An analytical partial least squares method for process monitoring

被引:28
作者
Qin, Yihao [1 ]
Lou, Zhijiang [2 ]
Wang, Youqing [1 ]
Lu, Shan [2 ]
Sun, Pei [2 ]
机构
[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
[2] Shenzhen Polytech, Inst Intelligence Sci & Engn, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Process monitoring; Information impurity; Analytical PLS (APLS); Thermal power plant process; ALGORITHMS;
D O I
10.1016/j.conengprac.2022.105182
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:8
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