Dimensioning a stockpile operation using principal component analysis
来源期刊：International Journal of Minerals Metallurgy and Materials2019年第12期
论文作者：Siyi Li Marco de Werk Louis St-Pierre Mustafa Kumral
文章页码：1485 - 1494
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 consistency. However, designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades. In this paper, we address this issue by applying principal component analysis（PCA） to reduce the dimensions of the input data. The study was conducted in three steps. First, we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance. Next, we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB. We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles. Finally, 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 components. The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.
Siyi Li，Marco de Werk，Louis St-Pierre，Mustafa Kumral
Department of Mining and Materials Engineering, McGill University
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 consistency. However, designing stockpiles has often proven difficult when the input material consists of multiple sub-materials that have different levels of variances in their grades. In this paper, we address this issue by applying principal component analysis（PCA） to reduce the dimensions of the input data. The study was conducted in three steps. First, we applied PCA to the input data to transform them into a lower-dimension space while retaining 80% of the original variance. Next, we simulated a stockpile operation with various geometric stockpile configurations using a stockpile simulator in MATLAB. We used the variance reduction ratio as the primary criterion for evaluating the efficiency of the stockpiles. Finally, 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 components. The results showed that PCA is indeed useful in solving a stockpile design problem that involves multiple correlated input-material grades.
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