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By A. D. Davis

Naturally occurring arsenic has been detected in water from inactive mines and several wells in the Keystone area of the Black Hills. Keystone City Well #4 showed a total arsenic concentration of 69 parts per billion (ppb) during pumping in 2004. The well is not used currently except for arsenic-removal research. Water also can be pumped from a mine shaft in the abandoned Keystone ? Columbia ? Bullion gold mines, whose interconnected workings flooded after mining ceased in the 1930s. This water has been considered for local supplies, although it would require treatment or blending because of arsenic concentrations. According to historical records, the Bullion Mine produced 100,000 pounds of white arsenic. An aquifer test on the mine workings was performed at a pumping rate of about 550 gallons per minute. The arsenic concentration in pumped water was 33 ppb. In addition to wells within Keystone, samples from the water supply at nearby Mount Rushmore National Memorial have shown arsenic concentrations greater than 10 ppb, the U.S. Environmental Protection Agency?s maximum contaminant level for arsenic in drinking water. Limestone-based technology for removal of arsenic from drinking water is an innovative and promising method that has been considered for treatment of water at Keystone. In batch testing with water from Keystone Well #4, limestone removed about 65% of arsenic from solution. A related problem of arsenic removal from drinking water involves disposal of the waste product, which is critical for commercial viability of removal technologies. Current research is investigating thermal stability of the waste in order to determine the potential for recycling of the material during manufacture of cement.

Jan 1, 2010

Arsenic (As) removal behavior from Changgoon mine tailings was investigated using Acidithiobacillus ferrooxidans (A. ferrooxidans) in well-controlled batch reactors by varying pulp density (0.5?4%, pH=1.8, temp=30oC), reaction initial pH (1.8?2.2, pulp density=0.5%, temp=30oC), and temperature (25?35oC, pulp density=0.5%, pH=1.8). The initial concentration of As in the mine tailings used for bioleaching tests was about 30000 ppm, and the size range of tailings used in this study was 63?74 µm. The reaction speed and initial cell concentration were fixed to 150 rpm and 107 cells/mL, respectively. To complement leaching experiments, pH, oxidation/reduction potential (ORP), and amount of ferrous/ferric ions in leachate were analyzed. Scanning electron microscopy and x-ray diffraction analyses were also conducted for the samples before and after leaching to see if any morphological and compositional changes in tailings took place during leaching process. The As leaching efficiency increased with decreasing pulp density, decreasing pH, and increasing temperature in the presence of A. ferrooxidans. Specifically, no significant change in As removal rate was observed when pulp density d 2% while the removal rate dramatically decreased at pulp density of 4%. Regarding pH effect, As removal rate was much more enhanced at lower pHs (i.e., 1.8 and 2.0) compared to pH 2.2. Increasing temperature was observed to tend to enhance As removal rate, showing about 20% enhanced leaching rate under 35oC compared to 25oC and 30oC at the elapsed time of 5 days. The results for As removal behavior at different conditions (i.e., pH, temperature, pulp density) were consistent with the change in pH, ORP, and ferric ion concentration in leachate, showing greater removal rate at the condition with lower pH, and higher ORP and ferric ion concentration in leachate. Furthermore, decreasing removal efficiency was likely in part due to the extent of the formation of by-product (i.e., jarosite) of ferric ions.

Feb 27, 2013

By A. E. Isaacson, T. H. Jeffers

The U.S. Bureau of Mines (USBM) is investigating arsenic removal from dilute waters using a biological precipitation/chemical adsorption system. The method includes (1) inoculating water with ferrous salts to increase hydrogen sulfide generation by indigenous sulfate-reducing bacteria, (2) utilizing the hydrogen sulfide to precipitate the bulk of the arsenic as arsenic sulfide, and (3) using beads containing ferric oxyhydroxide to remove the remaining arsenic. This approach was applied to a contaminated water containing 13 mg/L As. Arsenic extractions up to 99% were achieved. The use of sulfate-reducing bacteria is particularly warranted in this situation since most of the required nutrients are present in the water. Based on successful initial efforts, planning is underway to conduct a pilot-scale test at the site.

