Bioprocess software sensors development facing modelling and model uncertainties

• Main Author: Hulhoven, Xavier
• Other Authors: Bogaerts, Philippe
• Format: Doctoral Thesis
• Language: en
• Published: Universite Libre de Bruxelles 2006
• Subjects: Sciences exactes et naturelles; Agronomie générale; Cell culture; Biotechnology > Mathematical models; Biotechnological process control; Biotechnological process monitoring; Cellules > Culture; Biotechnologie > Modèles mathématiques; Procédés biotechnologiques > Contrôle; Procédés biotechnologiques > Surveillance; bioprocess; model; modelling; state observer; automatic; neural network; software sensor
• Online Access: http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210804

The exponential development of biotechnology has lead to a quasi unlimited number of potential products going from biopolymers to vaccines. Cell culture has therefore evolved from the simple cell growth outside its natural environment to its use to produce molecules that they do not naturally produce. This rapid development could not be continued without new control and supervising tools as well as a good process understanding. This requirement involves however a large diversity and a better accessibility of process measurements. In this framework, software sensors show numerous potentialities. The objective of a software sensor is indeed to provide an estimation of the system state variables and particularly those which are not obtained through in situ hardware sensors or laborious and expensive analysis. In this context, This work attempts to join the knowledge of increasing bioprocess complexity and diversity and the time scale of process developments and favours systematic modelling methodology, its flexibility and the speed of development. In the field of state observation, an important modelling constraint is the one induced by the selection of the state to estimate and the available measurements. Another important constraint is the model quality. The central axe of this work is to provide solutions in order to reduce the weight of these constraints to software sensors development. On this purpose, we propose four solutions to four main questions that may arise. The first two ones concern modelling uncertainties.<p><p>1."How to develop a software sensor using measurements easily available on pilot scale bioreactor?" The proposed solution is a static software sensor using an artificial neural network. Following this modelling methodology we developed static software sensors for the biomass and ethanol concentrations in a pilot scale S. cerevisae cell culture using the measurement of titrating base quantity, agitation rate and CO& === Doctorat en sciences agronomiques et ingénierie biologique === info:eu-repo/semantics/nonPublished