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By L. D. Patts, S. J. Janisko, E. G. Cauda, A. D. Bugarski, G. H. Schnakenberg, J. A. Hummer

Filtration systems with disposable filter elements (DFEs) are used in the underground coal mining industry to control particulate matter emissions from diesel-powered permissible and nonpermissible coal mining equipment. This study was conducted in underground mine conditions to evaluate three types of high-temperature DFEs used in those filtration systems. The DFEs were evaluated for their effects on the concentrations and size distributions of diesel aerosols and concentrations of nitric oxide (NO) and nitrogen dioxide (NO2). Those effects were compared with the effects of a standard muffler. The experimental work was conducted directly in an underground environment using a unique diesel laboratory developed in an underground experimental mine. After an initial DFE degreening period, the filtration system with all three DFEs was found to be very effective at reducing total mass concentrations of aerosols in the mine air. The effectiveness of DFEs in filtering aerosol mass was found to be a function of the engine operating conditions. The efficiency of the new DFEs significantly increased with operating time and buildup of diesel particulate matter in the porous structure of the filter elements. A single laundering process did not exhibit substantial effects on the performance of the DFE elements. The effectiveness of DFEs in removing aerosols by number was strongly influenced by engine operating mode. The concentrations of nucleation mode aerosols in the mine air were found to be substantially higher for DFEs when the engine was operated at high-load modes rather than at low-load modes. Initial heating of certain DFEs resulted in visible white smoke and substantially elevated aerosol number concentrations. The effects of the DFEs on the total concentration of nitrogen oxides (NOX) were found to be minor. The NO2 fraction was found to be generally lower for the DFEs than for the muffler. The engine-out NO2 fraction of the total NOX was found to be substantially higher for low-load modes than for high-load modes..

Jan 1, 2011

By Weiran Zuo, Jinsheng Guo, Chun Yu, Jinyu Zhan, Bao Guo

High-precision prediction of the target minerals’ content in the feed and concentrate products is vitally important for the efficient beneficiation of mineral resources. Visible and near-infrared (NIR) spectroscopy provides a cost-effective way for online measurement of the mineral content in an industrial process. In this investigation, simulated specimens consisting of five different types of minerals that are present in the copper oxide ore, malachite, quartz, calcite, hematite, and chlorite were prepared with a variety of malachite contents, and the mixed specimens were analyzed by a visible-NIR spectrometer in an integral sphere mode. The reflectance spectrum is used as input and the malachite content as output to build the prediction model. The obtained data was modeled by support vector machines (SVM), and a Grey Wolf Optimization (GWO) is proposed with the goal of improving the prediction accuracy. The GWO algorithm has been applied to adaptively search for the best combination of featured values. After cyclic comparison, the optimal penalty factors c and g can be quickly and accurately selected. The experimental results show that the SVM model established by the GWO algorithm has a better fitting effect and smaller prediction error, compared with other models.

Aug 3, 2023

By Ronald T. Cosburn

The Province of Ontario covers an area of some 400 thousand square miles, however the subject I will be talking about today, the licencing of pits and quarries in southern Ontario, refers to about one quarter of this total area but contains better than 93% of the total population of the province or about 8 million people. Southern Ontario had and still has many inviting features to industry. The Great Lakes almost surround us, hydro power has been relatively inexpensive and abundant, but more in line with our topic today are the ample deposits of sand, gravel and limestone, all of which are used directly or indirectly throughout and are the mainstay of our construction industry. I say we are fortunate to have these non-renewable resources in abundance in close proximity to our industrial and metropolitan areas, but like so many of our resources we have taken them for granted for too long. What has happened in our case, as I am sure is common in many of your own similar situations, because of the recent, current popular trend by naturalists and environ- mentalists to preserve our natural heritage at the expense of all else, through parklands and indiscriminate zoning policies, these once abundant reserves have been tremendously eroded.

