%0 Journal Article
%T Modeling, Analysis, and Intelligent Controller Tuning for a Bioreactor: A Simulation Study
%A V. Rajinikanth
%A K. Latha
%J ISRN Chemical Engineering
%D 2012
%R 10.5402/2012/413657
%X 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. 1. Introduction Bioreactor plays a vital role in chemical process industries to produce important chemical and biochemical compounds. In this system, living organisms also known as microbes are converted into marketable products such as beverages, antibiotics, vaccines, and industrial solvents [1¨C3]. The quality of the final product from a bioreactor depends mainly on the control loop employed to monitor and control the microbial growth based on the reference input. Apart from this, incidental external and internal disturbances in a reactor may result in reactor failure. Therefore, there is a strong financial inspiration to develop a finest control scheme that would facilitate rapid startup and stabilization of continuous bioreactors subject to redundant disturbances [4]. In the literature, a variety of methods have been discussed to implement a robust controller for the bioreactor operating at single or multiple steady-states. Kumar et al. have examined a bioreactor with input multiplicities. With an experimental study, the mathematical models for the different steady state operating regions are developed, and a nonlinear PI controller was implemented [5]. Sivakumaran et al. have discussed recurrent neural network (RNN) based modeling method for a nonlinear bioreactor operating
%U http://www.hindawi.com/journals/isrn.chemical.engineering/2012/413657/