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Bioengineering 2017, 4(1), 21;

Hybrid Approach to State Estimation for Bioprocess Control

Department of Automation, Kaunas University of Technology, Kaunas 44249, Lithuania
Department of Biochemie/Biotechnologie, Martin-Luther-Universität Halle-Wittenberg, 06108 Halle, Germany
Author to whom correspondence should be addressed.
Academic Editors: Christoph Herwig and Martin Koller
Received: 26 October 2016 / Revised: 27 February 2017 / Accepted: 2 March 2017 / Published: 8 March 2017
(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)
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An improved state estimation technique for bioprocess control applications is proposed where a hybrid version of the Unscented Kalman Filter (UKF) is employed. The underlying dynamic system model is formulated as a conventional system of ordinary differential equations based on the mass balances of the state variables biomass, substrate, and product, while the observation model, describing the less established relationship between the state variables and the measurement quantities, is formulated in a data driven way. The latter is formulated by means of a support vector regression (SVR) model. The UKF is applied to a recombinant therapeutic protein production process using Escherichia coli bacteria. Additionally, the state vector was extended by the specific biomass growth rate µ in order to allow for the estimation of this key variable which is crucial for the implementation of innovative control algorithms in recombinant therapeutic protein production processes. The state estimates depict a sufficiently low noise level which goes perfectly with different advanced bioprocess control applications. View Full-Text
Keywords: State estimation; hybrid modeling; Unscented Kalman Filter; recombinant protein production State estimation; hybrid modeling; Unscented Kalman Filter; recombinant protein production

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Simutis, R.; Lübbert, A. Hybrid Approach to State Estimation for Bioprocess Control. Bioengineering 2017, 4, 21.

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