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Research on Modeling and Optimization of Fermentation Process Based on Particle Swarm Optimization and Support Vector Machines

Author: XuJinRong
Tutor: PanFeng
School: Jiangnan University
Course: Control Theory and Control Engineering
Keywords: Modeling Optimization Penicillin Support Vector Machine PSO
CLC: TQ920
Type: Master's thesis
Year: 2008
Downloads: 310
Quote: 2
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Abstract


With the rapid development of biotechnology , microbial fermentation in the national economy is becoming increasingly important . However, due to the fermentation process is highly nonlinear, time- variability and uncertainty , the key variable is difficult to achieve online measurement , resulting in optimal control of the fermentation process extremely difficult , while the use of soft sensor modeling technology to predict the fermentation process is to resolve this one way out of the problem . Penicillin fermentation process is more typical of many microbial fermentation of a production process , so the penicillin fermentation process modeling and optimization control research has practical application value. Currently more for microbial fermentation process modeling and prediction method is based on neural network modeling method , but because it is based on the empirical risk minimization principle and therefore prone to over- learn the local minimum and other shortcomings , so that the model generalization ability well, thus affecting the prediction accuracy . And based on structural risk minimization principle support vector machine method has small sample learning ability, generalization ability, the prediction error is small, high-dimensional data processing ability and other characteristics . So for microbial fermentation process modeling problem, this paper based on support vector machine theory modeling approach to penicillin fermentation process predictive modeling , and forecasting model based on neural networks compared to simulation results show that SVM model has better prediction. However, support vector machine modeling process is an important parameter kernel function parameters , and punish insensitive loss function coefficients has a significant impact on the performance of the model , so the method selected parameter optimization problem exists . For microbial fermentation process optimization problem, using particle swarm optimization algorithm for support vector machine modeling process to optimize the important parameters to adjust . Simulation results show that the adjusted parameter optimization resulting model can achieve better prediction. Meanwhile Based on this model, feeding fed fermentation control process using particle swarm optimization of the fermentation process feeding rate, temperature , pH, dissolved oxygen concentration optimal control , the results show that this method can improve the final product synthesis volume .

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CLC: > Industrial Technology > Chemical Industry > Other chemical industries > Fermentation industry > General issues
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