Introduction

The problem of system parameter identification and optimization of control profiles has attracted considerable attention, mostly because of a large number of applications in diverse fields like chemical processes and biomedical systems [22,23,25,79,81,82,83]. To optimize a fed-batch culture, it is essential to have a model, usually a mathematical model, that adequately describes the production kinetics. Based on the mathematical model, the optimal control profiles can be determined to drive the bioreactor to reach the goal. Several techniques, such as the GA described in Chapter 2, have been proposed to determine the optimal control profiles [6,21,45,75,84].

Practically, the parameters of fermentation models vary from one culture to another. On-line tuning is thus necessary to find accurate and proper values of model parameters and to reduce the process-model mismatching. In this chapter, we intend to use the GA [60, 64, 65, 67] for: i) on-line identifying the parameters of a seventh-order nonlinear model of fed-batch culture of hybridoma cells, and ii) determining the optimal feed rate control profiles for separate feed streams of glucose and glutamine. Finally, we use these control profiles to drive the fermentation process to yield the highest productivity. The salient feature of the approach proposed in this chapter is the on-line model identification, which makes the method more attractive for practical use.

L. Z. Chen et al.: Modelling and Optimization of Biotechnological Processes, Studies in Computational Intelligence (SCI) 15, 29-40 (2006)

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The structure of this chapter is as follows: In Section 3.2, a mathematical model describing the kinetics of hybridoma cells [24] is introduced and the related aspects are briefly summarized. The problem of interest is also formulated here. Section 3.3 addresses the methodology proposed in this study. Numerical results are given in Section 3.4. Section 3.5 summarizes the work that is presented in this chapter.

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