Application of Entropy Method for Estimating Factor Weights in Mining-Method Selection for Development of Novel Mining-Method Selection System

IF 0.7 Q4 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Elsa Pansilvania Andre Manjate, Mahdi Saadat, H. Toriya, Fumiaki Inagaki, Y. Kawamura
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引用次数: 0

Abstract

Mining-method selection (MMS) is one of the most critical and complex decision-making 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 novelMMS 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.
应用熵法估算因子权重在采矿方法选择中的应用,开发新型采矿方法选择系统
采矿方法选择是矿山规划中最关键、最复杂的决策过程之一。因此,多年来,它一直是几个研究的主题,最终发展了不同的系统。然而,在改进和/或创造更有效的系统和处理由许多影响因素造成的复杂性方面,仍有更多的工作要做。本研究介绍了熵值法在特征选择中的应用,即在MMS中选择最关键的因素。采用熵值法,通过估计各因素的客观权重,评估影响MMS的因素的相对重要性,从而选择最关键的因素。结果表明,矿石强度、主岩强度、厚度、形状、倾角、矿石均匀度、开采成本和贫化程度是最关键的影响因素。本研究在数据准备步骤(即特征选择)中采用熵值法开发了一个采用推荐系统技术的novelMMS系统。最关键的因素将被用作主要变量来创建数据集,作为开发novel-MMS系统模型的基础。该研究是优化模型性能的关键步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Sustainable Mining
Journal of Sustainable Mining Earth and Planetary Sciences-Geology
CiteScore
1.50
自引率
10.00%
发文量
20
审稿时长
16 weeks
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