Concurrent Assessment of Process Operating Performance With Joint Static and Dynamic Analysis

Abstract

Assessment of operating performance for industrial processes is critical to guarantee high productivity and low cost, under routine operating condition. In the traditional research works on assessment of operating performance, the static characteristics are fully investigated, but the dynamic characteristics are seldom explored. Actually, the dynamic characteristics are important to distinguish operating performance and indicate the regulating actions of controllers. This article presents a concurrent static and dynamic assessment (ConSDA) method for operating performance in terms of industrial processes under closed-loop control. The performance levels are distinguished from both static and dynamic aspects. Canonical variate analysis and slow feature analysis are combined to fully extract the static and dynamic features of a process to well characterize each performance level. An efficient assessing scheme using the Bayesian inference based criterion is developed to provide meticulous assessing result with meaningful physical interpretability and sensitive switching identification for performance levels. The efficacy is demonstrated through application to a numerical example and a three-phase flow process. The rates of accurately distinguishing the performance levels for ConSDA is over 95% for the two applications with strong dynamic properties. Meanwhile, the highest average accuracy rates of four other assessing methods is 87.0%. The comparison illustrates the superiority of ConSDA.