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By Harold J. Schock

This paper describes an experimental method for the monitoring of diesel-generated exhaust gas pollutants in a dead-ended drift for the purpose of assessing the effect of control approaches. Experiments were conducted to determine the relative merits of these ventilation configurations: no auxiliary ventilation, induction ventilation at the entrance to the drift, and ventilation of the dead-ended drift with positive displacement tubing. The drift used in this experimental work was approximately 105 ft. (32.0 mf long and slightly downsloping, with an average height and width of 8 ft. (2.4 m) and 28 ft. (8.5 m1 , respectively. The data were gathered for assessing these ventilation methods and for gathering data to support a computer model of the dispersion and mixing processes. For this drift, it was found that ventilation with positive displacement tubing provided the best removal of diesel-generated pollutants. Induction ventilation appeared to be the least effective of the three methods examined. A comparison was also made between personal monitoring and area monitoring. Results of this analysis showed that in regions of high concentration gradients, it was not possible to get good agreement between point sampling and personal monitoring. In well-mixed areas, good agreement between the two methods of monitoring was obtained.

Jan 1, 1980

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 Lei Zhang, Zhibo Ma, Pingye Guo

The mechanical behavior of hard roofs often leads to significant deformation and strong mine seismicity in gob-side entry retaining (GER) of the longwall mining, affecting the mining safety and efficiency of coal mines. Field investigations indicate that the failure process of the retained entry by hard roofs can be divided into three stages. In this paper, theoretical analysis and numerical simulations are first used to evaluate the damage process of the retained entry under the hard roof. The results show that the instability of the retained entry in the conventional GER method results from the combined static-dynamic loads (in stage II and stage III) from the hard roof. Then, an innovative method, through fracturing roofs to maintain entry (FRME), based on GER with the hard roof is introduced to solve this problem. In this method, the roof of the mining area is pre-fractured, and when the coal seam is mined, the direct hard roof will collapse along the fractured surface due to its deadweight and mining pressure. The hard roof control effect on the static and dynamic loads, taking into account the innovative method, is verified with simulations and theoretical analyses. The results are compared with those of the conventional GER method. Furthermore, based on the numerical simulation study of dynamic responses, it is found that compared to the GER method, the dynamic response of the retained entry using the innovative method is more moderate and more conducive to the stability of the entry. Field application also validated the effectiveness of the innovative method to control the mechanical behavior of the “massive sudden collapse” of the hard roof.

Aug 14, 2023

By Brian H. Kaye

Efficient design of grinding circuits for efficient production of ultrafine material requires rapid feedback of information concerning the fineness of material leaving the grinding device. Generating this information requires efficient sampling and adequate rapid characterization of the material leaving the grinding machinery. Recent developments-in electrooptic inspection of dry end wet material leaving a grinding device are critically reviewed. New methods for characterizing ultrafines are discussed. A rapid operating automated cybernetic (intelligent) permeability device for automatic control of grinding circuits using Blaine index and other fineness indices is described.

Jan 1, 1983

By SONGHE WANG, YI XUE, PENG HOU, CAIHUI ZHU, XUANYE JIAO, Feng Gao

The abstract discusses the challenges of coalbed methane development in China due to low coal seam permeability. It highlights protective layer mining as a method to reduce in-situ stress and enhance permeability for improved gas drainage and disaster prevention. The study involves experimental and numerical simulations to analyze gas migration and permeability changes during stress release. It includes a stress-strain-seepage coupled test on coal, development of a mathematical model for coal seepage under mining unloading, and a numerical model based on the Wulan coal mine to explore in-situ stress release, permeability evolution, and gas drainage in an unloaded coal seam. The aim is to demonstrate the effectiveness of combining protective layer mining and gas drainage technology for gas outburst risk elimination.

