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By J. Weyer

"A lot of incorrect, dangerous and misleading statements about the effect of CO2 on the human body can still be found in the internet, the media and in books. It is a fact that already low concentrations can lead to critical reactions, higher concentrations cause a narcosis. The real effect is not a lack of oxygen but an inner suffocation. The breathing reflex is decreased and the transport of oxygen in the blood is reduced due to a low pH value of the blood. Furthermore a reduced oxygen content in the air can also lead to the failure of diesel engines. MOTIVE Different gases can appear in active mines as well in abandoned mines, adits and caves. The most dangerous gases are CO, NO/NO2, H2S, SO2, CH4 and CO2. Although the general danger arising from these gases is mostly known some aspects are underestimated. Furthermore misleading or wrong statements exist about the detection and the effect of gases on the human body. This is especially true for CO2. In the last years a couple of accidents happened with a great number of injured persons and unfortunately also some fatalities. As a result wrong information and comments about the effect of CO2 have been published in the media. A huge unexpected CO2 outburst in a potash mine showed again some additional problems and limits for rescue troops. ORIGIN OF CO2 AND MISLEADING STATEMENTS ABOUT THE EFFECT ON THE HUMAN BODYCarbon dioxide can appear as free gas in the earth crust. It is especially known in significant concentrations in some potash deposits, in hard coal mines and some caves. Furthermore it can be formed due to complete oxidation of carbon. It can appear in technical processes. It is also used for or in technical processes, as fire extinguishing agent, as cooling agent, it is contained (added) in sparkling water or other soft drinks and it is formed by fermentation processes. Up to today a lot of wrong information about the effect of CO2 on the human body is published or is still in the mind of many people and unfortunately even in the mind of members or leaders of rescue troops. On August 16th 2008 a faulty CO2 extinguishing installation in the city of Mönchengladbach/Germany caused the injury of 107 people and the evacuation of further 150 people /1/. People on the streets became unconsciousness and felt down. Fortunately all could be rescued due to the immediate rescue operation of local fire fighters. Furthermore the CO2 concentration on the ground was diminished by help of a helicopter. What could have happen with the engine will be discussed later. After 6 days a second similar accident took place /2/. In the news members of the rescue teams explained that CO2 leads to suffocation due to a lack of oxygen. This is wrong! Also cave explorers often say that a burning candle can detect a low oxygen and/or high Carbon dioxide content in the air. This is also wrong and deadly dangerous! Similar statements we find nowadays in the internet /3, 4 and many other/. In a forum “science and mathematics > chemistry” several people say that winegrowers go with a candle into the vine-cellar to detect CO2 /5/. Again: this can be deadly! Another dangerous statement is that CO2 in higher concentrations leads to headaches so people should leave such areas. A last example found in the literature is that at 3 % the breathing volume is doubled and at a concentration of 20 % one can felt some warmth on the skin. Both is not wrong but can lead to false conclusions. /6/"

Jan 1, 2015

By M. Hepokoski, M. Klein

"Mine refuge shelters are designed to protect mine workers from hazardous environmental conditions after a mine disaster, but high temperature and humidity levels inside these shelters may pose a significant safety risk. Due to the safety risks associated with human testing, detailed thermal models of shelters and human occupants have been developed using TAITherm simulation software. These models are used to predict the temperature and humidity within the shelter resulting from metabolic heating, evaporation due to sweat and respiration, moisture and heat from the shelter’s carbon dioxide scrubbing system, and heat transfer within the shelter. A detailed human thermoregulation model simulates the sensible and latent heat generated by the shelter occupants, and predicts the occupant’s body core temperature and moisture lost due to sweating and respiration. This information is used to assess the safety of the shelter. In this paper we will review technical details of the human thermal model, validation comparing the human model to human subject test data, and results of a mine shelter analysis. INTRODUCTION Portable mine refuge chambers are used in US coal mines to provide protection to mine workers from hazardous conditions following a traumatic event. Although these shelters are designed to supply clean air and sustenance for up to 96 hours, prolonged exposure to the mine shelter environment itself can pose a substantial risk to human safety. Heat generated by the shelter occupants combined with heat from the carbon dioxide (CO2) scrubbing system can raise the shelter interior temperatures and relative humidity to dangerous levels that can lead to heat stress in humans. Unfortunately, the thermal burden associated with mine shelter exposure is not well characterized due to potential hazards associated with underground human subject testing. In order to assess the thermal burden on people within a mine shelter environment, a detailed thermal model was created to accurately predict the temperatures and humidity within the shelter, as well as the impact of these environmental conditions on human occupants. The model consists of a mobile inflatable tent-type mine shelter, 23 humans, and also includes the surrounding mine environment. The thermal simulations were performed using ThermoAnalytics' validated commercial heat transfer prediction code, TAITherm (v12.0). Additionally, TAITherm's Human Thermal Module (HTM) provided the capability to simultaneously model the thermo-physiology of 23 unique humans contained within the mine shelter. The human thermal models accounted for the thermal interaction with the environment and also provided a core temperature response prediction, which can be used to evaluate survivability in heat stress situations [1]."

