计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (1): 239-243.

• 工程与应用 • 上一篇    下一篇

基于Marquardt和Runge-Kutta算法的发酵过程建模

刘登峰1,徐  玲1,2,熊伟丽1,姜丽华1,张洪涛3,徐保国1   

  1. 1.江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
    2.无锡商业职业技术学院 物联网技术学院,江苏 无锡 214153
    3.江南大学 工业生物技术教育部重点实验室,江苏 无锡 214122
  • 出版日期:2016-01-01 发布日期:2015-12-30

Fermentation process modeling with Marquardt algorithm and Runge-Kutta algorithm

LIU Dengfeng1, XU Ling1,2, XIONG Weili1, JIANG Lihua1, ZHANG Hongtao3, XU Baoguo1   

  1. 1.School of Internet of Things, Jiangnan University, Wuxi, Jiangsu 214122, China
    2.School of Internet of Things Technology, Wuxi Institute of Commerce, Wuxi, Jiangsu 214153, China
    3.School of Biological Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2016-01-01 Published:2015-12-30

摘要: 利用简单的线性模型很难描述发酵这类复杂的非线性动态过程,因此需要利用非线性方法对该类过程进行建模。为此,提出了利用基于Marquardt算法的非线性回归方法和基于四阶Runge-Kutta算法的非线性微分方程求解方法对发酵过程进行建模分析;并进一步利用统计方法分析了该非线性回归方法的有效性。该方法应用于黄酒发酵过程中,实现了黄酒发酵过程模型的求解和模型参数的动态优化。

关键词: Marquardt算法, Runge-Kutta算法, 黄酒发酵, 过程建模

Abstract: It is hard to describe complex nonlinear dynamic fermentation process by using a simple linear model. Thus nonlinear modeling is used to examine fermentation progress. To this end, a least-square based algorithm Marquardt algorithm is used to estimate model parameters from experimental data. A model of nonlinear differential equations is solved by Runge-Kutta algorithm. Both Marquardt and fourth Runge-Kutta methods are applied for optimizing parameter values for fermentation model. Regression tests are conducted and results indicate that the model can simulate the rice wine fermentation process with [R2] higher than 0.89.

Key words: Marquardt algorithm, Runge-Kutta algorithm, rice wine fermentation, process modeling