Macrokinetic Model For Penicillin In Fed-Batch Fermentation

Penicillin is the first antibiotic found in history. It initiates a new age of antibiotic treatment and is still widely used in clinic. Also, because of the birth of penicillin, microorganism fermentation has become a major part of pharmaceutical industry from then on. Aiming at the real process of industrial penicillin fermentation, this paper focuses on the following aspects:[1] Macrokinetic model for Penicillium chrysogenum in fed-batch fermentationA macrokinetic model describing mycelium growth and penicillin production in fed-batch fermentation of Penicillium chrysogenum is proposed on the basis of intracellular metabolic pathway network found in the literature. The model is composed of the stoichiometric balance equations for carbon source, ATP, NADH and pyruvate. A regulator model is used as an auxiliary part to simulate the lag phase during product formation. Combining the macrokinetic model with the bioreactor model, the relationship between manipulating variables, i.e., the substrate feeding rate, and state variables is established. The rolling parameter identification is applied to online identify the most sensitive model parameters so as to account for the model mismatch caused by time variant kinetic changes.[2] Model validationModel validation was done with a large number of industrial-scale penicillin cultivation(22 batches, from a large scale China pharmaceutical plant). The average error between model simulations and the measurements of product concentration is less than 3%. Finally, a 24-hour-ahead prediction of the product concentration is carried out with an average predict error of less than 3.5%.[3] Set-point control of the specific growth rate of biomassThe relationship between manipulating variables and state variables is established in the macrokinetic model. Therefore, the set-point control for the specific growth rate of the biomass is implemented via the substrate feeding rate, which maintains the specific growth rate of biomass at a relatively low level in the second half of the fermentation process. This control strategy ensures that the maximum substrate fed in is uptaken in the product formation, which benefits the economic profit of the fermentation process.[4] DiscussionThe generic makcrokinetic model is constructed using the metabolic networks of penicillin fed-batch fermentation and the glycerol phase fermentation of Pichia pastoris. The validation of this generic macrokinetic model using penicillin data has been done in the previous chapters; meanwhile the generic model is also validated using experimental data of the glycerol phase fermentation of Pichia pastoris. A process control software for biomass fermentation has been developed based on the proposed generic macrokinetic model.