Jan 1, 2016

Australia is a hub for mining technology development. The paper describes many of the key players involved in mining research in this country along with some of the interesting research outputs to emerge over the past decade.

Jan 1, 2010

By Ehsan Moosavi, Kamyar Tolouei, Mehran Gholinejad

The literature defines rockbursts in slightly different ways based on their generation mechanisms, field phenomena, and degree of damage, but in a way that is almost consistent. Generally, a rockburst refers to seismic events that mainly happen in underground excavations, for purposes such as underground mining, road and railway tunnels, nuclear power, etc., and that are followed by the collapse and outburst of rocks. Long-term rockburst prediction can be abstracted as a classification problem mathematically, in which we can introduce deep learning to solve it. Further, using deep learning in rockburst prediction can overcome drawbacks such as subjectivity and inconsistency brought from previous traditional approaches. A rockburst data-set, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the proposed algorithms can speed up parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.

Apr 26, 2022

By Jupneek Singh, Zachary A. Kiehl, Leila Safazadeh, Gareth Kennedy, Eleonora Widzyk-Capehart, Jeremy W. Ward

Mine workers are continuously exposed to a host of nonfatal stressors and potentially fatal hazards: noise exposure, excessive vibration, poor air quality, toxicant exposure, ignition of combustible gases, equipment-related accidents, and thermal heat stress. Wearable or portable sensors and contextual analytics provide a platform to unobtrusively collect and fuse multimodal health and safety data in real time to compute both acute safety and longitudinal health insights. This paper presents preliminary trial results, conducted by Simtars, VigiLife, Inc., and Queensland Mines Rescue Service, of the application of wearable sensors to monitor heat stress and other vital biometrics for mines rescue personnel.

Feb 1, 2024

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 BHAMIDIPATI S. SASTRY, ANMAYA NAIK, ASIF AHMAD, Srivatsan Jayaraman Sridharan, ADITYA PANDEY

Wet kata cooling power (WKCP) as measured by a kata thermometer is a well-known heat stress index used in the mining industry. Although, over the years, several researchers proposed different heat indices and indicated limitations of the kata thermometer, WKCP is used in the South African gold mining industry due to its simplicity and rapidity of assessment. This paper presents a comparative analysis of the newly developed empirical expression for the WKCP and some of the more commonly used empirical models. For this purpose, WKCP measurements are obtained based on experiments conducted in a low-speed wind tunnel. Based on a regression technique, the analysis presented aims to assess the disagreement between the empirical model predictions and actual measurements. The correlation is also presented between WKCP and the well-established thermoregulation models using parameters from the low-speed wind tunnel experiments, including dry-bulb temperature (Td), wet-bulb temperature (Tw), barometric pressure (P), and wind speed (v). It is found that the proposed empirical model for the WKCP represents the bioclimatic interaction quite well under the specific parameters that defines the harsh thermal environment and air condition, such as metabolic activity, clothing of the person, and the velocity of the ventilating air in which the person is working. The statistical analyses performed show that the empirical model proposed for the WKCP produces satisfactory estimates of the thermal stress experienced under lower air velocities, light clothing, and higher metabolic activity rates.

