Abstract
This work presents a bioprocesses parameter estimation method based on heuristic optimization approaches. The identification problem is formulated as a multimodal numerical optimization problem in a high-dimensional space. Then, the optimization problem is split in simpler sub-problems that require fewer computational resources. The main results are obtained using genetic algorithms (GA) and particle swarm optimization (PSO) methods. One applies three global-search metaheuristic algorithms for numerical optimization: two variants of PSO and one type of genetic algorithm. The estimation procedures are applied for identification of a bacterial growth model associated with the enzymatic catalysis where reaction kinetics is described by Monod and Haldane models . The performances of the proposed methods are analysed by numerical simulations. The simulation results indicate that the proposed metaheuristic algorithms are effective and efficient, and demonstrate that the applied techniques exhibit a significant performance improvement over classical optimization methods.
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Acknowledgments
This work was supported by UEFISCDI, project ADCOSBIO no. 211/2014, PN-II-PT-PCCA-2013-4-0544.
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Şendrescu, D., Tebbani, S., Selişteanu, D. (2015). Bioprocesses Parameter Estimation by Heuristic Optimization Techniques. In: Olaru, S., Grancharova, A., Lobo Pereira, F. (eds) Developments in Model-Based Optimization and Control. Lecture Notes in Control and Information Sciences, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-26687-9_11
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DOI: https://doi.org/10.1007/978-3-319-26687-9_11
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