Method of Soft-Sensor Modeling for Fermentation Process Based on Geometric Support Vector Regression  

作　　者：吴佳欢 王晓琨 王建林 赵利强 于涛 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology

出　　处：《Journal of Donghua University(English Edition)》2013年第1期1-6,共6页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.20676013)

摘　　要：The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.The soft-sensor modeling for fermentation process based on standard support vector regression （SVR） needs to solve the quadratic programming problem （QPP） which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft.sensor model is converted into the problem of finding the nearest points between two convex hulls （ CHs ） or reduced convex hulls （RCHs） in geometry. Then a geometric algorithm is adopted to generate soft.sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization （SEC-PSO） algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of ~soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.

关 键 词：fermentation process soft-sensor modeling geometric SVR swarm energy conservation particle swarm optimization (SEC-PSO) 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]