LAUSR

Abstract In modern chemical processes, a large number of process variables are collected and these variables are usually intricately correlated, which makes it difficult to carry out plant-wide monitoring of a process. To handle this problem, multiblock based distributed monitoring methods have been proposed. However, how to reasonably categorize the process variables has not been well handled. In this paper, a hierarchical hybrid distributed PCA (HDPCA) modeling framework is proposed to address this issue. In HDPCA, process variables are divided twice into subblocks in a hierarchical two-layer manner. The first-layer blocks of variables are obtained based on an improved general knowledge-based strategy. Then, the process variables in each block are further divided into subblocks in the second layer based on an improved mutual information (MI)-spectral clustering. A two-layer Bayesian inference fusion strategy is used to obtain the distributed monitoring results by fusing statistics and control limits from the second layer backward to the entire process. The feasibility of HDPCA is demonstrated on a benchmark process and an industrial hydrocracking process, in which HDPCA outperforms the other compared methods.