Mar 14, 2022

St. Peter sandstone is an arenaceous sheet that covers large area in North America. The interest for St. Peter sandstone has been increasing due to the upward trend for hydrofracturing and the consequent need for fracking sand. This material is possesses very unique mechanical properties. On the one hand, it is brittle as characterized by an unusually high friction angle, up to 69°, and steeply curved failure envelopes. On the other hand, it is friable, possessing extremely low, and in most cases zero cohesion. The underground mines that are working under this condition are lacking the ground control techniques for this particular mining environment. The geotechnical properties of the St. Peter sandstone were examined using uniaxial test, triaxial test, porosity test, flexural strength test. This paper aims to investigate the effect of microstructure on strength properties, utilizing microscopic studies, index test (porosity, size distribution).

Feb 23, 2014

By E. R. Bauer

In response to the mandates in the MINER Act of 2006, the National Institute for Occupational Safety and Health (NIOSH) conducted refuge alternatives research that included characterizing the utility, practicality and survivability of refuge chambers and outby safe havens. NIOSH also prepared and delivered a report to Congress in late December 2007 that summarized the findings of the re-search, included recommendations concerning the design and performance specifications for refuge alternatives, and focused on specific information that could inform the regulatory process on refuge alternatives. This paper highlights NIOSH?s research and recommendations concerning refuge alternatives, survivability evaluations of refuge chambers and presents a brief review of the current deployment of refuge chambers in underground coal mines in the U.S. The research has lead to the conclusion that refuge alternatives have the potential for saving the lives of mine workers if they are part of a comprehensive escape and rescue plan and if appropriate training is provided.

Jan 1, 2009

By K. KAKLIS, O. SAUBI, Z. Agioutantis, R. JAMISOLA

Three-point bending (TPB) tests were conducted on prismatic Nestos marble (Greece) specimens. The specimens were instrumented with piezoelectric sensors, and comprehensive recordings of acoustic emission (AE) signals were obtained. Machine learning in the form of artificial neural networks (ANNs) was then applied in an effort to investigate whether specimen load evolution can be predicted as a function of AE signals. A number of ANN models were developed, and the optimum model was selected based on the highest coefficient of determination (CoD) value as well as the lowest root mean square error (RMSE) value that was calculated for each model. The best performing ANN model exhibits accuracy above 99% with an RMSE value below 4%. It can be concluded that ANNs can potentially be applied to predict rock behavior under load especially when such loads lead to failure.

Sep 7, 2022

By Ricardo Oliveira Gallart de Menezes, Claudio Gerheim Porto, Reiner Neumann

The Jiboia heavy mineral sands deposit is a new discovery associated with the São Francisco River delta on the northern coast of Sergipe state. The identified resources are part of the much larger mineralized trend. Mineralization is hosted in paleodunes deposited on Holocene marine terraces (Fig. 1). The dunes are up to 30 m above sea level, and mineralization occur from 6 m above sea level upward. The main ore minerals constitute pseudorutile, ilmenite, zircon, rutile and monazite. This work presents a thorough characterization of the mineralogy and granulometric distribution of the Jiboia orebody.

Apr 1, 2024

By K. Awuah-Offei, A. U. Rehman

"Information diffusion potential of a social media user affects how quickly information about a mineral project can diffuse through that user. If management can use social media and network analysis, in community engagement program, to understand how fast and how far information will diffuse to communities and followers of particular social media users, it can selectively focus on the most influential users. This paper proposes a method to examine the information diffusion potential of social media accounts. The work uses Gephi, an open source software, to extract and analyze data from different Twitter handles to predict their information diffusion potential. The work uses case studies of real Twitter users (e.g. mining news organizations and bloggers) associated with mining to illustrate the approach. The case study results show that there are significant disparities in information diffusion potential that will demand selective attention. This research will help mines develop effective social media engagement strategies. INTRODUCTION The mineral industry provides raw material for industrial activity to support our society (Martens and Rattmann, 2001). Mines have changed conventional mining practices to sustainable ones because of the negative impacts of mining and increased public awareness (Wang et al 2016). The concept of sustainable mining is getting mature as stakeholders (e.g., governments, investors and host communities) are getting involved in mining projects (World Economic Forum, 2014). The failure of mining companies to address the demands of these stakeholders might result in failure to acquire social license to operate (Thomson and Boutilier, 2011). The social license to operate (SLO), if granted, is mutually beneficial to all stakeholders. Mines prefer being granted SLO and keeping it to avoid any kind of social unrest due to their operation (Prno, 2013; Moffat and Zhang, 2014). Mining communities can grant different levels of SLO; ranging from withholding SLO to psychologically identifying with the project (Joyce and Thomson, 2000; Thomsan and Boutilier, 2011). Most of the previous work to understand community perceptions and SLO in mining is generally qualitative (ICMM, 2012a; Thomson and Boutilier, 2011; Prno, 2013; Moffat and Zhang, 2014; IFC, 2014). In particular, there is limited work on how community perceptions change over time due to information diffusion (Boateng and Awuah-Offei, 2017). However, SLO changes with respect to time (Yates and Horvath, 2013), which can result in risks that are not easy to predict. Mine managers and engineers have a desire to understand these changes so as to mitigate these risks (Que and Awuah-Offei, 2014; Que et al., 2015)."