Jan 1, 1982

By E. J. Haas, K. A. Rost

"This study sought to identify changes in continuous mining machine (CMM) operators’ risk perception and risk behaviors as a result of adding a proximity detection system (PDS) into their environment. To accomplish this research task, 9 CMM operators from 5 locations were interviewed. Interviews were thematically analyzed for constructs including patterns in risk perception and behavior before and after PDS integration and the process of adapting to the PDS. Operators discussed standing in the red zone less than they did before using the PDS; however, they also discussed other risky decisions they made while learning how to operate their CMM with the PDS technology such as “cheating” the technology and working faster to meet production goals. The process of relearning job tasks with technology and unintended behaviors as a result of that relearning process need to be taken into account when introducing new technology into the mine environment. The study results show an in-depth view of mine workers’ perspectives and how their job tasks and environment could be or are affected when learning how to use new technology that are relevant to companies and mine operators when introducing new technology into the mine environment. INTRODUCTION Established “red zones” can help prevent striking and pinning incidents by providing guidance to continuous mining machine (CMM) operators about where they should stand and not stand while operating a CMM. Although red zones influence operator placement positions, these guidelines are not foolproof safety mechanisms. Depending on the job task and circumstances of the mine environment, a CMM operator may unintentionally or intentionally stand in a red zone. For example, CMM operators may work in close proximity to their CMM for better visual attention cues and the ability to work at a higher rate of production [1-3]. To further enhance protection and prevent collisions between vehicles and mine workers, a proximity detection system (PDS) was developed for the industry. The PDS determines an individual’s position relative to a specific piece of machinery, issues visual and/or audible warnings to the mine worker to change positions, and, if necessary, disables machine motion [4]. The introduction of new technology within other sectors has been identified as a factor that influences work decisions. Specifically, previous research demonstrates that when technology debuts there can be an initial intrinsic risk perceived among users about how the technology will interact with their current environment and behaviors; however, these ambiguities, including the possible risks and benefits, are seldom studied [5]. Some research suggests that the constant presence of technology can disrupt situation awareness and decision-making capabilities by increasing worker responsibilities and worker cognitive demands [6-8]. Further, if workers’ perceived risks both with and without technology are misjudged, unsafe decisions that result in human error and injury may be more likely [9-11]. Although these statements are provided within a broad contextual framework, they support the need to study how technology is perceived among end users in their own environment. Because research about technological impact on worker behaviors has suggested both positive and negative consequences of technology, this issue is worth addressing in the mining industry. Specifically, research has not determined whether CMM operators view their placement choices in the red zone as safe or risky and what effect PDS technology has, if any, on risk perception and subsequent risk behavior."

Jan 1, 2015

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 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 Jon A. Hummer, Aleksandar D. Bugarski, Shawn Vanderslice, Teresa Barone

A study was conducted to examine the potential of diesel emissions control strategies based on retrofitting existing power packages with exhaust aftertreatment devices and repowering with advanced power packages. The retrofit systems, a diesel oxidation catalyst (DOC) and diesel particulate filter (DPF), were evaluated individually using a US EPA tier 2 (ter 2) engine operated under four steady-state conditions and one transient cycle. The DOC effectively curtailed emissions of CO, and to some extent organic carbon (OC), elemental carbon (EC), and aerosol number concentration. The DPF system offered substantially higher reductions in OC and EC mass and aerosol number concentrations. Both, the DOC and DPF achieved reductions in the aforementioned emissions without adversely affecting emissions of NO2 and nano-sized aerosols. The strategy of repowering with an advanced system was examined using a US EPA tier 4 final (tier 4f) engine equipped with a cooled exhaust gas recirculation system and diesel exhaust fluid-based selective catalytic reduction system, but not with a DPF system. The tier 4f engine contributed substantially less than the tier 2 engine to the EC and OC mass, aerosol number, and CO, NO, and NO2 concentrations. The tier 4f engine was very effective in reducing aerosol mass, NO, and NO2 concentrations, but it was not equally effective in reducing aerosol number concentrations. The implementation of viable exhaust after treatment systems and advanced diesel power packages could be instrumental to the underground mining industry to secure a clean, economical, and dependable source of power for mobile equipment.

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 S. Chatterjee, S. Bandopadhyay

A spatial modeling technique based on the Bayesian neural network (BNN) is proposed. Incorporation of the Bayesian method for posterior probability of the output parameter helps calculate the uncertainty associated with the estimate. Parameters of the BNN model were selected using a grid search algorithm. The expected value and the variance of block support were calculated by Markov chain Monte Carlo sampling of the posterior distribution at discretized points within the block. The BNN model was validated by applying it to the Walker Lake data set and comparing the results with ordinary kriging results. The comparison revealed that the proposed BNN method performs marginally better than ordinary kriging. The variance map is less smooth than with ordinary kriging, and the proportional effect is less in the BNN-based model than in the ordinary kriging model. The developed BNN model was applied to the Coho block of a placer gold deposit in Nome, AK. Results reveal that the BNN model is globally biased for the Coho block and underestimates the sample mean. The grade volume curve was developed by calculating the block mean and block variance after discretizing the block into a number of points and applying the BNN model to each discretized point.