Sep 2, 2021

By S. E. Vanderslice, J. A. Hummer

"The results of laboratory evaluations were used to compare the potential of two alternative, biomass-derived fuels as a control strategy to reduce the exposure of underground miners to aerosols and gases emitted by diesel-powered equipment. The effects of fatty acid methyl ester (FAME) biodiesel and hydrotreated vegetable oil renewable diesel (HVORD) on criteria aerosol and gaseous emissions from an older-technology, naturally aspirated, mechanically controlled engine equipped with a diesel oxidation catalytic converter were compared with those of widely used petroleum-derived, ultralow-sulfur diesels (ULSDs). The emissions were characterized for four selected steady-state conditions. When fueled with FAME biodiesel and HVORD, the engine emitted less aerosols by total particulate mass, total carbon mass, elemental carbon mass and total number than when it was fueled with ULSDs. Compared with ULSDs, FAME biodiesel and HVORD produced aerosols that were characterized by single modal distributions, smaller count median diameters, and lower total and peak concentrations. For the majority of test cases, FAME biodiesel and HVORD favorably affected nitric oxide (NO) and adversely affected nitrogen dioxide (NO2) generation. Therefore, the use of these alternative fuels appears to be a viable tool for the underground mining industry to address the issues related to emissions from diesel engines, and to transition toward more universal solutions provided by advanced engines with integrated exhaust aftertreatment technologies. IntroductionDiesel engines have been the workhorses of modern society for industrial and transportation enterprises. Diesel engines of all generations are extensively used in a variety of light-, medium- and heavy-duty applications in underground mining operations around the world. Fuels obtained from various sources and with wide-ranging chemistries have been combusted in diesel engines. However, the bulk of the currently used fuel can still be traced to nonrenewable petroleum sources. The petroleum-derived diesel fuels are primarily made up of saturated acyclic hydrocarbons (parafins or alkanes), aromatic hydrocarbons (arenes or aryl hydrocarbon), and unsaturated hydrocarbons with double bonds (olefins and alkenes). Improvements in the quality of petroleum-based diesel fuels was identified in federal regulations as an important piece in resolving the puzzle of diesel engine emissions (Environmental Protection Agency, 2001, 2004). The reduction of U.S. diesel fuel sulfur content below 15 ppm had the effect of lowering sulfate emissions and enabling the development and implementation of catalyzed emissions control technology across a wide spectrum of applications."

Jan 12, 2017

By F. Taylor Ostrander

Some countries are infinitely poorer than others in mineral resources-despite the perfect equality of their political leaders ambitions. Compare the mineral poverty of Kenya and Tanganyika with the riches of Katanga Province and Northern Rhodesia. Compare Paraguay with Chile, There never has been any equality in the distribution of nature's wealth. Minerals cannot play any role at all in economic development where they don't exist, and there are bound to be differential rates of economic development where there are basic differences in the wealth of mineral resources available. Africa is my principal area of interest and I know that there is an unfortunate but widespread misconception about Africa's "vast mineral resources, "its "untapped mineral wealth." Why is it untapped? Why is it undeveloped?

Jan 1, 1963

By A. Jahedsaravani, M. Massinaei, M. Zarie

Deep learning is a subset of machine learning that uses artificial neural networks for extracting high-level features from image data. In the present study, a soft sensor is proposed for the prediction of the flotation performance through froth features generated by the use of pre-trained convolutional neural networks. Several state-of-the-art convolutional neural networks (AlexNet, GoogLeNet, VGGNet, ResNet, and SqueezeNet) pre-trained on the ImageNet database are used to predict the metallurgical performance of two flotation systems. The first case study is a batch copper flotation system video-captured over a wide range of process conditions. The second case study is an industrial coal flotation column equipped with a continuous video recording system. The pre-trained networks are used to extract features from the froth images, and these features are subsequently used to predict the flotation conditions and performance. The prediction results by the pre-trained algorithms were compared with the traditional image processing algorithms. This demonstrates the ability of the pre-trained structures to generalize to images outside the ImageNet database. GoogLeNet outperforms other network architectures and provides more accurate predictions of the flotation process behavior and performance.

