Performance-relevant kernel independent component analysis based operating performance assessment for nonlinear and non-Gaussian industrial processes

• Published in: Chemical engineering science Vol. 209; p. 115167
• Main Authors: Liu, Yan, Wang, Fuli, Chang, Yuqing, Gao, Furong, He, Dakuo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 14.12.2019
• Subjects: Comprehensive economic index; Industrial processes; Nonoptimal cause identification; Operating performance assessment; Performance-relevant kernel independent components; Operating performance assessment; Comprehensive economic index; Industrial processes; Performance-relevant kernel independent components; Nonoptimal cause identification
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2019.115167

•A novel PR-KICA method is proposed to overcome the defects of conventional methods.•PR-KICA is applied to operating performance assessment of industrial processes.•Two new criteria are designed to rank the order and determine the number of ICs.•Online assessment results include both performance grades and conversions.•Cause identification result helps to improve the process operating performance. The operating performance assessment of industrial processes becomes increasingly important in manufacturing production. A novel operating performance assessment method based on performance-relevant kernel independent component analysis is proposed here for nonlinear and non-Gaussian processes. The proposed method accounts for the comprehensive economic index, and the objectives are simultaneously to maximize the non-Gaussianity of independent components as well as the correlations between them and the comprehensive economic index. When applying it to online assessment, it demonstrates stronger robustness and higher sensitivity than the traditional methods do, which is attributed to its capacity in highlighting the performance-relevant variation information in modeling. Furthermore, both the performance grades and the conversions can be evaluated, which enhances the interpretability of the results. For the nonoptimality, the variable contributions are used to find the possible cause. Finally, the efficiency of the proposed method is illustrated by a case of gold hydrometallurgy process.