Optimization of Product Grade Transition by Model Predictive Control

Abstract

The production of synthetic polymers represents an important part of chemical industry. In these processes it is common that the same process is used for the production of different kind of products (various molecular weights, compositions, etc.). Therefore, beside the optimization of the operating conditions related to the production of different products, it is also important to minimize the time of the grade transition reducing the amount of off-specification products. This optimization can be considered as an optimal control problem. Among the wide range of tools and algorithms can be used to solve optimal control problems this paper studies the applicability of model predictive control (MPC) solutions. In the chemical industry the influence of MPC is increasing, they are very successful in wide range of industrial applications. This became possible because more and more algorithms are available for the implementation of model predictive controllers. MPC requires a proper model for the prediction of the effect of the current control signal to allow its optimization. It is important to note that the nonlinear behavior of the process mainly appears during grade transitions than at steady state operation. This phenomena would require the utilization of nonlinear models in the controller. However, the application of nonlinear first-principles models is restricted due to the formulation of these models requires the identification of large amount of kinetic parameters, which can be very time-consuming and costly. In these situations the applications of datadriven models models could be more beneficial. Hence this paper MPC solutions for the optimization of grade transitions based on input/output data driven models is studied. The free radical polymerization reaction of methyl-metacrylate is considered using azobisisobutironitil (AIBN) as initiator, and toulene as solvent. The aim of the process is producing different kind of grades, and the number-average molecular weight was for identify the right state of process, and it can be influenced by the inlet initiator flow rate. The proposed controller is compared to the wide-spread applied PID controllers and the control performances results are qualified the ISE (integral Square of Error) criteria. Using the impulse response and the step response models of the reactor, Dynamic Matrix Controller as MPC has been designed. The results show that the performance of the model predictive controller is better than the performance of PID controller which is also proved by the ISE criteria.