Jan 1, 2011

By Wei Victor Liu, Na Zhang, Chengkai Fan, Bei Jiang

The truck haulage data from open-pit mine sites are usually massive and multidimensional with multi-peak Gaussian distributions. Artificial neural networks (ANNs) are well-known machine learning algorithms to handle massive and multidimensional data for building models. Moreover, Gaussian mixture modeling (GMM) is a suitable option for processing the data under multi-peak Gaussian distributions and improving model accuracy. For the first time, this study used a back propagation neural network (BPNN), an extreme learning machine (ELM), and a Bayesian regularized neural network (BRNN) coupled with GMM to deal with the complex truck haulage data and build three weighted ensemble models (WE-BPNN, WE-ELM, and WE-BRNN models) to predict truck productivity at mine sites. Decision tree (DT), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) were used to build models to be compared with the weighted ensemble models. The results showed that the WE-BRNN had a higher accuracy than the WE-BPNN and WE-ELM models. The proposed weighted ensemble models performed better than the benchmark models in predicting truck productivity, indicating that a weighted ensemble approach based on the GMM analysis significantly improved the model accuracy. Based on the relative importance analysis, haul distance was the most crucial input variable for predicting truck productivity. This study provides a new approach to predicting truck productivity, which will help mining companies make sound budget decisions and improve mine planning.

Mar 2, 2023

By G. Kordosky, A. Feather, M. Virnig, P. Crane

Introduction The post World War II nuclear arms race and interest in nuclear power generation resulted in a significant demand for uranium in the 1950’s. This led to the development of uranium solvent extraction technology and uranium became the first metal to be recovered on a large scale by solvent extraction (SX) (1), interestingly more than 100 years after the extraction of uranium from an acid nitrate solution using diethyl ether was first reported by Peligot.(2) For a variety of reasons discussed elsewhere (3,4) there have been upheavals in the supply demand picture and price for uranium since the 1960’s. (Figure 1) The low price of uranium from the mid 1980’s until 2004 resulted in a depression of new resource exploration and mine development along with uranium mine / mill closures and decommissioning. People with skills in uranium processing could not find suitable employment and many experienced engineers and metallurgists took jobs elsewhere or retired. This resulted in a significant brain drain with respect to uranium processing technology.

Jan 1, 2007

By A. Podlesny, E. N. Rubinstein, J. J. Sammarco, B. Demich

Roof bolting typically follows the extraction of a commodity to help keep the roof from collapsing. During 2004 to 2013, roof bolter operators had the highest number of machinery-related injuries, accounting for 64.7 percent, at underground coal mines. This paper analyzes U.S. roof bolter fatal and nonfatal lost-time injury data at underground work locations for all commodities from 2004 through 2013 and determines risk indices for six roof bolting tasks. For fatal and nonfatal incidences combined, the roof bolting tasks in order of the highest to lowest risk index were bolting, handling of materials, setting the temporary roof support (TRS), drilling, tramming, and traversing. For fatalities, the roof bolting tasks in order of the highest to lowest risk index were handling of materials, setting the TRS, bolting, drilling, traversing, and tramming. Age was found to be a significant factor. Severity of injury, indicated by days lost, was found to increase with increasing age as well as with increasing experience, largely due to the confounding of age and experience. The operation of the roof bolting machine used in underground mining should be a research priority given the high frequency and severity of incidents. The results also suggest that temporal factors may exist, so additional research is warranted to better understand these factors and potentially develop interventions. This research provides a datadriven foundation from which future research can be conducted for safety interventions to reduce the frequency and severity of incidences involving the roof bolter activities of bolting, handling of materials, and setting the TRS.

Jan 1, 2016

By Dejan Petrović, Pavle Stojković, Jelena Ivaz, Saša Stojadinović

In spite of the fact that flyrock phenomenon represents the real threat for personnel and machinery, it still remains insufficiently investigated. There are efforts, in the mining community, to explain the causes of flyrock, but the models capable to accurately predict flyrock occurrence and define the flyrock distance still do not exist. This article is an attempt to establish such a predictive model. The model utilized the adaptive nature of artificial neural networks and combined it with the accuracy of numerical modeling of physical phenomena. Necessary data were collected by high-speed camera recordings of actual blasts at three different surface mines and processed for further use for artificial network training. The result was a neuro-numerical couple which was capable to predict flyrock occurrence, estimate the launch velocity of flyrock fragments, and calculate the maximum distance of flyrock fragments. The safe distance was then calculated by multiplying the flyrock distance with a factor of safety. The article explains the procedure for data acquisition and processing, artificial network construction, training, and validation. It also explains the principles of flyrock distance calculation and, finally, provides a definition of factor of safety and safe distance. The results showed that the prediction was satisfactorily accurate and reliable.

