Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity

• Published in: The Korean journal of chemical engineering Vol. 39; no. 2; pp. 275 - 283
• Main Authors: Huang, Ruoyu, Li, Zetao, Cao, Bin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V; 한국화학공학회
• Subjects: Aluminum; Bayesian analysis; Biotechnology; Catalysis; Chemistry; Chemistry and Materials Science; Decomposition; Electrolysis; Industrial Chemistry/Chemical Engineering; Materials Science; Monitoring; Process Safety; Process Systems Engineering; Statistical inference; 화학공학; Distributed Monitoring; Inter-unit Dissimilarity; Fault Detection; Dynamic Process; Slow Feature Analysis
• ISSN: 0256-1115; 1975-7220
• DOI: 10.1007/s11814-021-0901-6

In order to overcome the dynamic and large-scale characteristics of the plant-wide processes, this paper proposed a distributed slow feature analysis (SFA) with inter-unit dissimilarity method for process monitoring task. Firstly, to highlight the local dynamic features, the whole process is decomposed into several units according to the prior knowledge. Based on this, SFA monitoring model is built parallelly to handle the dynamic features. Considering the possible information loss caused by the process decomposition, the inter-unit dissimilarity index is carried out to monitor the variations between adjacent units. Finally, the fusion center is conducted by Bayesian inference to combine the results of SFA monitoring models and inter-unit dissimilarity statistics. The effectiveness of the proposed method is tested on the Tennessee Eastman process and an aluminum electrolysis process.