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.

在现代化学过程中，会收集大量过程变量，并且这些变量通常是复杂相关的，这使得很难在整个工厂范围内监视过程。为了解决这个问题，已经提出了基于多块的分布式监视方法。但是，如何对过程变量进行合理分类还没有得到很好的解决。本文提出了一种层次化的混合分布式PCA（HDPCA）建模框架来解决此问题。在HDPCA中，过程变量以分层的两层方式两次分为子块。基于改进的基于常规知识的策略来获取变量的第一层块。然后，基于改进的互信息（MI）谱聚类，每个块中的过程变量进一步划分为第二层中的子块。通过融合从第二层到整个过程的统计和控制限制，使用了两层贝叶斯推理融合策略来获取分布式监视结果。HDPCA的可行性在基准过程和工业加氢裂化过程中得到了证明，其中HDPCA优于其他比较方法。