Jan 1, 1995

By A. D. Davis, D. J. Dixon, J. L. Sorenson, C. J. Webb, S. Dawadi

Limestone-based material appears to be an effective arsenic removal process that has great potential for source reduction in drinking water. Limestone-based material offers several benefits to the drinking water community: 1) reasonable removal efficiency as compared to material cost, 2) broad geographic and water system applicability, 3) compatibility with other water treatment processes, 4) ease of technical use, and 5) low-cost disposal in ordinary landfills. Batch and column experiments have evaluated the removal efficiency of several limestone types. Additives and material alteration have increased the removal efficiency. Granulation of powdered limestone as a filter media currently is being investigated.

Jan 1, 2006

By R. N. Tempel, M. Lengke

Arsenic sulfide (orpiment, realgar, amorphous AsS and As2S3) oxidation rates were measured in mixed flow reactors at pH-2 and at temperatures of 25 ° to 30 °C. The measured oxidation rates for arsenic sulfide solids by dissolved oxygen are in the range of 10-11 to 10-10 mole m-2s-1 after being normalized to final surface area. The order of arsenic sulfide oxidation rates decreases at pH-2 from amorphous AsS > realgar > orpiment > amorphous As2S3. Compared to pyrite, arsenic sulfide oxidation rates are approximately 1.5 to 15 times slower than pyrite oxidation rates at pH-2.

Jan 1, 2003

By R. N. Tempel, M. Lengke

Arsenic sulfide [orpiment, realgar, amorphous AsS and As2S3] oxidation rates were measured in mixed flow reactors at pH~2 and temperature of 25-30oC. The measured oxidation rates for arsenic sulfide solids by dissolved oxygen are in the range of 10-11 to 10-10 mole m-2 s-1 after being normalized to final surface area. The order of arsenic sulfide oxidation rates decreases at pH~2 from amorphous AsS > realgar > orpiment > amorphous As2S3. In comparison to pyrite, arsenic sulfide oxidation rates are approximately 1.5 to 15 times slower than pyrite oxidation rates at pH~2.

Jan 1, 2002

By W. Hopkin

INTRODUCTION Necessary science and technology are not well established for the design of responsible treatment and disposal of arsenical residues from refractory gold ores. Specifically, there is uncertainty about the precise composition and behaviour of oxidised arsenic species in these wastes, and an analysis of possible chemical behaviour suggests that potentially difficult control and disposal problems remain to be so 1 ved. In tackling these the undoubted toxicity of arsenic in excess and its bad press should not blind one to the realities of actual behaviour in the environment, and that some adverse behaviour inevitably has to be to 1 erated as a cost in return for the recovery of an operation's gold for society. The problem is to minimise any such cost. Each refractory ore will be different. Many will contain so little arsenic that it will cause only trivial problems in disposal, but there remain others with significant arsenic content, say in the order of 1% or more, to cause concern. As already implied, concern can be over emphasised but equally underestimated. The balance which must be struck in the design of responsible disposal in the widest sense requires objective and practical assessment of a complex range of chemical, biochemical, engineering and economic design factors, all of which should involve the process design team in constructive dialogue with the 1 oca l regulatory authorities. In British terms, the general objective should be the Best Practicable Environmental Option. Arsenic is ubiquitous in waters, rocks and soils and all living things. Its compounds undergo natural geochemical and bi ochemi cal cycles including powerful adsorption and precipitation processes which return to nature all leakage from the inevitable disturbance of mining and extraction operations. Within limits, all life is tolerant to its presence, and adaptation and detoxication processes occur to deal with local excesses.

