基于tPSO-BPNN的赖氨酸发酵软测量.pdfVIP

学兔兔 第3l卷 第1O期 仪 器 仪 表 学 报 V01．31 No．1O 2010年10月 Chinese Journal of Scientific Instrument 0et．2010 基于tPSo．BPNN的赖氨酸发酵软测量术 黄 丽，孙玉坤，嵇小辅，黄永红，王 博 (江苏大学电气信息工程学院 镇江 212013) 摘 要：赖氨酸发酵过程是一个复杂的非线性强耦合动态过程。某些发酵过程关键生物参数(如菌体浓度、基质浓度、产物浓 度)难以实时在线检测。采用软测量技术可解决这一难题。建立了用于生物参数状态预估的tPSO—BPNN软测量模型。针对 BPNN软测量模型易陷入局部极小值，进化后期收敛速度慢以及全局有哪些信誉好的足球投注网站能力弱等缺陷，tPSO—BPNN软测量模型采用带极值扰 动粒子群(tPSO)算法优化BP神经网络权值和阈值。仿真结果表明，tPSO—BPNN软测量模型的性能优于BPNN软测量模型，能 够准确预估赖氨酸发酵过程中的关键参数，具有较高的精度和良好的应用前景。 关键词：带极值扰动粒子群优化；赖氨酸；生化参数；软测量 中图分类号：TP18 文献标识码：A 国家标准学科分类代码：120．30 Soft sensor of lysine fermentation based on tPSO-BPNN Huang Li，Sun Yukun，Ji Xiaofu，Huang Yonghong，Wang Bo (School of Electrical and Information Engineering，Jiangsu University，Zhenjiang 212013，China) Abstract：Lysine fermentation process is a complex nonlinear dynamic strong coupling process．it is very difficult to measure the primary biological parameters on line．However，soft sensing can solve the above problem．Aiming at the disadvantages in BPNN soft sensor，such as falling into local minimum easily，slow convergence speed and weak global search capability，a soft sensor model based Oil tPSO—BPNN is proposed and used for estimating the biological parameters，in which tPSO algorithm is applied to optimize weights and thresholds of BP neural network．Simulation re— suhs show that the proposed model could measure the key parameters on line during the course of lysine fermentation． And the model based on tPSO—BPNN has higher precision and better performance than the model based on BPNN． Key words：extremum disturbed particle swarm optimization(tPSO)；lysine；biochemical parameter；soft sensor 软测量方法有很多种类型 ：大体可分为基于机理建 1 引