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J4 ›› 2009, Vol. 44 ›› Issue (7): 71-78.

• 论文 • 上一篇    下一篇

微生物批式流加发酵的建模及基于HPSO算法的参数辨识

宫召华1,2, 刘重阳1, 冯恩民2   

  1. 1. 山东工商学院数学与信息科学学院, 山东 烟台 264005;
    2. 大连理工大学数学学院, 辽宁 大连 116024
  • 收稿日期:2008-12-15 出版日期:2009-07-16 发布日期:2009-11-01

Modeling in fedbatch fermentation and its parameter 
identification based on HPSO algorithm

GONG Zhaohua1,2, LIU Chongyang1, FENG Enmin2   

  1. 1. School of Mathematics and Information Science, Shandong Institute of Busines s and Technology, Yantai 264005, Shandong, China; 2. School of  Mathematics, Dalian University of Technology, Dalian 1 16024, Liaoning, China
  • Received:2008-12-15 Online:2009-07-16 Published:2009-11-01
  • About author:GONG Zhaohua(1978-), female, Ph.D. candidate, major in operational research and control theory. Email: yt-gzh@yahoo.com.cn
  • Supported by:

    Supported by National Science Foundatio

    n of Ch

    ina (10871033, 10671126) and National Basic Research Program of China (2007AA02Z

    208)

摘要:

研究了微生物批式发酵甘油生产1,3丙二醇过程的建模和参数辨识。由于流加过程中甘油和碱被间断地注入发酵罐,因而本文提出一个非线性多阶段动力系统描述该过程,并讨论了该系统的性质。以实验数据和计算值之间误差平方和最小为性能指标,建立了系统辨识模型,并证明了参数的可辨识性。最后构造了混杂粒子群算法求解该参数辨识模型,数值结果表明实验观测值和计算值之间的误差比已有文献降低了176%~4177%,因而该系统能更好的描述批式流加发酵过程。

关键词: 建模;参数辨识;粒子群优化;1,3丙二醇;批式发酵

Abstract:

Modeling and its parameter identification are investigated in glycerol bioconversion to 1,3propanediol (1,3PD) by Klebsiella pneumoniae (K.pneumoniae) in fedbatch cultures. Considering the discontinuity of adding glyceroland alkali in the process, a nonlinear multistage dynamical system is presented to formulate the process. Taking the minimal errors between the experimental data and calculated values as the performance index, we propose an identification model and prove the existence of optimal kinetic parameters. Finally, a hybridparticle swarm optimization (HPSO) algorithm is constructed to solve the identification model. Numerical results show that the error is reduced by 176%~4177% and the proposed dynamical system can better formulate the fedbatch culture.

Key words: modeling; parameter identification; particle swarm optimizati on; 1,3propanediol; fedbatch fermentation

中图分类号: 

  • O17514
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