Mining Method Selection Research Articles

Development of Fuzzy Pattern Recognition Model for Underground Metal Mining Method Selection

Selection of underground metal mining method is a crucial task for the mining industry to excavate the ore deposit with proper safety and economy. The objective of the proposed study is to demonstrate the application of a fuzzy pattern recognition model for the decision-making of the most favourable underground metal mining method for a typical ore deposit. The model considers eight factors (shape, depth, dip, rock mass rating [RMR] of ore zone, RMR of footwall, RMR of hanging wall, thickness of the ore body, grade distribution), which influence the mining method, as input variables. The weights of these factors were determined using the analytic hierarchy process (AHP). The study used the pair-wise comparison method to determine the relative membership degrees of qualitative and quantitative criteria as well as weights of the criteria set. The model validation was done with the deposit characteristics of Uranium Corporation of India Limited (UCIL), Tummalapalle mine selected. The weighted distances for easiest to adopt are found to be 0.1436, 0.0230, 0.0497, 0.2085, 0.0952, 0.1228, and 0.1274, respectively, for block caving, sublevel stoping, sublevel caving, room and pillar, shrinkage stoping, cut and fill stoping, and squares set stoping. The results indicate that the room and pillar mining method is having the maximum weighted distance value for the given ore deposit characteristics and thus assigned the first rank. It was observed that the mining method selected using fuzzy pattern recognition model and the actual mining method adopted to extract the ore deposit are the same.

Hierarchical fuzzy-AHP-based multi-criteria decision making approach for selection of underground metal mining method

In the last few decades, many underground mining methods were proposed for extractions of ores. The decision-making for selecting the most suitable mining method for a typical ore depost depnds on various intrinsic and extrinsic factors (intrinsic – dip, shape, thickness, depth, grade distribution, RMR (rock mass rating) and RSS (rock substance strength) of ore, hanging wall, footwall, and extrinsic – recovery, dilution, safety, productivity, flexibility, capital). The present study aims to develop a hierarchical Fuzzy-AHP (FAHP) model for choosing the most suitable underground mining method for an ore deposit. The structure of the proposed hierarchical FAHP model consists of five levels. The level-1 of the hierarchy defines two variables (intrinsic factors and extrinsic factors). These are further classified into quantitative or qualitative nature of variable (listed in level-2). The criteria, sub-criteria, and mining method variables are listed respectively in Level 3, Level 4, and Level 5. For each level of the hierarchy, a fuzzy pair-wise comparison matrices are developed using the corresponding levels’ listed variables. These matrices at each level are subsequently used to determine the local and global weights of each variable. The global weights are used for prioritizing the different mining methods. The proposed hierarchical FAHP model was validated by considering the field data of two different ore deposits in India. The results showed that the most appropriate mining method predicted from the decision-making model and the adopted mining method for extracting the ore deposit are same in two case studied mines.

Analysing the Role of Safety Level and Capital Investment in Selection of Underground Metal Mining Method

It is one of the important tasks to select a suitable mining method for economic and safely extraction of the specific ore deposit. The selection of individual mining methods depends on multiple factors like dip, shape, thickness, depth, grade distribution, RMR (rock mass rating) of ore and adjacent strata, and RSS (rock substance strength) of ore and adjacent strata. The present study aims to analyse the role of two extrinsic factors (safety and capital) in the selection of underground metal mining method. A fuzzy-AHP decision making model is developed to analyse the possible changes in the mining method with different levels of safety and capital. The study considers seven alternatives or mining methods (block caving, sublevel stoping, sublevel caving, room and pillar mining, shrinkage stoping, cut and fill stoping and square set stoping) in the model. The results revealed that the preference level or ranking of different mining method in a particular condition like low safety (SAL), medium safety (SAM), high safety (SAH), low capital (CL), medium capital (CM), and high capital (CH) remains same for different decision-making attitude and uncertainty level.

Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method

This study aims to analyse the sensitivity in decision-making which results in the selection of the appropriate underground metal mining method using the fuzzy-analytical hierarchy process (FAHP) model. The proposed model considers sixteen criteria for the selection of the most appropriate mining method out of the seven. The model consists of three-layer viz. the first layer represents the criteria (factors which influence the mining method), the second layer represents the sub-criteria (categorisation of the factors) and the third layer represents the alternatives (mining methods). The priority of the different mining methods was determined based on global weights. The global weights of seven mining method were determined using a different fuzzification factor under different decision-making attitudes (optimistic, pessimistic and unbiased). The sensitivity of the decision-making results was analysed in order to understand the robustness of the model.

