This research paper is about developing a better type of controller, known as MPC (Model Predictive Control) for pasteurization process plant. MPC is an advanced control strategy that uses the internal dynamic model of the process and a history of past control moves and a combination of many different technologies to predict the future plant output.. The dynamics of the pasteurization process was estimated by using system identification from the experimental data. The quality of model

more »

... like ARX, ARMAX, BJ and CT model structures was checked based on best fit with validation data, residual analysis and stability analysis. Auto-regressive with exogenous input (ARX322) model was chosen as a model structure of the pasteurization process dynamics and fits about 79.75% with validation data. Finally MPC control strategies were designed using ARX322 model structure. 1. Introduction In a modern world the economic and quality issues become more and more important, efficient control systems have become indispensable. Therefore the process industries require more reliable, accurate, robust, efficient and flexible control systems for the operation of process plant. In order to fulfill the above requirements there is a continuing need for research on improved forms of control. [1] Control of temperature plays an important role in pasteurization plants. High temperature short time (HTST) is keeping milk or other food stuffs at 72 0C for 15 seconds in insulated holding tube. The pasteurization process consists of three stages like regeneration, heating and cooling sections. The crucial stage is heating process using heat exchanger to ensure unpasteurized product achieve desired pasteurization temperature before pass through holding tube and cooling sections. Prior to pasteurize milk sample, the equipment must have adequate controller to control the outlet temperature in order to maintain at standard value. [2] The proportional integral (PI) and proportional integral derivative (PID) controllers are widely used in many industrial control systems because of its simple structure. These controllers are designed without process constraints only use mathematical expression based on error from a set point. In these circumstances, conventional controllers (PI and PID) are no longer to provide adequate and achievable control performance over the whole operating range. Thus designing a controller considering the process constraints and optimize the control performance is essential. [3] Model Predictive Control also known as receding horizon control, is an advanced strategy for optimizing the performance of multivariable control systems. MPC generates control actions by optimizing an objective function repeatedly over a finite moving prediction horizon, within system constraints, and based on a model of the dynamic system to be controlled. [4]