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1 Literature Review

2 Literature Review This review of literature will discuss and critically analyze different mining method selection schemes, their usage, benefits and drawbacks. The Analytical Hierarchy Process (AHP) This model has been used a lot as a mining method. In this process, a complex decision problem is broken down into criteria, sub-criteria, and alternatives in a hierarchical structure for AHP. Pairwise comparisons are used to evaluate the criteria and alternatives. The efficacy of AHP in selecting mining methods has been the subject of several studies like Want et al. 2014. AHP was used, for instance, in a 2008 study by Kecojevic and Komljenovic to choose the mining method for a coal deposit in Serbia. According to the findings of the study, AHP provided a transparent and methodical approach to selecting a mining method and assisted in determining the mining method that was most suitable in light of the economic, environmental, and social factors. One more study by Mu, et al. (2007) evaluated various mining techniques for an Iranian gold deposit using AHP. The investigation discovered that AHP was successful in choosing the most fitting mining strategy in light of variables, for example, creation rate, capital expense, working expense, and natural effect. For selecting a mining method, Hartman (1992) compared AHP to other decision-making techniques like fuzzy logic and decision tree analysis. Due to its ability to handle complex decision-making issues and simultaneously take into account multiple criteria, the study determined that AHP was the best method for selecting mining methods. AHP is effective, but it has some drawbacks. The approach is based on subjective judgments, and the

3 outcomes may vary based on the preferences and biases of those making the decisions. Additionally, AHP is predicated on the accuracy and completeness of the input data, which may not always be the case in practice. The Kuz-Ram model This approach, which was developed in the 1960s, is still extensively utilized today. It takes into account things like the strength and thickness of the ore body, the depth of the deposit, and the quality of the rock mass. The mining industry frequently employs the Kuz-Ram model when deciding which mining strategy is best for a given ore deposit. In the 1960s, G.V. Kuznetsov and G.V. Ramazanov made the initial proposal for the model, which has since undergone numeanfrous revisions and enhancements. One of the vital qualities of this model described by Cunningham (2005) is its straightforwardness and convenience. The model makes use of a set of easily-measurable ore strength, depth, thickness, and rock mass quality as input parameters. These boundaries are then used to ascertain the most fitting digging technique for the given store. This model is effective, but it has some drawbacks. The model doesn't take into account important things like how mining affects the economy and the environment, which are important things to think about in modern mining operations ( Hartman, 1992). Additionally, the model is predicated on the independent input parameters, which may not always be the case. The Decision Support System for Underground Mining (DSSUM)

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4 DSSUM is a computer-based tool for making decisions that takes technical and financial considerations into account when choosing an underground mining method. A computer-based decision-making tool known as the Decision Support System for Underground Mining (DSSUM) has been utilized in the process of selecting a mining method. DSSUM coordinates both specialized and monetary elements to help chiefs in choosing the most fitting digging technique for a given mineral store. A few examinations have assessed the viability of DSSUM in mining strategy choice ( Hartman, 1992). Liu, et al (2007) in the study selected the mining method for a coal deposit in China using DSSUM. The investigation discovered that DSSUM gave a thorough and objective way to deal with mining technique determination and assisted with recognizing the most fitting mining strategy in light of elements like store math, orebody qualities, and hardware choice. Marjority of the authors such as Chen, et al. (2008) study compared DSSUM to other decision-making techniques for mining method selection, such as AHP and fuzzy logic. According to the study, AHP and fuzzy logic were better suited for selecting the most appropriate mining method based on social and environmental factors, while DSSUM was successful in selecting the most appropriate mining method based on technical and economic factors. Darling (2011) applied DSSUM to choose the digging technique for a copper store in Iran. It also assisted in determining the mining method that was most suitable based on aspects like ore grade, rock strength, and mining depth. DSSUM is effective, but it has some drawbacks. The technique depends on exact and finish information input, which can be a test practically speaking. Additionally, the assumptions and preferences of the decision-makers may influence the results because DSSUM may not take into account all relevant economic and technical factors. Fuzzy Logic Model

