Applying Nonnegative Matrix Factorization for Underground Mining Method Selection Based on Mining Projects' Historical Data

A numerical study for a mining project using real options valuation under commodity price uncertainty

The present study aims to make a comparative study of the selection of mining method using five multi-criteria decision-making (MCDM) models (TOPSIS, VIKOR, improved ELECTRE, PROMETHEE II, and WPM). Underground mining method selection is a multi-criteria decision-making problem, and the mine planners face the challenges in the selection of the appropriate mining method. The selection of mining method depends on multiple intrinsic factors (dip, shape, thickness, depth, grade distribution, RMR of ore, RMR of hanging wall, and RMR of footwall) and extrinsic factors (available technology). The study considered only intrinsic factors in selection of mining method. In the last few decades, many multi-criteria decision-making models have been developed. The study uses AHP technique for determining the weights of the effective criteria. The proposed techniques were implemented for Tummalapalle mine of Uranium Corporation of India Limited (UCIL), India. The results revealed that mining methods selected were not uniform. Actually it is a case of room and pillar being the preferred method by three of the MCDM models, while it is a second or equal preference method in two of the MCDM models applied.

The Selection of the mining method for underground minerals extraction is the crucial task for the mining engineers. Underground minerals extraction is a multi-criteria decision making problem due to many criteria to be considered in the selection process. There are many studies on selection of underground mining method using Multi Criteria Decision Making (MCDM) techniques or approaches. Extracting minerals from the underground involves many geological characteristics also called as input parameters. The geological characteristics of any mineral deposit vary from one location to another location. Thus only one mineral extraction method is not suitable for different deposit characteristics. There are many mineral extraction methods available for different characteristics of the ore deposit. As of now only MCDM approach or Hybrid MCDM approaches or MCDM approaches with fuzzy logic were used for selecting a mining method for underground metal mine. In this study, only fuzzy logic approach is used for selecting a mining method for different deposit characteristics. The proposed model considers five deposit characteristics as input parameters and seven underground mining methods output parameters The developed fuzzy logic based approach is also validated by the deposit characteristics of two Indian mines. The model produced the suitable mining method for extraction of the minerals at the specified Indian mines and the same mining methods are used by the mine authorities.

The aim of the current study is to develop a multi-criteria decision making model using fuzzy-analytical hierarchical process (FAHP) for selection of the appropriate underground metal mining method. The model considered eight criteria for evaluating the seven mining methods. A three-level hierarchical structured model is proposed for decision-making analysis. The criteria, sub-criteria, and the mining methods are listed respectively in the first, second, and third level of the hierarchy. The local and global weights for each parameter were determined using the FAHP method. The global weights of the various mining methods were used to determine the ranking of various mining method for a typical ore deposit. The model validation was done using the ore deposit data of two mines in India. It is observed that the adopted mining methods and the corresponding predicted mining methods are same in both the cases.

The aim of the current study is to develop a multi-criteria decision making model using fuzzy-analytical hierarchical process (FAHP) for selection of the appropriate underground metal mining method. The model considered eight criteria for evaluating the seven mining methods. A three-level hierarchical structured model is proposed for decision-making analysis. The criteria, sub-criteria, and the mining methods are listed respectively in the first, second, and third level of the hierarchy. The local and global weights for each parameter were determined using the FAHP method. The global weights of the various mining methods were used to determine the ranking of various mining method for a typical ore deposit. The model validation was done using the ore deposit data of two mines in India. It is observed that the adopted mining methods and the corresponding predicted mining methods are same in both the cases.

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.

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.

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

Decision making is a process of selecting the best alternative from the pool of alternatives. This selection process depends on many influencing parameters; these parameters may be beneficiary or non-beneficiary. The proposed decision-making techniques were implemented in the appropriate underground mining method selection process. The selection of underground mining method depends on various geo-mining parameters such as technical, physical, mechanical, and economic parameters. In the proposed work, the selection of best mining method for bauxite deposit was implemented using Improved AHP and VIKOR. Improved AHP technique was considered to determine the weights of the influencing parameters. The proposed techniques can consider the association among the influencing parameters and alternatives. Results obtained by the proposed techniques were compared with the results obtained by other researchers for the bauxite deposit. The results showed that the suitable mining method for the specified criteria of the bauxite mine was conventional cut and fill.

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory. Considering the geologic conditions, technology, economy and safety production, ten main factors influencing the selection of mining method were taken into account, and the comprehensive evaluation index system of mining method selection was constructed. The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively. New measurement standards were constructed. Then, the unascertained measurement function of each evaluation index was established. The index weights of the factors were calculated by entropy theory, and credible degree recognition criteria were established according to the unascertained measurement theory. The results of mining method evaluation were obtained using the credible degree criteria, thus the best underground mining method was determined. Furthermore, this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China. The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model, so the optimal method can be easily determined. Meanwhile, the proposed method can take into account large amount of uncertain information in mining method selection, which can provide an effective way for selecting the optimal underground 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.

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.

Application of Z-numbers theory to study the influencing criteria in underground 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.

U.S. Department of Energy studies have indicated that the United States has produced only about one-third of its estimated reserves of heavy oil, primarily because this oil is unrecoverable by conventional production methods. One technology that shows promise for recovering these reserves is oil raining. Of three fundamental mining methods, surface extractive mining, underground extractive mining and underground mining for access, the surface extractive mining and underground mining for access methods appear to be technically feasible for oil recovery. Two heavy oil reservoirs are used as the basis for an economic evaluation of the two technically feasible mining methods. These two reservoirs were selected from an extensive list of heavy oil reservoirs in the United States based on a favorable combination of physical characteristics including depth, net pay thickness, oil saturation in barrels per acre, reservoir area, and total estimated reserves. The Kern River Field, Kern County, California was used as the basis for an economic evaluation of oil production using surface extractive mining methods. The McKittrick Field, Kern County, California was used as the basis for an economic evaluation of oil production using underground mining for access methods. The economic evaluation of surface extractive mining was based on a mine plan capable of producing approximately 38,000 barrels of oil per day for a 20 year project life. The economic evaluation indicated that surface extractive mining would require an oil selling price of $27.26 per barrel to produce a 20 percent return on investment. This evaluation included capital and operating costs for mining, processing, and disposal of the processed oil sand ore. The economic evaluation of underground mining for access was based on a mine plan capable of producing between 10,000 and 20,000 barrels of oil per day for a nine year project life. The mining for access method was determined not to be economical without thermal assistance because capital and operating expenses were not fully recovered over the project life due to low oil mobility. The economic evaluation indicated that mining for access with thermal assistance would require an oil selling price of $43.25 per barrel to produce a 20 percent return on investment. This analysis indicates that lighter oils with higher mobility offer better potential for successful economic demonstration of mining for access technology.