Jan 1, 2018

By T. A. de Almeida, M. de Paiva Bueno, M. Powell

The mining industry is one of the foundations of modern society, providing not only the raw materials needed to maintain a contemporary lifestyle, but also serving as a basis for the development of future technologies. From the most basic components of an appliance to the materials needed to design an electric car or a space rocket, each raw material has gone through a painstaking procedure of extraction and processing, and ways to make this process more efficient will be of great help to meet an ever-increasing demand for minerals. One of the biggest challenges when extracting mineral resources is to measure the variability of the mineral body prior to processing. Comminution is the most power-demanding stage, and the lack of geometallurgical testing, often for financial reasons, may result in an inefficient operation. The Geopyörä Rock Breakage test was developed with the objective of making mineral variability data more accessible by providing both standard comminution parameters and rock mechanical properties at low cost, and with a modest sample size, allowing a larger number of samples to be tested to reduce uncertainties and assure productivity. The objective of this paper is to present the results of an extensive validation of this new rock breakage method against two of the main tests currently in use, namely the SMC® and Bond ball mill work index tests. More than 100 samples have been tested and the results compared, showing that the new method can accurately estimate the parameters of the traditional tests. This confirms that the new test is a reliable tool for comminution and geometallurgical tests.

Feb 1, 2023

By Xiaozhen Song, Ashish Ranjan Kumar, Shimin Liu

To investigate the influence of boreholes, fans, and curtains for large opening underground mines (LOUM), we conducted a field survey for LOUM with the survey data serving as the numerical modeling inputs using computational fluid dynamics (CFD) models. Five different CFD modeling results were obtained with different borehole, box fan, and curtain setting conditions. The results indicate that most air quantity directly escapes through outlet 1 after coming into the inlet, causing a huge loss of airflow; volumetric air exchange index (VAEI) and minimum hours of fully exchanging the air (MHFEA) measure air exchange efficiency, with zone 2 of case 5 having the highest VAEI; The borehole minimally affects zones 2, 3, and 4, but drastically alters airflow direction in zone 1; The box fan enhances airflow to zone 2, improving ventilation efficiency and increasing VAEI; Curtains have little effect on distant working faces but influence airflow near inlets and outlets. This study provides a foundation for future research on the influence of boreholes, fans, and curtains, as well as optimizing their placement and effectiveness in face ventilation.

Feb 22, 2026

By S. Torno

The elimination of ultrafine (<10 µm) clay particles is essential to the treatment of abandoned coal-washing plant tailings for the recovery of the remaining coal fraction by enhanced gravity processing. Effective hydrocyclone classification is required to remove this fraction. The highly variable composition and particle size distribution of such wastes creates significant difficulties in the operation of hydrocyclones for such classifications. Hydrocyclone laboratory investigations and their plant setup require lengthy or time-consuming tests. The use of empirical models of such classifications frequently requires modification to correspond to actual operating conditions. Results of the evaluation of computational fluid dynamics (CFD) modeling to predict hydrocyclone classification of coal-washing plant tailings are presented. Results of the standard k-epsilon turbulence, SSG turbulence and large eddy simulation CFD models are compared with the results obtained from analyzed hydrocyclone classifications. The CFD models tested are shown to be very effective in the simulation of hydrocyclone classifications for the determination of the classification particle size and adequate for determining the efficiency index values for many applications. The large eddy simulation model was identified as the most precise, but it is recommended that all three models be improved simulations of classifications above and below the classification particle size.

