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.

矿物加工厂通常对其输入原料的等级具有较窄的公差，因此通常要保持库存以减少材料差异并确保一致性。但是，当输入物料由等级不同的不同级别的多种子物料组成时，设计库存常常被证明是困难的。在本文中，我们通过应用主成分分析（PCA）来减小输入数据的维数来解决此问题。该研究分三个步骤进行。首先，我们将PCA应用于输入数据，以将其转换为较低维空间，同时保留原始差异的80％。接下来，我们使用MATLAB中的库存模拟器来模拟具有各种几何库存配置的库存操作。我们使用方差减少率作为评估库存效率的主要标准。最后，我们使用多元回归来确定库存效率与各种设计参数之间的关系，并基于原始输入变量和主成分来分析回归结果。结果表明，PCA在解决涉及多个相关输入物料等级的库存设计问题方面确实有用。