Retrospective comparison of several typical linear dynamic latent variable models for industrial process monitoring

• Published in: Computers & chemical engineering Vol. 157; p. 107587
• Main Authors: Zheng, Jiale, Zhao, Chunhui, Gao, Furong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2022
• Subjects: Dynamic latent variable (DLV) models; Process dynamics; Process monitoring; Retrospective study; Three-phase flow process; Process monitoring; Process dynamics; Retrospective study; Dynamic latent variable (DLV) models; Three-phase flow process
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2021.107587

•This paper presents a retrospective comparison of seven representative dynamic latent variable (DLV) models for dynamic process monitoring.•This paper summarizes the essential contributions and problems of representative DLV methods and provides a significant reference for further development on DLV models.•This paper presents guidance on the selection of DLV models in practical application by evaluating the monitoring performance of the discussed approaches through a benchmark process. Process dynamic behaviors resulting from closed-loop control and the inherence of processes are ubiquitous in industrial processes and bring a considerable challenge for process monitoring. Many methods have been developed for dynamic process monitoring, of which the dynamic latent variables (DLV) model is one of the most practical and promising branches. This paper provides a timely retrospective study of typical methods to fill the void in the systematic analysis of DLV methods for dynamic process monitoring. First, several classical DLV methods are briefly reviewed from three aspects, including original ideas, the determination of parameters, and offline statistics design. Second, a discussion on the relationships of the discussed methods has been established to make a clear understanding of process dynamics explained by each method. Third, five cases of a three-phase flow process are provided to illustrate the effectiveness of the methods from the application viewpoint. Finally, future research directions on dynamic process monitoring have also been provided. The primary objective of this paper is to summarize the prevalent DLV methods for dynamic process monitoring and thus highlight a valuable reference for further improvement on DLV models and the selection of algorithms in practical applications.