Jan 1, 2011

By Magnus Ericsson

The unexpectedly quick recovery in world crude steel production in the second half of 2009 was due almost entirely to increased production in China that began in late 2008. Previous peaks in monthly production in China were matched by April 2009. The rest of the world had still not reached pre-crisis production rates by May 2010. Chinese production drives growth, but there has been some growth in most other larger crude steel producing countries as well, compared to production in 2009. If the production rate of the first four months of 2010 continues, the total output of crude steel in 2010 will be somewhere around 1,410 Mt (1.5 billion st), the same as in the record year of 2007.

Jan 1, 2010

By Yucheng Li, De Huang, Yonghong Liu

Mine frictional resistance is one of the essential parameters of mine ventilation systems, and it is of considerable significance for the prevention and control of mine risks and accident hazards. Frictional resistance requires a lot of resources and time and has errors. To reduce the errors of frictional resistance, a regression model of frictional resistance is proposed based on the correlation analysis of the affecting factors. Through the comparative test analysis of seven regression methods, it is found that the classification and regression tree method for solving the proposed regression model has better performance than other methods. The least square error criterion is used to calculate and rank the importance of the factors affecting frictional resistance. The results show that the cross-sectional area of the tunnel, the support method, and the presence or absence of devices or pedestrian ladders have the most considerable influence on the prediction accuracy of frictional resistance. The support methods and cross-sectional areas are essential input variables. Through the combination test analysis of the influencing factors, the results show that the regression model does not have predictive ability when the number of input variables is less than 3 and the best predictive performance cannot be obtained using all the influencing factors. The best prediction model of resistance is to combine the shape of the tunnel, the support method, the presence or absence of devices or pedestrian ladders, and the cross-sectional area. The method proposed in this paper does not require a lot of resources and time investment, and the frictional resistance error is small. At the same time, it solves the problem of resistance prediction with incomplete data of influencing factors and provides a compelling new method for obtaining frictional resistance parameters.

Jan 28, 2022

By Pratt Rogers, Aaron Young

This paper considers the mining industry’s need for digital transformation (DT) and presents a three-part review of the foundational components of the DT process: ubiquitous data, connectivity and decision-making. It also provides a discussion on the future of DT in mining as well as a research direction, which identifies the need of academia to provide support for mining companies in the form of both research and the education of digitally literate, dynamically capable mining engineers into the coming decades.

By J. Christophe Niyonzima, Ekata Emmanuel Edo, Sayaf Mustafa, Liqun Luo

Iron and manganese both are important ferrous metals, and they usually need to be extracted separately for better usage. Ferromanganese ore from the Yunnan Province of China was investigated by subjecting it to reduction roasting, leaching, magnetic separation, hydrometallurgical separation process, X-ray diffraction (XRD), electron probe micro-analysis (EPMA) and scanning electron microscopy/energy-dispersive spectroscopy (SEM-EDS). It is noted that the ferromanganese ore contains valuable components or elements of 21.41 wt%Fe2O3 and 19.88 wt%MnO, which are 14.99% and 15.40% of the total of Fe and Mn, respectively, with a large amount of impurities comprising 33.00 wt% SiO2, 9.81%wt%Al2O3, etc. After roasting, microscopic examination showed the presence of haematite, magnetite, pyrolusite, psilomelane, hollandite, marcasite, pyrite, limonite, braunite and manganite, while XRD analysis showed that the magnetite content increases with the increase in roasting temperature, reflecting the increase in conversion of haematite to magnetite. Based on EPMAand SEM-EDS analysis, it was shown that the haematite and magnetite interface were the main Fe-bearing minerals; however, the dissemination of the two minerals, that is, haematite andmagnetite with the coexistence of Fe- and Mn-bearing minerals, will result in difficulty in effective separation of Fe and Mn. The effective roasting temperature and roasting time were 800 °C and 60 min, respectively, to avoid the formation of a composite oxide of MnxFe3-xO4 beyond 800 °C. The Fe recovery via roasting and magnetic separation only was 66.56%, whereas a combination of magnetic separation which ensued after the leaching process showed a recovery of 80.08% and 59.95% of Fe and Mn, respectively.

