New methods for detection of control loop interaction to prioritize control loop maintenance

Abstract

In a chemical process there are many control loops connected in a multiloop control configuration. Change in set point and/or controller parameters of one control loop may also affect the variables of other loops. These 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, canonical correlation is used to calculate interaction among the loops and then PageRank algorithm is used to determine rank of the loops. In another approach, integral of absolute or squared error is used to determine loop interaction and then a proposed index is used to determine loop rank. Simulation results show the validity of the proposed methods.