May 5, 2023

By M. Sunkpal, K. C. Kocsis

"An underground environment with high air temperature and humidity conditions generated from various heat and moisture sources can significantly affect the thermoregulation processes of the human body. This can lead to a series of heat-related illnesses such as heat exhaustion, heat cramps and heat stroke. The majority of the metabolic energy of a mine worker results in the production of heat. If the human body is to maintain thermal equilibrium, then this metabolic heat must be transferred to the ambient surroundings at the same rate. The heat loss from the human body occurs through a combination of heat transfer processes, such as respiratory heat exchange, convection, radiation and evaporation. If the heat loss from the human body to the ambient surroundings is less than the metabolic heat produced, the excess will be accumulated within the human body, which will result in a rise in the core and skin temperatures. This can ultimately lead to a heat-related illness or a combination of heat-related illnesses. This paper aims to discuss major heat-related issues and provide an overview of various mine ventilation and cooling systems, which can be employed to overcome high levels of heat and humidity in underground mines. In this paper, the effects of heat exposure on the health, safety and productivity of the mine workers are also highlighted. IntroductionMining in the United States remains one of the most hazardous industries, despite significant reductions in fatal injuries over the last century (Coleman and Kerkering, 2007; Saleh and Cummings 2011; Jacklitsch, Musolin and Kim, 2016). Occupational health hazards within the mining industry include hazards that are physical, such as traumatic injury or hearing loss; chemical, such as silica or diesel particulate matter; biological; ergonomic; and psychological (Donoghue, 2004; Saleh and Cummings, 2011). The effects of some of these hazards can be diagnosed immediately, while many, like black-lung and silicosis, will have long-term effects on the health, safety and life quality of mine workers (Donoghue, 2004; National Institute for Occupational Safety and Health, NIOSH, 2000). Among these hazards, exposure to heat and humidity can significantly affect the safety, health and productivity of mine workers, particularly in deep and hot underground mines (Roghanchi, Sunkpal and Kocsis, 2015). Short-term acute heat exposures can cause a rise in the core temperature of the human body, which can result in a heat-related illness or combination of heat-related illnesses. Adverse long-term chronic heat exposure can generate serious occupational heat-related illnesses, such as cardiovascular diseases, mental health problems and chronic kidney diseases. High core body temperatures coupled with dehydration can generate negative behavioral effects such as physical fatigue, irritability, lethargy, impaired judgment, loss of dexterity and loss of concentration (Xiang et al., 2014)"

Jan 9, 2017

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 JESSICA W. A. AZURE, Kansake A. Bruno, Samuel Frimpong, AZUPURI G. A. KABA, FORSYTH A. KADINGDI, PROSPER E. A. AYAWAH

Blasting generates undesirable ground vibration with adverse effects on humans, the environment, and structures. Numerous studies have used various techniques including machine learning (ML) to predict the peak particle velocity (PPV) caused by blasting in surface mines. These studies used the validation-set approach to evaluate the performance of their predictive models. In this study, Random Forest (RF), Gaussian Process (GP), and Gradient Boosting Machine (GBM) were applied for the prediction of PPV using data from a large open-pit gold mine in Ghana. An ensemble model based on a stacked generalization approach was then built on these three base models to significantly improve the PPV prediction performance. The model evaluation was done using repeated tenfold cross-validation which resolves the challenges with the validationset approach for small datasets. The dataset used in this study comprised a total of 196 measurements taken from different blasting operations. R-squared ( R2 ), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the model performance. The results obtained from these models revealed that all the base models did not significantly outperform each other. However, the stacked generalization approach had a better performance, with RMSE = 0.12, R2 = 0.96, and MAE = 0.07. This approach presented an improvement over the existing methods and will be useful for highly accurate predictions of ground vibration during blast design. The application of this model will be useful for efficient and effective blast design.