Mar 26, 2021

By Staff

"In 2018, the total estimated value of nonfuel mineral production in the United States increased by 3 percent from 2017 to $82.2 billion. The estimated value of metals produced domestically decreased by 4 percent, and the estimated value of indusrial minerals produced domestically increased by 7 percent (Table 1).Some idle U.S. metal mines and processing facilities restarted in 2018, including two primary aluminum smelters in Indiana and Missouri, a rare-earths mine in California and a zinc mine in New York. In contrast, four iron ore mines in Minnesota and a pelletizing plant in Indiana were shut down. Increased oil and natural gas drilling activity in 2018 led to increased production of some industrial mineral commodities that are used for oil and gas drilling, including barite and industrial sand and gravel. Production of industrial minerals primarily used in infrastructure and residential construction — including, cement, construction sand and gravel, crushed stone and gypsum — increased in 2018. Overall, mining activity trended upward for the second consecutive year because of increases in oil and gas extraction and industrial mineral mining (Table 2).Mineral industry performanceDiscussion of mine production is commonly segmented according to the type of materials produced within the broad categories of metals and industrial minerals (also known as nonmetallic minerals). Industrial minerals can be further subdivided as natural aggregates and other industrial minerals. Metals tend to have higher unit values but very low production quantities compared with those of industrial minerals, such as crushed stone or construction sand and gravel, which have higher production quantities but are lower-valued materials. Therefore, for discussion and analysis of the performance of the nonfuel minerals industry, the value of production is used rather than the tonnage produced. Tonnages of natural aggregates are orders of magnitude larger than those for most other mineral commodities, thus making direct comparisons based on weight meaningless (Fig. 1)."

May 1, 2019

By A. Anani

The goal of hot mix asphalt (HMA) design is optimal material blending to meet Superpave specifications. HMA cost minimization has being modeled as linear programing (LP) (Awuah-Offei et al 2011) and mixed integer LP problems (Brown et al, 2012) and solved with commercial software such as LINDO and CPLEX. Often these software, which are designed to solve general optimization problems, have licenses that cost thousands of dollars. The objective of this study is to develop a novel solution algorithm to the HMA optimization problem presented by Brown et al. (2012), which will negate the need for expensive commercial packages and is specific to this problem. This novel algorithm is based on the branch and bound algorithm for solving mixed integer problems. The algorithm is incorporated into a software package with an easy-to-use graphical user interface. The developed algorithm is validated using a real-life HMA problem. Using this solution algorithm will save companies thousands of dollars, otherwise used to purchase software licenses.

Feb 23, 2014

By P. J. Ryan

Phelps Dodge’s Candelaria copper-gold mine is located 20 km (12miles) south of Copiapó in northern Chile’s Atacama province (Region III) (Fig. 1). The Candelaria Mine is about 800 km (500miles) north of Santiago and 75 km (47 miles) east of the Pacific Ocean. Its elevation is 600 m (1,970 ft) above sea level. The Copiapó River Valley is 6 km (4 miles) northeast of the mine site. It is the only major source of water in this arid region. The average rainfall is 14mm/a (0.5 in./year). During 1995, the mine produced 521.4 kt (574,800 st) of copper concentrate containing 150 kt (165,000 st) of salable copper and 3 t (98,800 oz) of gold. The 1995 unit cash cost was less than 50 cents/lb of copper. This makes Candelaria one the world’s lowest cost copper producers.

Jan 1, 1996