摘要
发酵动力学主要研究发酵过程中菌体生长、产物合成和底物消耗之间的关系,对发酵过程的调控及发酵规模的放大都有着重要的指导意义。目前微生物发酵动力学一般由菌体生长动力学、产物生成动力学和底物消耗动力学三部分组成,但是其模型绝大多数都是非线性,参数拟合难度大。目前常用的估算方法有线性转化拟合、非线性拟合和遗传算法拟合法。本文首先总结目前国内外分批发酵中常用的数学模型及其表达式,然后通过实例并结合软件编程详细的介绍了这3种方法的软件实现方法,并且比较3种方法拟合效果。结果表明线性转化法拟合误差较大,非线性和遗传算法拟合效果较好,但遗传算法能以较大概率逼近全局最优,而非线性拟合法则容易陷入局部最优。
The fermentation kinetics mainly focus on the relationship among cell growth,product synthesis and substrate consumption,which play a significant guiding on the regulation of the fermentation process and enlarging the scale of fermentation.Fermentation kinetics consists of cell growth kinetics,production kinetics and substrate consumption kinetics.Most of dynamic models for microbial fermentation are so non-linear that the regression of models parameters is difficultly obtained.The current methods are used by a linear,non-linear and genetic algorithm fitting.The paper summarizes the current mathematical model and its expression used in batch fermentation,and then three methods were used with the help of software programming to estimate the parameters of kinetics combined with the examples.The results show that linear transformation method has a larger fitting error,non-linear and genetic algorithms methods can provide considerable fitting results,and the genetic algorithm has a larger probability to approach the global optimum while the non-linear method is easily trapped in local optimum.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2011年第4期437-441,共5页
Computers and Applied Chemistry
基金
南昌大学"赣江学者奖励计划"项目
食品科学与技术国家重点实验室探索项目(SKLF-TS-200817)
关键词
发酵动力学
线性回归
非线性回归
遗传算法
fermentation kinetics
linear regression
non-linear regression
genetic algorithms
