A. Survyla “Novel soft sensors for bioprocess state estimation” doctoral dissertation defense

Author, Institution: Arnas Survyla, Kaunas University of Technology

Science area, field of science: Technological Sciences, Informatics Engineering, T007

Scientific Supervisor: Prof. Dr. Renaldas Urniežius (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)

Dissertation Defense Board of Informatics Engineering Science Field:
Prof. Dr. Rytis Maskeliūnas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Arnas Kačeniauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Olga Kurasova  (Vilnius University, Technological Sciences, Informatics Engineering, T007)
Assoc. Prof. Dr. Rui Oliveira (Nova University of Lisbon, Portugal, Technological Sciences, Chemical Engineering, T005)
Prof. Dr. Vidas Raudonis (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)

Dissertation defense meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73 – 402, Kaunas)

The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino g. 50, Kaunas)

Annotation: In this work, the fields of informatics engineering and biotechnology are combined in order to solve the fundamental problem of real-time recording of the most important parameters of the cultivation process, which cannot be directly measured. The main goal of the dissertation is to develop algorithms that allow the estimation of the main parameters from gas analysis information: biomass concentration, target protein, relative cell growth rate and viable mammalian culture cells. It was established that in order to maintain the optimal efficiency of the cultivation process, it is necessary to monitor the most important parameters of the bioprocess. The study introduces the concept of “virtual sensors” or “soft sensors,” software-based instruments that measure process variables in real time using accumulated process data. Special attention is given to soft sensors that evaluate the cultivation process from the exhaust gas information from the bioreactor, and this is a reliable and cost-effective alternative. In conclusion, this study contributes to the field of biotechnology by providing a systematic approach to address limitations in the measurement of critical cultivation process variables. The proposed algorithms and soft sensors are a suitable solution to effectively control and monitor the process and optimize it.