An analytical partial least squares method for process monitoring. (July 2022)

• Record Type: Journal Article
• Authors: Qin, Yihao; Lou, Zhijiang; Wang, Youqing; Lu, Shan; Sun, Pei
• Is Part Of: Control engineering practice. Volume 124(2022)
• Publication Date: 2022-07
• Subjects: Process monitoring -- Information impurity -- Analytical PLS (APLS) -- Thermal power plant process; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105182
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Partial least squares (PLS) is an algorithm commonly used for key performance indicator (KPI) industrial process monitoring in recent years. However, there are many shortcomings in PLS, such as uncertainty of the optimization solution, an imperfect optimization goal, and information impurity. To overcome these shortcomings, an analytical PLS (APLS) method is proposed in this study. APLS fully analyzes the correlation between process variables and quality variables, and is solved by an analytic solution to avoid the large computational complexity brought by iterative calculations. A computational complexity analysis of PLS and APLS is performed to verify the advantages of APLS in terms of computational complexity compared to PLS. To better further study the information impurity existing in PLS, we present the proof related to this problem. Moreover, in order to verify the effectiveness of APLS, a numerical example and the thermal power plant process are utilized. It can be seen that the proposed method has a better detection performance compared with existing PLS-related methods. Highlights: An analytical partial least squares method is proposed for process monitoring. The analysis of information impurity problem existing in partial least squares (PLS) is performed. APLS has lower computation complexity than PLS. Numerical example and thermal power plant process data testing show that APLS has better monitoring performance than PLS.