Jan 1, 1991

By Morris Beattie

Arseno Processing Ltd. has developed process technology for the extraction of gold from refractory ores and concentrates. These ores, which do not respond to conventional extraction processes, are rapidly becoming the primary targets for mining companies wishing to become significant gold producers. It has recently been estimated that by the mid 1990's, the annual gold production from the Pacific Basin Epithermal Arc in southeast Asia could be as high as 550 tonnes. Within this area the Porgera deposit under development by Placer, Renison and MIM, and the Lihir Island deposit under development by Kennecott have been shown to be refractory and require pretreatment before the gold can be extracted. The additional deposits which are now starting to be developed in this area are also expected to require such pretreatment. The Arseno process promises to be an economical pretreatment for these ores and to result in the maximum achievable gold recovery. Numerous refractory deposits are also known in Australia, North America, China and other parts of the world. Samples from many deposits throughout the world have been tested using the Arseno process conditions and the consistently successful results have demonstrated its widespread application. The samples which have been tested include ores in which the gold is locked in pyrite, marcasite or arsenopyrite. The process is insensitive to the sulphur content of the feed. Feed materials containing from 1 % to 50% sulphur have been tested successfully with the process. In the Arseno refractory gold process, sulphide minerals are dissolved so that the contained gold becomes accessible for subs7qu7nt gold recovery. This gold recovery is achieved by cyamdat1on of the process residue. For many of the materials which have been tested, gold extractions in excess of 95% have been achieved.

Jan 1, 1998

Let’s start off with your assessment of the mining industry. Today, the status of the mining industry is stronger than it has been over the previous few years. While mining continues to be a major part of the world’s economic foundation, the industry is gaining strength due to rebounding metals and minerals prices. And now, with these rising prices and the changes the industry instituted during the years when mining-related commodities were selling at low prices, our stronger, more productive and internationally competitive industry should flourish. Mining remains an essential business and the industry is far ahead of the game when it comes to the trend of globalization as a key to sustained growth. Also, the industry continues to realize increased productivity through technological innovations. And an increasing number of countries are moving towards capitalism, which opens doors for exploration and mining within their borders. After looking at all of these factors, I believe that the mining industry will only continue to gain momentum in the future.

Jan 1, 2004

By J. Boisvert, E. Daniels, C. Deutsch

"INTRODUCTION Conventional mine planning is performed on a single resource model at a selective mining unit (SMU) scale deemed appropriate for the deposit and mining method. Estimation methods such as kriging and inverse distance, commonly used to construct a single model, are susceptible to histogram smoothing (Rossi & Deutsch, 2014). This can be mitigated by limiting the number of composites per estimate, therefore correctly reproducing the distribution of estimates (Boisvert & Deutsch, 2012). Search limiting techniques remedy histogram smoothing at the cost of introducing a problematic conditional bias (Isaaks, 1999) and introduces even greater spatial smoothing. Localization is an alternative, offering histogram reproduction through post processing of more robust probabilistic modeling techniques. A single localized model at the SMU scale is desirable. The correct histogram can be reproduced and a single model allows for the use of conventional mine planning and resource evaluation techniques. Despite the advertized benefits of localization, the necessary reliance on a larger panel scale as a middle step in the localization process yields unsightly “edge-effect” artifacts (Boisvert & Deutsch, 2012). Two approaches to artifact reduction are presented here. The first is a straight forward optimization approach designed to minimize an objective function that penalizes artifacts including panel edge discontinuities to generate a model free of panel edge-effects. The second approach redefines a panel in a less conventional fashion as a randomly chosen set of SMUs throughout the domain. This method produces a model void of any regular panel shape resulting in an artifact free model. Artifact reduction minimizes the obvious “edge-effect” issues of localization but local precision issues described by Boisvert & Deutsch (2012) persist. Localization is not a replacement for quantified uncertainty for comprehensive risk analysis and resource evaluation. LOCALIZATION Initially developed with Uniform Conditioning in mind, localization collapses a distribution of local uncertainty at a panel scale to an SMU scale model for mine planning (Abzalov, 2006). This process has been extended to Indicator Kriging (Hardke, 2011), Sequential Gaussian Simulation (Boisvert & Deutsch, 2012) and MultiGaussian Kriging (Daniels and Deutsch, 2014). The localization process is performed in a similar fashion regardless of which technique is chosen to create the prior model of uncertainty. Methodology There are three scales of importance considered in the localization process. The point scale, otherwise known as the data scale, is the smallest and is the scale at which experimental data is collected. The SMU or block scale is the familiar scale at which mine planning and resource evaluation takes place. Lastly, the panel scale is larger and contains a number of SMUs. Each of the three scales is illustrated in Figure 1."

