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Applied Research of Mixture Kernel Support Vector Machine in Modeling for Fermentation Process

Author: MaoZhiLiang
Tutor: PanFeng
School: Jiangnan University
Course: Detection Technology and Automation
Keywords: Support Vector Machine Mixed kernel Particle swarm optimization algorithm Glutamic acid fermentation process Soft Measurement Modeling
CLC: TQ920.1
Type: Master's thesis
Year: 2011
Downloads: 127
Quote: 2
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Abstract


The microbial fermentation engineering is the basis of modern biotechnology and its industrialization, with the rapid development of China's fermentation industry, continues to improve, there is an urgent need for online testing and optimization of the process parameters control the degree of automation of the fermentation process requirements. However, the limitations of sensor technology development level and price factors, the fermentation process only some of the physical parameters and chemical parameters online detection of industrialization, biomass has an important impact for the fermentation process parameters have not been able to achieve industrialization online measurements. Therefore, the fermentation process, the introduction of soft sensor technology has great theoretical and application value. To glutamic acid fermentation process as the research object, the soft sensor modeling is an important biological parameters of glutamic acid fermentation process. For glutamic acid fermentation process with a high degree of non-linear characteristics of time variability and uncertainty, the soft sensor modeling application on the basis of a detailed analysis of the fermentation process, the SVM (Support Vector Machine SVM) on glutamyl acid fermentation process biomass parameters soft sensor modeling. SVM theory, the choice of kernel function is a core issue of the kernel function, the two meet Mercer's theorem global nuclear function and local kernel function linear combination and adjusted by the weighting coefficient mixed kernel local nuclear and global nuclear work, so that the corresponding mixed kernel SVM has higher precision and better generalization ability. Designed mixed kernel SVM modeling basic steps, and the establishment of glutamic acid fermentation process based mixed kernel SVM soft sensor model, glutamic acid concentration, the concentration of residual sugar and OD value estimate. Simulation results show that the learning ability and generalization ability of the model than the single kernel function SVM model improved. Mixed kernel SVM model estimated on the basis, in order to improve the prediction accuracy of soft sensor model using optimization algorithm is an important parameter in the mixed kernel SVM modeling process with a strong global search capability of chaotic particle swarm (CPSO) optimal adjustment. The algorithm consists of precocious convergence judgment and processing mechanism is composed of two parts, with a strong ability to avoid local minima, through the function simulation experiments verify the effectiveness of the algorithm. Design specific steps of this method, glutamic acid fermentation modeling results show that using this method to optimize the model parameters, the modeling accuracy has been further improved.

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