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By Brad Ross

? Value in number of children ? Polygamy common ? Support of extended family and friends ? HIV/AIDS ? Not a culture of savings ? Minimal mechanical experience ? Supportive ? Unintended hindrance ? Mining claims ? Export Permits Lack of funding

May 1, 2011

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 J. A. López-Molina, S. García, V. Castellanos-Pedroza

"Groundwater inflow into underground constructions may cause hazardous working conditions and environmental problems. It is essential to localize and characterize potential flow paths and to estimate the amount of water leakages, so that appropriate sealing works can be performed. Estimating the amount of water ingress is a multi-phase process starting from qualitative and quantitative examination of rock mass. Due to association of uncertainty, vagueness and incompleteness with the monitored data set, particularly resulted from the in situ tests, accounting relevant approaches such Fuzzy Logic and Recurrent Neural Networks, are proposed in this research for learning acquisition, extraction of rules and prediction of behaviors. The computational set of artificial tools permits using the complex information entities or information granules (rock types, geologic structures, hydro-fracture potential) for data abstraction and derivation of knowledge from records (numerical or linguistic format). This is more a theoretical perspective than a set of methods or principles. As a theoretical perspective, this proposal encourages an approach to data that recognizes and exploits the information present in indexes at various levels of resolution or scales. In this sense, it encompasses the methods which provide flexibility and adaptability in the resolution at which knowledge or information is extracted and represented. The product of merging these tools is a 3D artificial-mapping of rock classification (Qi-system), geological conditions and Lugeon units that permits, as far as possible, to understand more and better the flow behavior."

Jan 1, 2015

By Ting-Wei Wang, Yung-Wen Chen

Artificial ground freezing is commonly used globally during excavation of shield tunnels and for groundwater control. It is a special construction method for temporary ground improvement that involves introducing coolant into underground pipes to freeze water in the soil. The frozen soil is cemented together as a result to form a frozen wall, which provides high strength and low permeability. Once the thickness of the ice wall reaches design requirements, underground excavation can be conducted with the coolant removed afterward to return the soil to its original state. This technique is cost-effective in scenarios where complete groundwater cut-off and excavation support are required; where work is deep underground and faces difficult ground conditions and where access restrictions limit the use of other displacement-type ground improvement techniques. Due to the demand for shield tunnel construction for utilities in Taiwan, ground freezing has become an important soil stabilization method there. In this article, we will share two brine freezing case studies and an in situ liquid nitrogen experiment that was conducted to develop this technique.

Feb 1, 2022

By Bruno Vingiani, Alessandro Bertero

"The use of Artificial Ground Freezing (AGF) for temporary soil strengthening and seepage control is an effective alternative to grouting when dealing with soils whose degree of finesses would imply the use of highly expensive chemical grouts. However, compared to grouting, the application of this improvement technique usually needs a more accurate preliminary soil investigation campaign and a design assisted by specific calculation software. Besides a general description of the main features of this technique, the paper will describe a recent application of AGF carried out in Rome to underpass an existing metro station.INTRODUCTIONNatural ground freezing occurs seasonally or permanently (permafrost) in some areas of the globe close to the poles and can negatively affect the engineering performance of underground structures. On the other hand, Artificial Ground Freezing (AGF) is man-made controllable freezing process that can be used in foundation engineering to temporarily strengthen the ground and create a barrier to the passage of groundwater. During the process, the heat is extracted from the soil in a cylindrical shape around a number of freezing pipes installed into the ground. This action produces columns of frozen soil, which expand gradually till they merge and create continuous frozen wall. The thickness of this barrier can be regulated according to the specific project requirements. Nowadays, AGF technique is applied to a wide range of foundation engineering problems where soil stability and groundwater flows are an issue (Pimentel et al. 2011, Haß and Schäfers, 2013). Typical applications include the temporary stabilization and waterproof of the ground around the excavation profile of mined tunnels until the construction of the final lining (Figure 1).The case history described in this paper deals with a recent application of AGF carried out during the construction of metro Line “C” in Rome. Specifically, this technique has been applied to allow the construction of two single-track mined tunnels running underneath an existing metro station belonging to Line “A”. In that case, the AGF has been evaluated by the Designers as the only possible solution for improving the mechanical and hydraulic characteristics"

Jan 1, 1900

By Luca Pingue, Regine Jagow-Klaff, Franco Muratore, Carlandrea Albonico, Damiano Frontini, Rudolf Wernecké

This paper describes an artificial ground freezing (AGF) application carried out to ensure both watertightness and structural support for the safe excavation of four cross passages of the Forrestfield Airport Link (FAL) project in Perth. The line will deliver 8.5 km extension of the existing urban rail network connecting the growing eastern suburbs of Perth with the existing suburban rail network. The proposed soil improvement technique is adopted as a smart alternative to jet grouting from the surface since the use of heavy equipment with long vertical mast was not allowed due to the line underpassing an airport in operation. In this paper, details of the challenges and lessons learned during both design and construction phases are given. Furthermore, it is described the geometry and the installation of freeze pipes in presence of a massive steel frame, ventilation system and slurry pipes of the tunnel boring machine (TBM) in operation. It is also illustrated the web monitoring system to allow key personnel to follow in real time the development of soil temperatures, tunnel convergences and ground surface deformations as well as changes in the interaction between international parties involved due to the COVID-19 pandemic.

