Model Predictive Control Design for Nonlinear Process Control Reactor Case Study: CSTR (Continuous Stirred Tank Reactor)

Abstract

Continuous Stirred Tank Reactor (CSTR) is an essential focus in process control and offering a diverse range of researches in the area of chemical and control engineering. To ensure the successful operation of a continuous stirred tank reactor (CSTR), it is necessary to understand their dynamic characteristics, which ultimately enable effective control systems design. The problem of controlling the nonlinearity of CSTR is considered as a challenging issue especially for a control engineer corresponding to its non linear dynamics. Most of the traditional controllers are restricted just for linear time invariant system application. But in real world, the non linear characteristics of system and their function parameter changes due to wear and tear, that's why these changes can't be neglected. A simulation on mathematical model has several advantages over the experiment on a real model or system, which is used for steady state analysis and dynamic state analysis. Model predictive control (MPC) has been the most successful advanced control technique applied in the process industries, nowadays recognized as a standard methodology for the control of industrial and process systems. In this approach, a process model is used to predict the effect of a finite number of future moves on the controlled variable. From the transient responses of the controlled variable to the changes in manipulated variable, a dynamic network has been developed for prediction of one time step a forward in future process output with good accuracy. MPC refers to a class of computer controlled algorithms that utilize and explicit process model to predict the future response of the plant. At each control interval, an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant and entire calculation is repeated at subsequent control intervals. The proposed MPC structure is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. The simulation results confirm that the MPC is one of the best possibilities for successful control of nonlinear systems.