Nov 8, 2022

By Turker Hudaverdi, Yasar Agan

This study tries to predict blast-induced flyrock using intelligent modelling techniques. Flyrock is a site-specific phenomenon. Design variables and rock properties highly influence flyrock throw distance. Site investigations were conducted in a sandstone quarry. Fundamental operational parameters, blast dimensions, bench face condition, and rock mass structure were monitored. Stepwise regression technique was applied for variable selection. Burden–hole diameter ratio, in situ block size, and powder factor were determined as the most significant parameters. Considering output of stepwise regression, artificial neural network, ANFIS, and Gaussian process regression techniques were applied to predict flyrock throw. A comprehensive validation was performed using twelve different performance indices. Pre-determination of input parameters supported development of successful soft computing applications. Rock block size was found to be an appropriate input variable for flyrock modelling. In addition to classical performance indices, standardized and symmetric accuracy metrics were quite useful in model validation process. The intelligent models predict flyrock range with an error less than 6 m. The mean percentage errors are lower than 10%. ANFIS seems to be the best model for flyrock prediction. The calculated mean absolute error is 5.36 for ANFIS model.

Nov 12, 2023

By Aleksandar D. Bugarski, Dylan A. Ritter

The results of laboratory characterization of tailpipe emissions for three “clean” engines that meet U.S. EPA Tier 4 final emissions standards were used to assess the viability and effectiveness of repowering existing engines and powering new mobile equipment with those engines as a control strategy for reducing exposure of underground miners to diesel aerosol and criteria gases. The evaluated engines were representative of those that achieve the emission standards through implementation of various in-cylinder emissions control strategies, use of crankcase filtration, and use of three types of exhaust aftertreatment systems: (1) diesel oxidation catalytic converter (DOC), (2) combination of DOC and the full-flow wall flow monolith diesel particulate filter (DPF), or (3) combination of DOC, diesel exhaust fluid (DEF) based selective catalytic reduction (SCR) system, and ammonia slip catalyst (ASC). The study showed that the highest reductions in concentrations of diesel aerosols in underground workings, in terms of both mass and number, could be achieved if the engines, preferably in all power classes, are fitted with viable DPF systems. The use of U.S. EPA Tier 4 final engines equipped with DOC and DOC/SCR/ASC systems could help operators to considerably reduce mass, but not number concentrations of aerosols. The emissions of two of the evaluated engines, one equipped with DOC and the other equipped with DOC/DPF systems, were characterized by substantial secondary NO2 emissions that would limit the viability of those engines for underground mining applications. It appears that the catalyst formulations used in the exhaust aftertreatment systems of the “clean” engines marketed to the underground mining industry need to be formulated to minimize the potential for generation of secondary NO2 emissions. The results indicate that the engines fitted with viable SCR/ASC systems present a low-NO2 alternative. All three “clean” engines were found to have low CO output. Therefore, due to nuances associated with the use of diesel-powered mobile equipment in underground mines, special attention needs to be paid to the selection and potentially optimization of “clean” engines for underground mining applications.

Feb 1, 2025

By Yao Yevenyo Ziggah, Victor Amoako Temeng, Solomon Evans Kweku Koomson

Tyre hours/life deficit is a major operational challenge facing the mining industry which adversely affects materials production and costs. An accurate forecast of the tyre life is key in addressing this menace. This study for the first time employed the hybrid intelligent technique by utilising three metaheuristic optimisation algorithms, including particle swarm optimisation (PSO), genetic algorithm (GA), and whale optimisation algorithm (WOA), as trainers for the parametric weights and biases to optimise multilayer perceptron neural network (MLPNN) for enhancing prediction of on-site dump truck tyre life in the mine. Four hybrid models known as PSO-MLPNN, WOA-MLPNN, GA-MLPNN, and BP-MLPNN were developed using a total of 157 tyre dataset records obtained from a surface mine in Ghana. In assessing the prediction performances for the models developed, five statistical performance metrics of variance accounted for (VAF), Nash–Sutcliffe efficiency index (NASH), coefficient of determination (r2), mean absolute percentage error (MAPE), and correlation coefficient (r) were utilised. Moreover, ranking, uncertainty analysis and Bayesian information criterion (BIC) techniques were utilised to establish the most effective hybrid model, whereas sensitivity analysis was conducted on the input parameters. Results achieved showed that PSO-MLPNN was the best for prediction because it had the least MAPE value of 1.196% and relatively high values of VAF (99.642%), NASH (0.996), r2 (0.996), and r (0.998). Besides, PSO-MLPNN had the best selection criteria values of 6, 7.1725, and 444.834 for the ranking, uncertainty analysis and BIC respectively. Hence, PSO-MLPNN is recommended for the prediction of on-site dump truck tyre life for the studied mine.

