An integrated application of control performance assessment and root cause analysis in refinery control loops

Abstract

Assessing the performance of control loops is an important component of Control Performance Monitoring (CPM) systems. Most of the industrial chemical processes have a large number of control loops interacting with each other in a complex way due to material and energy integration in the plant. A problem occurring in a certain control loop can easily upset the performance of the other control loops. Therefore, identification of the ""bad"" control loops causing a plant-wide disturbances is a crucial task. In this work, an integrated approach covering performance assessment and interaction analysis is proposed to detect the ""bad"" loops based on their performances. First, Minimum Variance Control (MVC) benchmark is used to screen-out the poor performing loops. Then, the spectral envelope method utilizing frequency analysis is used to identify the common oscillation periods among the loops under study. Finally, Granger causality is used to plot the interaction map between the loops. Even though these methods are well developed and used for several purposes separately, we present an integrated approach which focuses and analyzes the ""bad loops"". The developed approach has been tested in a refinery plant having 18 control loops. The results show that the proposed method is clearly able to identify and isolate the root-cause control loops. The validation of results and further improvements in the control loops under study have been given.