Investigation of the Optimization of Unloading Mining Scheme in Large Deep Deposit Based on Vague Set Theory and Its Application

With the development of shallow surface mineral resources in metal mines, it is gradually turning to the stage of deep mining. According to the current mining depth and the average annual depth, during the period of “14th Five-Year Plan,” one-third of the underground metal mines will reach or exceed the mining depth of 1,000 m, with the deepest being 2,000 m. In the stage of deep mining, mines will face the conditions of high stress, high temperature, high well depth, and strong mining disturbance, which will greatly increase the difficulty of large-scale deep mining. Among them, the high ground stress environment is the principal problem of many technical problems in deep mining. The selection of mining method has become a prerequisite for solving the problem of efficient and safe mining of deep deposits. In this paper, the vague set theory was introduced into the selection of mining methods and a vague set model for deep unloading mining schemes was established. Taking the Jinchuan No. 2 mining area as the engineering background, four unloading schemes for deep mining were proposed, and the Vague set model was used for optimization. It is concluded that the mining approach with large-section unloading is the optimal unloading mining plan. The application shows that it has the advantages of high unloading efficiency, large production capacity, and low loss index. It has been fully promoted in the deep mining of the mining area. It is feasible and effective to use the vague set theory in the selection of deep unloading mining schemes, which provides a proper approach in the selection of deep unloading mining schemes.

A framework for Off-Earth mining method selection

A new framework for Off-Earth mining method selection is proposed based on theoretical equipment reliability and axiomatic design principles. This enables categorisation of mining equipment for different deposit types and can focus continued research efforts into high impact areas. Eventually the procedure can help streamline iterative feasibility studies for Off-Earth mining operations. A sensitivity analysis has also been conducted on various mining system parameters. This has shown that the rock breakage, excavation and transport equipment selection does not have a major impact on ISRU energy consumption. Rather, the geological parameters and processing efficiency are the main factors which influence energy usage.

A Literature Review of Multi Criteria Decision-Making (Mcdm) Towards Mining Method Selection (Mms)

A Literature Review of Multi Criteria Decision-Making (Mcdm) Towards Mining Method Selection (Mms)

Mining method selection for underground mining with the application of VIKOR method

Multi-criteria decision making is widely used in mining to solve a variety of problems, as well as to support the mine planning and design process. The choice of the mining method of excavation for underground exploitation is a very complex and responsible matter, and support in the application of multi-criteria decision-making methods are of great importance of making the final decision. This paper will present the scientific methodology for the mining method selection by the application of VIKOR method, which gives the results of compromise alternatives.

Methodology for underground mining method selection

Methodology for underground mining method selection

Methodology for underground mining method selection

Methodology for underground mining method selection

Determination of the most appropriate tools of multi-criteria decision analysis for underground mining method selection—a case study

Selecting the optimal mining method is one of the most critical issues in mining design. It entirely depends on different parameters, such as geotechnical, geological, economic, and geographic factors. Although many researchers have tried to apply a various multiple-criteria decision analysis (MCDA) technique to select the most appropriate mining method, there are no clear reasons for choosing a specific decision method. For this purpose, a new methodology has been presented in this research work to find the most appropriate MCDA techniques for selecting the optimal underground mining method in the Anguran zinc deposit, north-west of Iran. At the first step, a list of fifty parameters, including geomechanical, geometrical, technical, economic, environmental, and social parameters, has been made for selecting the optimal underground mining method. Then, the most influential parameters, including thickness, RMR of the hanging wall, and production rate, were chosen the most crucial parameters according to the experts’ opinions in this matter. Also, among all conventional MCDA methods, including ELECTRE, MAUT, ANP, MACBETH, AHP, TOPSIS, PROMETHEE, VIKOR, TODIM, Grey, MULTIMOORA, and MAHP, the TODIM has been selected as the best MCDA method for further studies on the case. Applying this technique, the cut and fill mining was chosen as the best method for the Anguran zinc mine while the square set mining was the worst method in this case. The results also revealed that the proposed method could be extensively applied to find the most appropriate MCDA in mining engineering.

Studying the Appropriate Underground Mining Methods in Sukari Gold Mine

This paper represents an attempt to study mining methods and development in the area of underground mining in Sukari gold mine. Detailed mining and rock mechanics data had been collected from Sukari gold mine to select the appropriate mining method for underground area. Underground mining methods have been studied to show that the selected mining method in SUKARI gold mine is suitable for the geology of the ore body, and hence proposed mining method could be taken into account. A brief background about Sukari gold mine is given and the used mining method in Sukari gold mine has been approved. Some of the suitable mining methods are discussed according to selection method and the most appropriate one is listed. Mining method selection was done by UBC method and Hartman flow chart.

