Dissertation > Industrial Technology > Chemical Industry > Other chemical industries > Fermentation industry > General issues > Fermentation process

Modeling and Model Calibration for Nosiheptide Fermentation Process

Author MaGuanYu
Tutor LiHongRu
School Northeastern University
Course Control Theory and Control Engineering
Keywords nosiheptide parameter identification model calibration genetic algorithms
CLC TQ920.6
Type Master's thesis
Year 2012
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Biotechnology industry is in booming, and is becoming another hot area after the international economic competition of IT industry. Nosiheptide, has broad market prospects. Nosiheptide fermentation process is directly related to the product formation and final yield. This paper is to study the Nosiheptide fermentation process modeling and model calibration problems, the main contents are as follows:In the search on the basis of extensive literature, this article summarize the fermentation process knowledge and fermentation process modeling development present situation, introduce in Nosiheptide fermentation process, recount fermentation process parameters for Nosiheptide.From the actual situation in Nosiheptide fermentation process, this article select the biomass concentration, substrate concentration, dissolved oxygen, concentration and fermentation liquid volume as the state variable, and use genetic algorithm to determine the unknown parameters in the model dynamics, finally get the complete available nosiheptide phased fed-batch fermentation process model.After we get the model, this paper proposes the idea of parameter sensitivity, classify nosiheptide phased fed-batch fermentation process model parameters due to the fermentation equipment acid-base environment changes and other factors change and the emergence of the "offset". Then we can identify the need for correcting parameters, and get after correction model.In order to facilitate the model calibration study, we use Matlab GUI programming and interface design, create a fermentation process correction platform. The platform has multiple model selection, basic experimental input data, experimental model and data import, the parameter identification method of choice, calculation of the required correction parameters, the simulation results show, data export functions.

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