Detection of control loop interactions and prioritization of control loop maintenance

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 also affect the variables of other loops. The presence of loop interactions in a process plant can cause significant cost, 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 provides data driven techniques to determine control loop interactions and rank the loops according to their importance. First, two indices have been developed using integral of absolute or squared error criteria to determine loop interaction and rank of the loops. In another 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 value to determine the rank of the loops. Simulation and experimental results show the validity of the proposed methods.