Detection of control loop interactions and prioritization of control loop maintenance

Abstract

Chemical processes with multiloop control configurations have significant amount of control loop interactions due to tight mass and heat integration. Change in set point and/or controller parameters of one control loop may affect the variables of other loops. The presence of loop interactions in a process plant can cause significant quality and production losses of the plant. It is challenging to measure the degree of interaction between control loops and rank the loops according to the extent of interactions. This paper presents two data driven techniques to quantify control loop interactions and rank the loops according to their importance. In the first approach, a novel method based on canonical correlation analysis has been developed to calculate interaction among the loops and then normalization is done with respect to the maximum canonical correlation to determine the rank of the loops. In another approach, two indices have been developed using integral of absolute or squared error criteria to quantify loop interaction and determine rank of the loops. Both methods require step test data of the plant. Simulation and experimental results show the validity and efficacy of the proposed methods.