Abstract
In this work, Evolutionary Algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three different aspects are the target of the optimization procedure: the feeding trajectory (the amount of substrate introduced in a bioreactor per time unit), the duration of the fermentation and the initial conditions of the process. A novel EA with variable size chromosomes and using real-valued representations is proposed that is capable of simultaneously optimizing the aforementioned aspects. Outstanding productivity levels were achieved and the results are validated by practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angelov, P., Guthke, R.: A Genetic-Algorithm-based Approach to Optimization of Bioprocesses Described by Fuzzy Rules. Bioprocess Engin. 16, 299–303 (1997)
Bastin, G., Dochain, D.: On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Publishers, Amsterdam (1990)
Bryson, A.E., Ho, Y.C.: Applied Optimal Control - Optimization, Estimation and Control. Hemisphere Publication Company, New York (1975)
Chiou, J.P., Wang, F.S.: Hybrid Method of Evolutionary Algorithms for Static and Dynamic Optimization Problems with Application to a Fed-batch Fermentation Process. Computers & Chemical Engineering 23, 1277–1291 (1999)
Herrera, F., Lozano, M., Verdegay, J.: Tackling Real-Coded GAs: Operators and Tools for Behavioral Analysis. Artif. Intel. Review 12, 265–319 (1998)
Marteijn, R., Jurrius, O., Dhont, J., de Gooijer, C., Tramper, J., Martens, D.: Optimization of a Feed Medium for Fed-Batch Culture of Insect Cells Using a Genetic Algorithm. Biotechnol. Bioeng. 81, 269–278 (2003)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, USA (1996)
Moriyama, H., Shimizu, K.: On-line Optimization of Culture Temperature for Ethanol Fermentation Using a GA. Journ. Chem. Tec. Biotec. 66, 217–222 (1996)
Rocha, I.: Model-based strategies for computer-aided operation of recombinant E. coli fermentation. PhD thesis, Universidade do Minho (2003)
Rocha, I., Ferreira, E.C.: On-line Simultaneous Monitoring of Glucose and Acetate with FIA During High Cell Density Fermentation of Recombinant E. coli. Analytica Chimica Acta 462(2), 293–304 (2002)
Rocha, M., Cortez, P., Neves, J.: Evolutionary Approaches to Neural Network Learning. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 24–28. Springer, Heidelberg (2003)
Roubos, J.A., van Straten, G., van Boxtel, A.J.: An Evolutionary Strategy for Fed-batch Bioreactor Optimization: Concepts and Performance. Journal of Biotechnology 67, 173–187 (1999)
Tholudur, A., Ramirez, W.F.: Optimization of Fed-batch Bioreactors Using Neural Network Parameters. Biotechnology Progress 12, 302–309 (1996)
van Breusegem, V., Bastin, G.: Optimal Control of Biomass Growth in a Mixed Culture. Biotechnology and Bioengineering 35, 349–355 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rocha, M., Neves, J., Rocha, I., Ferreira, E.C. (2004). Evolutionary Algorithms for Optimal Control in Fed-Batch Fermentation Processes. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-24653-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21378-9
Online ISBN: 978-3-540-24653-4
eBook Packages: Springer Book Archive