Simultaneous State and Kinetic Observation of Class-Controllable Bioprocesses

Velislava Noreva Lyubenova, Maya Naydenova Ignatova, Vesela Nevelinova Shopska, Georgi Atanasov Kostov and Olympia Nikolaeva Roeva
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Velislava Noreva Lyubenova: Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Science, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
Maya Naydenova Ignatova: Department of Mechatronic Bio/Technological Systems, Institute of Robotics, Bulgarian Academy of Science, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria
Vesela Nevelinova Shopska: Department of Wine and Beer Technology, Technological Faculty, University of Food Technologies, 26 Maritza Blvd., 4000 Plovdiv, Bulgaria
Georgi Atanasov Kostov: Department of Wine and Beer Technology, Technological Faculty, University of Food Technologies, 26 Maritza Blvd., 4000 Plovdiv, Bulgaria
Olympia Nikolaeva Roeva: Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 105, 1113 Sofia, Bulgaria

Mathematics, 2022, vol. 10, issue 15, 1-15

Abstract: Monitoring of bioprocesses is a challenge in designing modern systems for control. In the biotechnology industry, the lack of reliable hardware sensors for key variables related to the metabolism of microorganisms is a topical problem. This predetermines the progress of a scientific field that relies on the development of software sensors for immeasurable variables. In this paper, a new approach for the monitoring of class-controllable bioprocesses that evolve through various physiological states (metabolic regimes) is proposed. At the core of the approach is the potential to present total biomass as a sum of the biomass concentrations obtained during each of the metabolic regimes. Algorithms for estimation of immeasurable variables and their kinetics are here derived and applied using real experimental data. As a case-study, a fed-batch process for phytase production by E. coli is considered. Effectiveness of the method is proven by using two sets of real experiments. One is used to tune the software sensors and the other to verify the approach. The stability analyses are provided, as well. The obtained results and successful verification confirm the adaptive properties of the approach. The considered software sensors will be further built into an interactive system for training specialists/students of biotechnology.

Keywords: adaptive monitoring; controllable bioprocesses; software sensors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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