Jan 1, 2015

By George B. Rice

In a recent speech in Pittsburgh, Dr. George Keyworth, the President's Science Advisor, made a statement which I believe deserves our very careful consideration. Dr. Keyworth said that there is no energy crisis. The crisis, he explained, is simply that people refuse to accept the solution. The solution which Dr. Keyworth has in mind is increased utilization of our abundant supplies of solid fuels and, in particular, uranium. I share his view concerning the solution to our energy needs. The use of uranium fuel is a safe, clean, and dependable means to generate our electric power. It is time that we addressed the real energy crisis: the refusal to accept the nuclear solution. The reason for the refusal is not difficult to find. It is nihilistic thinking about risk. Under this thinking, we assume the worst possible case and act accordingly, simply because we cannot prove to a total certainty that nuclear energy is perfectly safe. If this absolutist approach were generally applied throughout our society, there is no doubt all of us would soon be sitting around our campfires fearfully holding the wild animals at bay with our trusty spears. Today I am here to enlist your support in reversing the regulatory trend that threatens the very extistence of the nuclear power industry. As distinguished scientists, engineers and businessmen, you can use your influence to help bring rational regulation to the industry. Our industry supports strong safety and environmental protection programs. We understand the need for and do not object to reasonable regulation. Many anti-pollution measures can be practical to implement, cost effective and highly successful in minimizing environmental impacts. However, it is a fact of life that in the field of health and safety regulation, the law of diminishing returns operates with a vengeance. Absolute or near-absolute safety is impossible and any attempt to achieve it is intolerably costly. Fixation on absolute safety is particularly acute in the regulation of the nuclear power industry. Government Agencies, overly anxious to allay the irrational fears of those opposed to nuclear power, are literally regulating the industry to death - exactly the result sought by the anti-nuclear groups. Dr. Robert L. DuPont wrote in a recent issue of [Business Week]: "The nuclear power industry has been virtually stopped in the U.S. [because of fear]. This is true despite the fact that for more than 20 years the commercial nuclear industry has operated under unprecedented public health scrutiny and that to date there have been no radiation-related injuries, let alone deaths, suffered by any member of the public."1 I believe a useful way to convey the nature of the problem faced by the nuclear industry is to review an example of [unreasonable] regulation. While the example relates to our domestic industry, I am certain there are similar situations in other countries. For the example I will use the Nuclear Regulatory Commission's recently issued regulations governing the stabilization of uranium mill tailings.2 These regulations, known as the Uranium Mill Licensing Requirements, specify, among other things, that radon emanation from uranium mill tailings be limited to no more than 2 pCi/m2-sec. First, one must understand that this standard will have virtually no impact on the total amount of radon to which the public is exposed. Radon emitted from even completely unstabilized tailings piles is a tiny fraction--much less than 1%--of the amount of radon released from natural soils in the United States.3 In fact, it is far outweighed by natural variations in the background flux. For example, changes in the level of the Great Salt Lake in recent years have had [eight times] as much effect on the amount of radon released into the Salt Lake City regional air than the annual release from the Vitro Mill tailings pile located in that city.4 Nevertheless, NRC claims that the standard is required to protect the public. The Commission admits, however, that there are no studies which establish that exposure to radon at the low levels associated with uranium mill tailings will result in any adverse health effects.5 In the absence of actual evidence, the Commission assumes that some such effects will occur on the basis of the linear, non-threshold model.6 Employing this model, NRC calculates that the maximum hypothetical risk for the average member of the population is only about 1 in 70,000,000 from the radon that would be emitted from [three times] the number of mills now in existence, even if the tailings produced through the year 2000 are left unstabilized.7 NRC has elsewhere explained that this level of risk would be equivalent to the risk posed by "a few puffs on a cigarette, a few sips of wine, driving the family car about 6 blocks, flying about 2 miles, canoeing for 3 seconds, or being a man age 60 for 11 seconds." This level of risk is [de minimis] in comparison to other risks commonly and readily incurred in our society.9 Moreover, even this remote risk is overstated. A group of prominent health physicists, including experts from the Department of Energy, The Environmental Protection Agency, Britain, Canada and Germany recently published a study indicating that the risk to the public per unit exposure to radon can be no greater than one-third that suggested by the Commission, and [may in fact be zero].l0 Regulators routinely rationalize the need for their regulations. For example, NRC attempts to justify the radon flux standard because it is necessary to reduce the risk to someone who builds a house on top of a tailings pile. This possibility, however, is totally unrealistic because the Mill Tailings Act requires that stabilized tailings be transferred to

