Table of Contents
ISRN Chemical Engineering
Volume 2012, Article ID 413657, 15 pages
http://dx.doi.org/10.5402/2012/413657
Research Article

Modeling, Analysis, and Intelligent Controller Tuning for a Bioreactor: A Simulation Study

1Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai 600 119, India
2Department of Instrumentation Engineering, Anna University, MIT Campus, Chennai 600 044, India

Received 24 October 2012; Accepted 8 November 2012

Academic Editors: A. Gil and M. E. R. Shanahan

Copyright © 2012 V. Rajinikanth and K. Latha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this paper, a novel modeling technique has been attempted to develop the mathematical model for a bioreactor functioning at multiple operating regions. The first principle mathematical equations of the reactor are used with the POLYMATH software to generate essential data for the model development. A relative analysis is also carried out with the existing models in the literature. An optimal PID controller is then designed using a multiobjective particle swarm optimization algorithm. The controller tuning procedure is individually discussed for both the stable and unstable steady state regions. The controller tuned for each region is scheduled using a set-point scheduler to achieve a complete control over the bioreactor. The effectiveness of the proposed scheme has been confirmed through a comparative study with the controller tuning methods proposed in the literature. The results show that, the proposed method provides enhanced performance in effective reference tracking and load disturbance rejection with minimal ISE and IAE. Finally the proposed method is validated on the nonlinear bioreactor model in the presence of a measurement noise. The results testify that the PSO tuned PID performs well in tracking the change in biomass concentration at the entire operating region.