Mar 11, 2024

"Editor’s note: Each year, Mining Engineering features an industrial minerals review. Several people put in a fair amount of time in developing the material for this issue, all the while doing their own jobs. Thank you to the industrial minerals annual review editor, to the Industrial Minerals and Aggregates Division technical committee chair and vice chairs, and to the authors of the individual commodity profiles.Jim Norman, member SME, of Tetra Tech, is the industrial minerals annual review editor.Vishal Gupta, member SME, of EP Minerals LLC, is chair of the Industrial Minerals &Aggregates Division’s Technical Committee.The technical committee vice chairs include:• Vishal Gupta, member SME, of EP Minerals LLC, Agricultural Materials.• Joe Garska, member SME, of Imerys, ceramics and refractory materials.• Stanley Krukowski, member SME, of the Oklahoma Geological Survey, chemical raw materials.• Russell Winn, member SME, of R.E. Janes Gravel Co., chemical raw materials.• Justin Zdunczyk, member SME, of Pike Industries, construction aggregates.• Jerry Gauntt, member SME, of Golder Associates, fillers and pigments.• Ebrhaim K. Tarshizi, member SME, of the Michigan Tech, sorbents, filters and process aids.• Daniel T. Eyde, member SME, of St. Cloud Mining, specialty minerals.Their help makes possible this July industrial minerals issue. On behalf of ME readers, the editors thank them.Virta, R.L. U.S. Geological Survey, National Minerals Information CenterFour companies - H.C. Spinks Clay Co., Inc., Imerys, Old Hickory Clay Co., and Unimin Corp. - mined ball clay in four states in 2013. On the basis of preliminary data, production was 1 Mt (1.1 million st) with an estimated value of $47 million. Production increased by 3 percent in tonnage from 973 kt (1.1 million st) with a value of $45.1 million in 2012. Tennessee was the leading producer state accounting for 64 percent of domestic production, followed by Texas, Mississippi and Kentucky. About 67 percent of total ball clay production was airfloat, 22 percent was crude or shredded and 11 percent was water-slurried.ConsumptionIn 2013, domestic ball clay producers sold clays to the following markets: ceramic floor and wall tile (44 percent); exports (21 percent); sanitaryware (18 percent); miscellaneous ceramics (9 percent); fillers, extenders, and binders, and unspecified uses (4 percent each), based on 2012 end use patterns and current markets. Other markets accounted for less than 1 percent of the remaining ball clay sales or use. Sales reported for the manufacture of fiberglass or most filler, extender, and binder applications were likely to have been mainly kaolin mined or purchased by the ball clay producers.PricesThe average value of domestically produced ball clay was approximately $47/t ($43/st) in 2013 compared with $46/t ($42/st) in 2012, on the basis of a preliminary survey of domestic ball clay producers. The unit values of exported and imported ball clay were $126/t ($114/ st) and $373/t ($338/st), respectively, in 2013, compared with $62/t ($56/st) and $314/t ($285/st), respectively, in 2012. Unit values for most large export shipments increased in 2013, and a larger number of low-tonnage, high-value export shipments were shipped in 2013 than in 2012, resulting in a doubling of the average export value. Two low-tonnage, high-value shipments in 2013 accounted for the increased value of imports."

Jan 1, 2014

By John S. Newton

Modern high capacity underground mining machinery is a complex of mechanical, electrical and hydraulic components which have greatly increased capability over similar components of mining machinery of ten and. even five years ago. Looking into the future I feel certain in stating that there will be changes equally great in the coming five and ten year periods. With this premise in mind, I should like to bring to your attention some of the things that do and will have a continued major impact on the industry, particularly as they relate to the maintenance and operation of these machines of increasing capability. In underground mining today, nearly every individual plays a key role. A machine operator's importance in his work is comparable with that of the pilot of an airplane, a railroad engineman, a bus driver, a skilled machinist, a bulldozer operator and many others whose assignment is one of doing, by himself, with his hands and his brains all that the specially designed machine has been created to do. These machine operators are supported by a staff of skilled engineers, maintenance personnel, supervision, etc., but objectives are realized only when the operator maintains his schedule. What is being done and what can be done to make it easier for the operator to maintain his schedule which, as with the airplane, is constantly being improved? This question can be answered best, perhaps, by dividing the subject into four general categories - (A) Power Supply. (B) Control and Protective Devices. (C) Hydraulic Component Design. (D) Mechanical Design.