Jan 1, 1981

By Paweł Stefaniak, Natalia Duda-Mróz, Sergii Anufriiev, Wioletta Koperska

Due to the high environmental risks and negative impact of a failure, tailings storage facilities (TSFs) need constant monitoring. Advanced mathematical models have been developed in the past to predict the behavior of TSFs and raise alerts if needed. To be precise and reliable, such models need a spatial distribution of soil types within the dam as an input. Getting this data from laboratory measurements is time and costconsuming. In this article, we propose an ANN-powered algorithm, which allows us to accurately estimate the soil distribution based on a cone penetration test (CPT).

Jun 25, 2023

By I. Andras

The paper deals with the application of artificial intelligence methods in assessing the effectiveness of longwall shield support. A fuzzy sets based method is used to derive response surfaces/curves representing the dependence between basic parameters of the shield, as the initial setting pressure and the stiffness on the response metric, i.e. the convergence and load increase during a working cycle.

Jan 1, 2008

By Stavros A. Argyropoulos

The strides that have been made in recent years in applying Artificial Intelligence to pyrometallurgy, are reviewed. The computer reproduction of the way in which a human being reasons and calls on past experience and stored knowledge when faced with problem will be extremely helpful in pyrometallurgy. It is expected that Artificial Intelligence will be applied in all areas of human endeavor. Areas of pyrometallurgy that are most promising for expansion into A1 in the near future are presented.

Jan 1, 1990

By P. K. Chatterjee

Before coal can be extracted from surface or underground mines, approval must be obtained from state and federal authorities. Permitting is the application process through which such approval is sought. The application includes en environmental impact statement Which focuses on Such topics as subsidence, toxicity of acid mine drainage, pollution levels of rivers and streams, post mining reclamation, and the effect on the hydrological balance of the area. The permitting procedure is extremely time and labor intensive, and requires extensive interdisciplinary knowledge. This paper examines a LISP based permitting procedure which will help to reduce the time taken to prepare and review a permit application.

Jan 1, 1986

By G. T. Lineberry, B. R. Yama

Artificial intelligence research has produced several tools for commercial application. Some of the techniques that are widely used today include neural networks, fuzzy logic and expert systems. Artificial neural networks (ANNs) are excellent predictive, pattern recognition and data analysis tools. In the mining industry, ANN techniques are being used commercially for real-time process-control applications. Modeling of spatial data, ore-reserve estimation, tunnel design, long wall-stability prediction and geologic roof classification are additional applications in which neural networks have been applied successfully. In this study, a standard back propagation algorithm was used to train a series of neural networks for a real-world predictive task. After training and optimizing the neural network architecture, the performance of the network is measured on an independent validation set. Results indicate a mean error of less than 1 % between the actual and predicted values. A neural network model was developed for learning the spatial continuity of a mineral field and, consequently, for predicting sulfur values for given coordinates. The neural network not only performed satisfactorily, but in some cases performed even better than the kriging model.

