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

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 decompo...

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Bibliographic Details
Published in:The Korean journal of chemical engineering 2022, 39(2), 263, pp.275-283
Main Authors: Huang, Ruoyu, Li, Zetao, Cao, Bin
Format: Article
Language:English
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
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Summary: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.
ISSN:0256-1115
1975-7220
DOI:10.1007/s11814-021-0901-6