Oct 22, 2021

By W. G. Lawrence

The rapid development of science and technology in Russia has been the result of well laid plans since 1918. At that time the Institute of Silicates was established is Moscow and later the establishment of similar more specialized institutes for the study of cement, glass, refractories, construction ceramics, electrical ceramics, building materials and asbestos provided the necessary research facilities for studies related to all types of ceramic inorganic materials. The' Institute of Ceramics and the Institute of Silicate Chemistry, Academy of Sciences, USSR, were founded in Leningrad. The work now going on in these various research' institutes covers every branch of silicate science and ceramics The impetus behind such establishment of special¬ized laboratories is no doubt due to the 5-year plans and the necessity of improving the scientific knowledge regarding the materials being used and the products being manufactured.

Jan 1, 1960

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 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 Pratt Rogers, Aaron Young

Digital transformation (DT) is the process by which entities adapt themselves to modern technology. As digital technology becomes more prevalent (automation, cameras, sensors, touchscreens, artificial intelligence, etc.), there will be increased pressure on companies to leverage it for additional gains. For many companies in the mining industry, this will involve overcoming a steep learning curve. Furthermore, as the long-term success of the mining industry is dependent on a labor force with new skills (data management, analytics, digital literacy, etc.), new curriculum will need to be appended to the mining engineering discipline. This paper considers the mining industry’s need for DT. Then, it presents a three-part review of the foundational components of the DT process–ubiquitous data, connectivity, and decision making. Finally, it 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 Pratima Meshram Abhilash

In this review, availability of germanium in primary and secondary resources and its recovery from these resources are presented. With nearly 40% germanium consumed in fiber optics and scarcity of resources, the global consumption outplays the primary production. Germanium exists as a substituted element in zinc sulfide matrix of lead–zinc sulfides and lignites. Nearly 60% of the germanium supply is met by zinc refining residues, and about 20–30% of germanium is produced from coal and its by-products. Germanium recovery from various resources such as zinc plant residues, coal and coal plant waste, spent optical fibers, and copper flue dust has been examined. Bioleaching was exploited by few researchers to extract germanium from zinc sulfides, sulfide tailings, jarosite, coal fly ash, and flue dust. This review covers the various work carried out by different chemical separation processes, viz., chemical precipitation, solvent extraction (SX), ion exchange (IX), and membrane processes, as well as biological approaches.

Jan 16, 2022

By Jian Wang, Kun Li, Hao Wu, Juan Nan

In this paper, in order to solve the problems of low accuracy and slow speed of fire flame recognition, an extreme learning machine (ELM) method based on improved Harris hawks optimization (IHHO) is proposed for fire flame recognition. A novel Harris hawks optimization (HHO) is used to solve the problem of parameter selection of ELM. In order to solve the problem that the original HHO is prone to fall into local optimum, firstly circle mapping is used to initialize the population to solve the problem of uneven distribution and small range of the initial population. Then the formula of escaping energy of HHO is modified to make the population have a large range in the middle and late iterations while gradually decreasing in the whole iteration process. Thus, the exploitation stage in HHO is improved to make the search range smaller near the optimal solution to accelerate the convergence process. Finally, at the end of each iteration, a certain number of individuals are selected to perform Gaussian and Cauchy hybrid mutation to prevent the IHHO from falling into local optimization. Through three groups of experiments, the effectiveness of the proposed IHHO and IHHO-ELM is verified. In experiment 1, the convergence performance of IHHO is significantly better than that of the other remaining algorithms in the test results of six benchmark functions. In experiments 2 and 3 of real fire flame recognition case, IHHO-ELM outperforms other remaining algorithms on the whole and has significant advantages in some indexes.

Jan 17, 2023

By T. G. Rozgonyi

The basic goal of this investigation was to determine the degree of drying of high moisture lignite through the use of high temperature and pressure steam autoclaves. Specific items studied were: a) the effect of lignite particle size, b) steam temperature and pressure required, and c) the time of ahydration. Although removal of sodium, ash, sulfur and other ash-forming minerals was not the goal of this study, basic analysis of effluent water showed organic material and alkalai metal content. Removal of inherent moisture from run-of-mine lignite was tested by lock-feeding the lignite, which was previously sized by screening into 25 kg autoclaves. High pressure, up to 4.053 MPa (588 psi), saturated steam was then introduced into the pressure reactor. Reaction time, pressure, temperature, and depressurization rates were strictly controlled for each reactor test run. Results indicate that about 80% of lignite total moisture was removed, including some of the inherent moisture, yielding a non-oxidized, and thus stable, lignite product. As stated, a side benefit occurred as some reduction of non-combustible portions of the lignite also took place. The conclusion reached as a result of the tests is that the steam-drying process can be successfully applied to reducing moisture in lignite, and may be considered for additional studies on acceleration of the coalification process. On the basis of the data presented, the author believes that lignite can be economically upgraded in steam autoclaves by simultaneously reducing the moisture content by 80 percent and the sulfur content by at least 50 percent. Upgrading the lignite by this method will result in large energy and financial savings.

Jan 1, 1984