5 Linguistic variables are used in fuzzy logic models to describe the deposit's characteristics and the mining method. With this method, making decisions can be made with more uncertainty and flexibility. Acargolu et al. (2011) classified images from remote sensing using a fuzzy logic model. The authors employed a fuzzy rule-based classification strategy, which made it possible to deal with the classification process's uncertainty and imprecision. The findings demonstrated that the fuzzy logic approach performed better than artificial neural networks and maximum likelihood classification techniques. Bahri et al. ( 2015) identified abnormal behavior in smart grid systems using a fuzzy clustering strategy. A fuzzy c-means algo to cluster data were utilized by the authors to identify abnormal behavior and cluster the data. The findings demonstrated that the fuzzy logic approach outperformed other clustering strategies like k-means in identifying abnormal behavior. An investigation carried out by Acaroglu, et al. (2008) used a fuzzy logic model to analyze the sentiment of Chinese microblog posts. The authors classified texts according to their sentiment using a fuzzy rule-based method. The outcomes showed that the fluffy rationale approach beat other opinion examination strategies, including support vector machines and most extreme entropy models. The Delphi technique A group of experts offer their thoughts on the most effective mining strategy in this consensus- building method. The outcomes are then investigated to show up at a ultimate conclusion. Through a series of surveys and feedback, the Delphi method is a structured communication strategy that aims to reach a consensus among experts. While it is usually utilized as a direction and determining device, it has likewise been applied as a mining technique in information

6 examination. Darling (2011) mentioned that identifying key factors or indicators, forecasting future trends, and evaluating the impact of interventions are just a few of the many areas where the Delphi method can be used to gather and synthesize expert opinions. It includes a few rounds of reviews and criticism, with the outcomes from each round illuminating the ensuing round. The Delphi method's ability to deal with data that is both complex and uncertain is one of its main advantages as a mining method. As per Fuling, et al. (2017), by get-together and combining well- qualified assessments, it can give experiences into points that may not be completely perceived or quantifiable. Key indicators or factors that may be difficult to measure or quantify can also benefit from this method's identification and prioritization.

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7 References Acaroglu, O. (2011). Prediction of thrust and torque requirements of TBMs with fuzzy logic models. Tunnelling and Underground Space Technology, 26(2), 267-275. Acaroglu, O., Ozdemir, L., & Asbury, B. (2008). A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunnelling and Underground Space Technology, 23(5), 600-608. Bahri, N. A., Ebrahimi, F. M. A., & Reza, S. G. (2015). A fuzzy logic model to predict the out-of- seam dilution in longwall mining. International Journal of Mining Science and Technology, 25(1), 91-98. Chen, E. S., Hripcsak, G., Xu, H., Markatou, M., & Friedman, C. (2008). Automated acquisition of disease–drug knowledge from biomedical and clinical documents: an initial study. Journal of the American Medical Informatics Association, 15(1), 87-98. Fuling, W., Jiangtao, W., & Guoqiang, L. (2017). Study on the Evaluation System of Regularization Construction of China Maritime Safety Administration. INNOVATION AND MANAGEMENT. Hartman, H. L. & Mutmansky, J. M. (2002). Introductory mining engineering (2nd ed.). John Wiley & Sons Inc., New York: U.S.A. [ISBN: 0471348511] Hartman, H.L. (Ed.). (1992). SME Mining Engineering Handbook (2nd ed.). Society for Mining, Metallurgy, and Exploration, Inc., Englewood: U.S.A [ISBN: 0873351002]

8 Liu, K., Zhu, W., Wang, Q., Liu, X., & Liu, X. (2017). Mining method selection and optimization for hanging-wall ore-body at Yanqianshan Iron Mine, China. Geotechnical and Geological Engineering, 35, 225-241. S Darling, P. (Ed.). (2011). SME Mining Engineering Handbook (3rd ed.). Society for Mining, Metallurgy, and Exploration, Inc., Englewood: U.S.A [ISBN: 9780873352642

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