Research On Modeling And Optimization Of Nosiheptide Fermentation Process

The21st century is a century of biotechnology. As a booming new industry, the development and progress of biological engineering, to a certain extent, solves environmental pollution, energy shortages, inadequate resources and many other crises that human faces, and it even affects the adjustment of industrial structure, triggers for techniques revolution. Fermentation engineering is an important component of biological engineering, and other bio-engineerings, such as enzyme engineering to obtain the enzyme and genetic engineering to obtain the bacteria, must rely on fermentation engineering. Thus, the progress of fermentation engineering and technology will play a crucial role in the further development of bio-engineering.Nosiheptide, as a novel feed additive, has been approved in the foreign market in the late1980s, while there is little research on this area in China. In recent years, with in-depth study of nosiheptide, it has been successfully applied in swine feed, poultry feed and aquaculture feed. Just because of the broad market prospects of nosiheptide, the thesis is committed to the research on modeling and optimization of nosiheptide fermentation process. The main work is summarized as follows:In this thesis, based on the extensive literatures and the technology of nosiheptide fermentation process, biomass concentration, substrate concentration, oxygen, product concentration, and fermentation broth volume are selected as the state variables to establish a phased fed-batch mechanism model of nosiheptide fermentation process. During the process of modeling, kinetic parameters are identified through genetic algorithm.This thesis presents an ideology of comprehensive sensitivity analysis of model parameters. As time goes on, the model will appear the phenomenon of aging, and then the model will be calibrated timely by updating part of the model parameters. Based on the correct model, the highest product concentration at the end of fermentation is selected as the optimal target, while temperature, aeration, stirring speed, and feeding speed are selected as the optimal decision variables. With genetic algorithm being the optimization algorithm, the results are obtained and shown by optimization datas and curves. In order to save the cost of human and material resources and to enable researchers to obtain relevant datas and simulation curves, modeling and optimization experiment simulation platform of nosiheptide fermentation process is developed in this thesis. This platform combines Visual Studio C#, Matlab2007a and SQL Server2005. This platform includes the modeling and calibration experimental platform, simulation platform and optimization platform, in which, users can do research on model parameter identification, model accuracy calculaion, model calibration, process simulation and process optimization.Finally, future directions for research on modeling and optimization of nosiheptide fermentation process are discussed after summarizing the whole work in this thesis.