Abstract
Mining-method selection (MMS) is one of the most critical and complex decisionmaking processes in mine planning. Therefore, it has been a subject of several studies for many years culminating with the development of different systems. However, there is still more to be done to improve and/or create more efficient systems and deal with the complexity caused by many influencing factors. This study introduces the application of the entropy method for feature selection, i.e., select the most critical factors in MMS. The entropy method is applied to assess the relative importance of the factors influencing MMS by estimating their objective weights to then select the most critical. Based on the results, ore strength, host-rock strength, thickness, shape, dip, ore uniformity, mining costs, and dilution were identified as the most critical factors. This study adopts the entropy method in the data preparation step (i.e., feature selection) for developing a novel-MMS system that employs recommendation system technologies. The most critical factors will be used as main variables to create the dataset to serve as a basis for developing the model for the novel-MMS system. This study is a key step to optimize the performance of the model.
Highlights
The success of a mining project relies heavily on the feasibility of the adopted mining method that maximises profits and recovery of mineral resources while minimising environmental impacts
The entropy method is considered suitable owing to its advantage of not requiring a huge amount of historical data to analyse the relative importance of the factors
Diversity is directly related to the objective weight; the higher the diversity in a criterion, the higher the objective weight of the same criterion
Summary
The success of a mining project relies heavily on the feasibility of the adopted mining method that maximises profits and recovery of mineral resources while minimising environmental impacts. Economic factors: comparative capital and mining costs of suitable methods, reserves (tonnage and grade), mine life, production rate, and productivity These factors play an important role during the final decision-making process of MMS, determining the feasibility of the methods based on financial and economic analyses. The entropy method is considered suitable owing to its advantage of not requiring a huge amount of historical data to analyse the relative importance of the factors (i.e., features correlation) This method has the advantage of determining criteria (i.e., factors or features) weights without direct involvement (i.e., opinion or judgement) of decision-makers [15].
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