Dimensioning a stockpile operation using principal component analysis

作     者：Siyi Li Marco de Werk Louis St-Pierre Mustafa Kumral 

作者机构：Department of Mining and Materials Engineering McGill University 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2019年第26卷第12期

页      面：1485-1494页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：bed-blending mining stockpile principal component analysis multiple regression 

摘      要：Mineral processing plants generally have narrow tolerances for the grades of their input raw materials,so stockpiles are often maintained to reduce material variance and ensure ***,designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their *** this paper,we address this issue by applying principal component analysis(PCA)to reduce the dimensions of the input *** study was conducted in three ***,we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original ***,we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in *** used the variance reduction ratio as the primary criterion for evaluating the efficiency of the ***,we used multiple regression to identify the relationships between stockpile efficiency and various design parameters and analyzed the regression results based on the original input variables and principal *** results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.