greater hazard. Utilizing mass to measure these types of exposure will be very difficult to accurately characterize the quantity of

Jan 1, 2019

By S. Kevin McNeil, William Joa, Catherine Johnson, S. Omar Garcia

The geometry of an explosive is known to have a fundamental effect on the resulting shock wave propagation. Typically researchers use a spherical or hemispherical geometry in order to simplify the shock wave so that resultant overpressure and impulse are uniform in all directions. The goal of this research is to understand the overpressure characteristics from a sheet explosive, a geometry often utilized for breeching in the military. In training alone, soldiers can be exposed to several tests per day, which may be contributing to the prevalence of blast induced mild traumatic brain injuries. Using PCB pressure transducers and high speed video, it was found that as the angle departed from normal (perpendicular) to the face of the sheet explosive, the over pressure decreased significantly. When in line with the plane, a decrease of over pressure by as much as 20% was observed. Time of arrival was roughly 20% faster directly in front of the charge with a pressure amplification of 46% relative to a spherical baseline charge of the same mass. This testing may reveal a more complete relationship between the pressure and angle from the explosive that could have applications for military and law enforcement with respect to best practices on blaster location for reduced over pressure exposure.

Jan 1, 2019

By J. Navoyski, B. Macdonald, W. J. Helfrich, J. L. Bellanca, B. Demich

DISCLAIMER The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention. Mention of company names or products does not constitute endorsement by NIOSH. ABSTRACT The pervasiveness of high-fidelity video games raises expectations of quality, detail, and lighting in virtual reality (VR) applications. However, high-quality models of mining equipment and environments are often not readily available, and developers may not be familiar with the latest techniques, such as physically based rendering (PBR). Because a consistent aesthetic is critical for maintaining immersion and controlling visual stimuli in research as well as in training, researchers at the National Institute for Occupational Safety and Health (NIOSH) created an asset development workflow. This paper details this workflow, including precision modeling and PBR techniques as well as a rigorous review process. It also describes lessons learned in the creation of photorealistic VR assets. BACKGROUND Virtual reality (VR) describes the simulated experience generated by an assemblage of hardware and software that are used to immerse the user. Over many years VR developers targeted the development of virtual environments (VE) toward various purposes: entertainment, the treatment of phobias [1], rehabilitation [2], and training [3]. A large part of the VE development includes digital assets, where digital assets refer to the non-programmed elements including 3D models, audio, and user interface. Because vision provides nearly 10Mbps of data [4] and accounts for the majority of all sensory input combined [5], a focused effort on asset appearance in VEs is warranted. Furthermore, research has shown that higher-resolution imagery increases search performance and training transfer, necessitating high-fidelity assets [6, 7]. In addition to being visually appealing, it is important that the assets and environment are also dimensionally accurate and consistent in order to elicit appropriate responses from the end users as they interact with and among these items [8, 9]. Researchers at the National Institute for Occupational Safety and Health (NIOSH) have developed VEs and digital assets under several research projects over the course of the last two decades [10, 11, 12, 13]. This work has provided researchers with a strong knowledge base for digital asset development. However, the assets themselves have had limited reuse value. Due to the extended timeframes and limited scope of these projects, the previously developed assets are not consistent in visual quality and dimensional accuracy. Moving forward, researchers wanted to minimize future development time and use of resources as well as adopt new asset development tools and game engine technology. Therefore, the aim of this paper is to document a new asset development workflow including geometry, materials, UV layout, and shader implementation combined with review steps in order to produce high-fidelity simulation assets that are accurate with consistent characteristics, using repeatable methodologies that are well documented.

Jan 1, 2019