Performance assessment method of dynamic process based on SFA-GPR. (March 2022)

• Record Type: Journal Article
• Authors: Wang, Haodong; Wang, Xin; Wang, Zhenlei
• Is Part Of: Journal of process control. Volume 111(2022)
• Page Start: 27
• Page End: 34
• Publication Date: 2022-03
• Subjects: Dynamic characteristics -- Slow feature analysis -- Gaussian process regression -- Performance assessment; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.01.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: A dynamic process performance assessment method based on slow feature analysis and Gaussian process regression (SFA-GPR) is proposed to capture more complete feature information from industrial process data with dynamic characteristics, thus improving the accuracy of the assessment model. First, different binary number performance labels are used to categorize data setsin terms of performance grades, and the process feature subspace is extracted using the slow feature analysis algorithm, followed by the establishment of the Gaussian process regression offline model between the feature subspace and the performance labels. During the online assessment, in conjunction with the proposed performance assessment strategy, performance grades and the current process's operating status are determined based on the performance label of sample data obtained by the assessment model. Finally, the method is used to assess the online performance of the debutanizer and cracking furnace in the ethylene plant. The findings indicate that the method has a higher level of assessment accuracy. Highlights: The SFA-GPR method can solve the problem of insufficient accuracy caused by the dynamic characteristics of the process. According to the proposed strategy, the current process performance level and process operating status can be identified. Experiments show that, unlike traditional methods, this method has lower computational complexity and higher accuracy.