Jan 1, 1962

By Peter K. Achireko, Samuel Frimpong

Design and optimization of open pit limits are of paramount importance because they provide information for evaluating the economic potential of a mineral deposit and for developing short and long range mine plans. Many algorithms and their modifications have been used to design and optimize open pit limits. These algorithms have provided mine planning engineers with pertinent information in designing, optimizing and extracting ore reserves by open pit technology. However, they do not address the random field properties associated with the ore grades and reserves and commodity prices, and thus, fail to yield the truly optimized pit limits in any time horizon. In this paper, a new algorithm, MCSIMFNN, which overcomes these limitations is proposed and used to optimize open pit limits. The random field properties of the ore grade and reserves have been modelled using the modified conditional simulation based on the best linear unbiased estimation and local average subdivision techniques. Artificial neural networks are used to classify the blocks into classes based on their conditioned values. The error back propagation algorithm, in the neural networks, is used to optimize the pit limits by minimizing the desired and actual outputs error in a multi- layer perceptron under the pit wall slope constraints. The optimized pit value obtained by this algorithm has been compared to that from the Lerchs-Grossman's algorithm in a case study. The results are the same, but in random multivariable states, the MCSI MFNN algorithm is the most suitable for pit optimization. The method is also fast since it mimics the operation of the brain in the solution of the pit optimization problem.

Jan 1, 1996

By James Boyd

JAMES BOYD 29.1-STAFF-AND-LINE ORGANIZATION Industrial organizations are established primarily to coordinate the activities of many individuals working toward a specific goal. In mining, the goals can vary widely, and the skills necessary to reach them likewise will vary from operation to operation. It is essential to keep in mind the principal point that an organization embodies the necessary skills which supplement each other and achieve a given objective. Those who direct such an organization are grouped together as "management" and accept the responsibilities for the regulation, administration, supervision and guidance of the enterprise. Those who comprise the "administration" and have supervisory authority are referred to as the "executives." In the early days of mining it was common for one executive to exercise virtually complete control over the entire organization, and direct its operation from a central office. However, the science of management has advanced significantly since then. Mining organization administration now allocates increased authority and flexibility to those in direct-line responsibility, and organizes competent people in specialized fields to provide supervisors with guidance where it is needed in day-to-day operations. Few mining companies today are confined to one operation. Therefore, highly developed skills can be provided in the staff which assists management. This is known as staff-and-line organization, illustrated, in general, in Fig. 29-1. Each operation requires diverse skills for the carrying out of specific functions. The acquisition and training of those with such skills is an important operation in assembling organizations. An organization normally is modified to accommodate individuals rather than being a "hard and fast" mold into which people are fitted. The modern manager is inclined to organize to minimize central direction, with delegation to line executives, providing staff whose day-to-day operations, in cooperation with line executives, coordinate a continuing drive toward the organization's objectives. The staff officers, therefore, tend to become what could be described as parts of the president's brain, dealing with specific areas requiring certain skills. They do not necessarily direct operations, but watch them to be sure that the skills which they have themselves developed can be transmitted to line operations by persuasion rather than by direct authority. The elements of management requiring staff assignments are: 1. Planning, which, at staff level, is only a coordinating activity. Planning is done at all levels in the organization. 2. Engineering. 3. Finance. 4. Control. 5. Personnel and industrial relations, which set personnel policies, including salary plans and the means of handling relations with the unions. 6. Research, which, in a modern organization, is essential to progress and needs to be overseen at the top. 7. Legal staff to see that all operations of the company are provided with sound legal advice, and that the company's policies stay within the law.

Jan 1, 1973