Metaheuristic algorithm for Modeling and control of Clarification process of sugar Industry

Abstract

Mathematical modeling is a fundamental problem in control design for a modern industrial process. The realistic representation of many physical systems results in higher order system, nonlinear nature, due to the presence of unknown parameters and sudden process parameter variations. In many cases, industries need reliable, healthy, accurate, and stable control systems during the entire situations, such as a change in environmental conditions and process variables, etc. To fulfil the above necessities, control engineers and researchers have been striving with their research on enhancement of controller design. Often industries are working with Proportional Integral Derivative (PID) control. In many situations, the PID controller fails to provide a required result which can be obtained by proper and robust parameter tuning of the controller so that the desired performances can be achieved. For tuning the controller parameter, conventional methods like Ziegler-Nichols (ZN) and Cohen-Coon (CC) methods are used. Both the methods are effective, when the system is linear. Nevertheless, the nonlinear nature of the industrial processes is often changing with time and easily affected by an external disturbance. The linear controller design is a complicated task, however, addressed by intelligent controller design. Intelligent controller design using metaheuristic algorithms rather than the conventional method often proves its robustness and ensures the closed-loop system stability. newline