Jan 1, 2000

By Babu R. Yama

Artificial Intelligence research has produced several tools for commercial application. Neural Networks, Fuzzy Logic and Expert Systems are some of the techniques that are widely used today. Artificial Neural Networks (ANNs) are excellent predictive, pattern recognition, and data analysis tools. In the mining industry, ANN techniques are being used commercially for real-time process control applications. Modeling of spatial data, ore reserve estimation, tunnel design, longwall stability prediction, and geologic roof classification are additional application areas in which neural networks have been applied successfully. A standard back-propagation algorithm was used to train a series of neural networks for a real-world predictive task. After training and optimizing the neural network architecture, performance of the network is measured on an independent validation set. Results indicate a correlation coefficient of up to 84 % between the actual and predicted values. A neural network model is developed for learning the spatial continuity of a mineral field and, consequently, for predicting sulfur values given coordinate values. The neural network performed not only satisfactorily, but in some cases performed better than the kriging model.

Jan 1, 1997

In longwall mining, premining prediction of methane emission rate depends on a number of geological factors, geographical factors, and operational factors. These same factors also can impact the selection of a specific degasification system. This study proposes a principle component analysis (PCA) and artificial neural network (ANN) approach to predict the ventilation methane emission rates of U.S. longwall mines and the optimum combination of degasification boreholes based on the given characteristics of the mine. Artificial neural networks, more than conventional computer models, are adaptable systems that can solve problems such as non-polynomial or very complex relationships that are difficult to describe mathematically. Data on ventilation emissions and degasification system design were obtained from 63 longwall mines in 10 states and were combined with corresponding geological, geographical, and operational parameters for the period 1985-2005. PCA was used to determine the variables that most influence ventilation emissions and degasification system design. Different combinations of variables in the data set and network structures were used for network training, cross-validation, and testing. The resultant ANN models predicted ventilation methane emissions with a 90-95% accuracy and were superior to multi-linear and second order non-linear models. The ANN-based expert classification system was able to classify the design of the degasification system (no degasification, gob vent borehole, horizontal and gob vent borehole, and horizontal, vertical and gob vent borehole) with more than 80% accuracy. The models can be used as prediction and decision tools for the ventilation emissions and degasification system selection for specific site and mine-design conditions.

Jan 1, 2009

By Daniel Hodouin

Artificial neural networks (ANN) are of growing interest in a wide variety of applications. Pattern recognition, speech processing, financial analysis, signal processing and process control are at present the most successful ones. This paper is an attempt to illustrate the potentiality of ANN applied to mineral processing. An introduction to the technology of ANN is first proposed to describe the various components and to explain the various phases encountered in setting up a network. Then examples are given to illustrate the use of ANN dynamic modelling of mineral processing units or plants and in process control.

Jan 1, 1991

By Ben L. Seegmiller

The artificial stabilization of a pit slope at the Twin Buttes Mine near Tucson, Arizona has been completed as part of a practical rock mechanics program. The research program began in June 1971 as a result of the three million ton South Slope slide(1) which caused significant economic and operational problems. Hydrological studies, field and laboratory testing and investigation of various stabilization systems(2)occupied the first year of research. In July 1972 a design for an artificial stabilization test using post-tensioned cable bolts was completed and the selection of an outside contractor was made the following month. The project began in mid-September and was completed during the first week of January 1973. The test site is located adjacent to the South Slope slide in a zone of high potential failure. When the site was initially selected it was thought that it would remain stable at least until the test could be completed. However, heavy rains which fell in late October induced displacement in the half of the test site adjacent to the South Slope slide. Prior to this stabilization test it was thought both impractical as well as improbable that a moving slide could be stabilized with an artificial support system(3). Because the project was nearing the half-way point it was decided that an attempt should be made to complete the test as originally planned. This meant, in effect, that an attempt would be made to stabilize the moving section as well as the stable section.

Jan 1, 1973