May 1, 2022

By P Mugebe, M Yahyaei, M S. Kizil, R Low

This paper seeks to establish an empirical relationship between the impact of mining technological innovation implementation and a company’s market value. The company’s market value is represented by its stock price. In a bid to ascertain the relationship, a stock price prediction model is sought to be established using the firm-specific factors impacted by the implementation of innovative technology. This work is performed under the watch of the prevailing macroeconomic factors. The background anchors in the understanding that the difference between the economic status of a mineral deposit and its uneconomic status lies in the level of mining technological innovation most prevalent at a given time (Hartwick, Olewiler and Preuss, 1986; Wellmer and Scholz, 2017; Wright and Czelusta, 2003). This is derived from productivity improvement, resulting in sustainable profitability due to technological innovation. Unfortunately, there is not an adequately established relationship that links this technological innovation impact to the share price performance of the mineral commodity company.

Sep 1, 2024

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 B. Denby

Following an introduction to the symposium and its topics, including techniques such as expert systems, neural networks, genetic algorithms and case-based reasoning, the contents list of the 8 selected papers is presented

Apr 1, 1994

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 Zhang Y. D, Zhang R. X

A new decision-making approach of artificial neural network system with hierarchic structure is presented. The fundamentals of network topology, node functions and network training are introduced. A neural network system with hierarchic struct

Jan 1, 1998

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

By D F. Akhmetov, M Yasuike, M Komazaki, Y Kawa

Remote monitoring of geological situation at distant regions with complicated relief, including reliable detection of the rock falls and estimation of the scale of accidents, is of great importance for practical needs. In the paper, vibration data, caused by fall of sample objects and registered by cable sensors, are processed and analysed in order to construct some measure for the indirect evaluation of weight of fallen objects. Nonlinear models, which based on artificial neural networks (ANN), are proposed for the signal presentation. To improve and simplify the learning and classification abilities of the whole system, the aggregative learning method (ALM) is implemented. ALM features relatively low memory and computational resources needed for the procedure realisation, especially for data classification (recall) stage, in compare to conventional methods. The validity and efficiency of the proposed approach are tested through its application for rock fall detection and weight evaluation system using cable sensors and mobile communication network. Classification abilities of the proposed approach are shown useful for estimation of the fallen object weight. Characterised with high computational efficiency and simple decisionmaking procedure, the derived method can be useful for simple and reliable real-time monitoring system design.

Jan 1, 2003

By Sepideh Javanshir

The process of liquid?liquid extraction of gold from chloride solution is complicated by the presence of many variables acting simultaneously in an unknown non-linear fashion for the determination of the final extraction efficiency. In practice, it is quite difficult to develop a model for predicting various properties of such process. Therefore, an alternative approach based on Artificial Neural Network (ANN) is considered to assist in the simulation process. Artificial neural networks (ANN) are powerful tools that can be used to model and investigate various highly complex and non-linear phenomena. This paper presents application of Multi-Layer Perceptions (MLP) to construct relationship that will be employed to develop a quantitative method for the estimation of the extraction efficiency as a function of hydrodynamical parameters such as stirrer speed, [AuCl4]- and DBC concentration, organic and aqueous flow rate. The model is shown to be in excellent agreement with the experimental data (R2 value for training and testing data was 0.9992 and 0.9915 respectively). Therefore, it can be used to optimize the parameters of liquid?liquid gold extraction with dibutyl carbitol (DBC).

Sep 1, 2012

By E P. Leite, L A. Magalhpes

The Amazon Craton is one of the largest cratonic areas and comprises one of the est-endowed metallogenic provinces in the world. It hosts a number of different ore deposit types for which a variety of mineral exploration data are presently available at various scales and formats. These data, however, are not properly organised nor integrated by means of advanced spatial analysis tools. In this view, here we present a suite of neural network methods that were applied to these data, providing unprecedented mineral potential scenarios for several precious and base metals in the region. An EXTENDED ABSTRACT is available for download. A full-length paper was not prepared for this presentation.

Jan 1, 2008

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