Intelligent User Assistance for Automated Data Mining Method Selection

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

A Fuzzy Techno-Financial Methodology for Selecting an Optimal Mining Method

The main purpose of this paper is to propose a solution to the problem of mining method selection in mining projects. To this aim, a novel fuzzy techno-financial methodology based on technical and capital budgeting criteria has been developed. The methodology embodies five capital budgeting techniques, i.e., real options analysis, net present value, modified internal rate of return, profitability index and discounted payback period. The developed methodology is based on the University of British Columbia mining method selection technique and two fuzzy multiple criteria decision-making techniques called the fuzzy technique for order of preference by similarity to ideal solution and fuzzy analytic hierarchy process. The proposed methodology also integrates a sensitivity analysis of technical and financial criteria to determine the stability of the solution. To illustrate the practical application of the proposed methodology, it was applied in the selection of an optimal mining method for Konkola East orebody in Zambia. The results showed that sub-level open stoping is the optimal mining method for Konkola East orebody as reflected by the highest closeness coefficient (CCi) value of 0.14. Sensitivity analyses revealed that this solution is most sensitive to − 50% weight change in technical criteria and + 50% weight change in financial criteria. This is because these changes alter the optimal mining method from sub-level open stoping to block caving.

Application of Fuzzy Analytic Hierarchy Process to Underground Mining Method Selection

The paper proposes a problem-solving approach in the area of underground mining, related to the evaluation and selection of the optimal mining method, employing fuzzy multiple-criteria optimization. The application of fuzzy logic to decision-making in multiple-criteria optimization is particularly useful in cases where not enough information is available about a given system, and where expert knowledge and experience are an important aspect. With a straightforward objective, multiple-criteria decision-making is used to rank various mining methods relative to a set of criteria and to select the optimal solution. The considered mining methods represent possible alternatives. In addition, various criteria and subcriteria that influence the selection of the best available solution are defined and analyzed. The final decision concerning the selection of the optimal mining method is made based on mathematical optimization calculations. The paper demonstrates the proposed approach as applied in a case study.

MSAHP: An approach to mining method selection

SYNOPSIS Mining method selection is the most fundamental activity of a mining study, because everything else depends on it. It is a process whereby the mining method attributes are matched to the orebody characteristics. This crucial decision must invariably be made at a stage of the study when the least information is available. Method selection with Analytic Hierarchy Process (MSAHP) is based on the Analytic Hierarchy Process (AHP) and was developed in Microsoft Excel™. It was designed to facilitate good decisions based on expert judgement and sparse data according to a systematic mathematical process. The program matches the attributes of 10 mining methods to the characteristics of the orebody under review and results in the ranking of the mining methods according to their suitability to the orebody. The underlying premise of MSAHP is that the mining method best suited to the orebody will also be the most economical. Keywords: mining method selection, orebody characteristics, Analytic Hierarchy Process.

A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

The aim of this study is to investigate the applicability of artificial neural networks (ANN) and game theory in the development of an underground mining method selection model. To realize this, six different ANN models that can evaluate geometric and rock mass properties of an underground mine, environmental factors and ventilation conditions to determine mining methods that satisfy the safety conditions for an underground mine were developed. Among the mining methods determined by ANNs, the optimal mining method was determined by the ultimatum games, in which a compromise between safety and economic conditions was simulated. By using a combination of developed ANN models and ultimatum games, a new model based on artificial neural networks and game theory for the selection of underground mining method was developed. This model can make predictions in the presence of lack of information by following technological developments and new findings obtained in scientific/sectoral studies if learning is continuous. Moreover, the model can evaluate all selection criteria and provide literature-based solutions. In the light of findings obtained within this study, it is revealed that artificial neural networks and game theory can be used in the selection of underground mining methods.

A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

A New Model Based on Artificial Neural Networks and Game Theory for the Selection of Underground Mining Method

陕西山阳县苏岭沟金矿K6-1号矿体采矿方法选择

陕西山阳县苏岭沟金矿K6-1号矿体采矿方法选择

Geometallurgical Concepts Used in Industrial Mineral Production

AbstractGeometallurgy has developed since the 1970s, primarily on metallic ore operations. In parallel, industrial mineral operations have been optimized through detailed deposit knowledge and market development, without making specific reference to geometallurgical concepts. The Norwegian mining industry is dominated by industrial mineral and construction material operations, and, in this paper, key differences between the industrial mineral and the metallic ore sectors are investigated, along with their influence on the development and the use of economic block models and optimization methodologies. Further, the key levers and factors (mining method selection, processing route, scale, sequence, and cutoff policy) for value creation in industrial mineral operations are discussed, along with how and to what extent geometallurgy has been used. It is concluded that the five key levers cannot be used in industrial minerals operations as effectively as they are used in metallic ore operations. In industrial minerals, in situ strength variations are an important parameter in estimating key performance indicators such as recovery and product quality. When modeling the spatial variation in rock strength potential, additivity issues must be resolved by investigating the process the raw material is exposed to. The Norwegian industrial mineral sector has been using elements of geometallurgy but is facing unresolved issues related to strength variations and the use of measurement while drilling data.