Disturbance Modelling based Benefit estimation from Advanced Process Control: Case study on Delayed Coker Unit

Benefit estimation is one of the key components for introducing Advanced Process Control (APC) / Multi-variable Predictive Control (MVPC) / Model Predictive Control (MPC) to a process, as the cost associated with it have to be justified in economic terms. The conventional approach to estimate benefit is based on the assumption of percentage reduction in the standard deviation of key controlled process variables, which comes from the experience and process knowledge. The conventional technique is found to be ineffective because of the uncertainty associated with it. In this research, an approach was made to develop a novel method to numerically estimate percentage reduction in the standard deviation of key controlled process variables by modelling the disturbance(s) with application to APC such that we need not assume the reduction in standard deviation while basic equation remains same as in conventional approach. The effectiveness of the proposed method is justified by implementing it in MATLAB on the real process plant data of Delayed Coker Unit (DCU) in Petrochemical refinery which experiences cyclic disturbance(s). The simulation is done by two ways, one is directly injecting the disturbance data & other is by characterizing/modelling the disturbance pattern and both the results are found to be very close. These two results were further verified by comparing with real plant data after APC has been implemented.