Multi-Objective Optimization of PID Controller for Temperature Control in Centrifugal Machines Using Genetic Algorithm

Abstract

The main aim of the study focuses on the optimizing the response of the PID controllers used typically for temperature control loop in centrifugal machines in sugar industry using soft-computing. The centrifugal machines are used for the filtering sugar and molasses and the whole process is carried out at a certain fixed temperature. Any alterations from the set-point will cause instability in the system resulting in unsafe process conditions, poor product quality, unnecessary plant shutdowns, higher maintenance and operating costs, etc. This study employs the optimization of PID controllers using multi-objective genetic algorithm for better plant operations. For the initial tuning of the PID controllers, classical methods like Ziegler-Nichols, Chien-Hrones-Reswick (CHR) and robust time response have been used, but all have shown the transient response; so MATLAB’s PID Tuner has been used for the initial estimation of the parameters followed by optimization using genetic algorithm and multi-objective genetic algorithm. On comparing the results, better results have been obtained in case of multi-objective genetic algorithm optimization offering better plant operation and process safety which classical methods have failed to provide.