An Automated Method For Model-Plant Mismatch Detection And Correction In Process Plants Employing Model Predictive Control (MPC)

• Main Author: Ahmed Bahakim, Sami Saeed
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2012
• Online Access: http://utpedia.utp.edu.my/6648/1/2012%20-%20An%20Automated%20Method%20for%20Model-Plant%20Mismatch%20Detection%20%26%20Correction%20in%20Process%20Plants%20Empl.pdf; http://utpedia.utp.edu.my/6648/

A model-predictive controller (MPC) uses the process model to predict future outputs of the system. Hence, its performance is directly related to the quality of the model. The difference between the model and the actual plant is termed model-plant mismatch (MPM). Since MPM has significant effect on MPC performance, the model has to be corrected and updated whenever high MPM is detected. Re-identification of the process model with large number of inputs and outputs is costly due to potential production losses and high manpower efforts. Therefore, detection of the location of the mismatch is needed so that only that channel is re-identified. Detection methods using partial correlation analysis as well as other methods have been developed, but these are qualitative methods that does not indicate the extent of the mismatch clearly and whether or not corrective action is necessary. The proposed methodology of this project uses a quantitative variable (e/u) which is the model errors divided by the manipulated variables, to identify changes in the plant gain and hence the mismatch. Taguchi experiments were carried out to identity the most contributing gains to the overall process, and then focus on these major contributors to find the threshold limits of mismatch by trial and error. When the mismatch indicated by the variable (e/u) exceeds the threshold limit, auto-correction of the model gain of the controller is made to match with the new plant gain. The proposed method was assessed in simulations using MA TLAB and Simulink on the Wood and Berry distillation column case study and was successfully validated. Testing for various mismatch scenarios for both two major contributors to the process, the algorithm was able to bring the output back to the desired set-point in a very short time.