SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (11): 1801-1810.DOI: 10.1016/j.cjche.2015.09.005

• 过程系统工程与过程安全 • 上一篇    下一篇

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • 收稿日期:2015-02-10 修回日期:2015-07-22 出版日期:2015-11-28 发布日期:2015-12-18
  • 通讯作者: Jianlin Wang
  • 基金资助:

    Supported by the National Natural Science Foundation of China (61503019), the Beijing Natural Science Foundation (4152041) and Beijing Higher Education Young Elite Teacher Project (YETP0504).

Nonlinear state estimation for fermentation process using cubature Kalman filter to incorporate delayed measurements

Liqiang Zhao, Jianlin Wang, Tao Yu, Kunyun Chen, Tangjiang Liu   

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2015-02-10 Revised:2015-07-22 Online:2015-11-28 Published:2015-12-18
  • Contact: Jianlin Wang
  • Supported by:

    Supported by the National Natural Science Foundation of China (61503019), the Beijing Natural Science Foundation (4152041) and Beijing Higher Education Young Elite Teacher Project (YETP0504).

摘要: State estimation of biological process variables directly influences the performance of on-linemonitoring and optimal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithmis provided and the implementation of the proposed algorithmis constructed. Then a nonlinear state spacemodel for fermentation process is established and the SCKF algorithmincorporating delayedmeasurements based on fermentation processmodel is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposedmethod with higher estimation accuracy and better stability.

关键词: Nonlinear state estimation, Fermentation process, Cubature Kalman filter, Delayed measurements, Sample-state augmentation

Abstract: State estimation of biological process variables directly influences the performance of on-linemonitoring and optimal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter (CKF) to incorporate delayed measurements. The square-root version of CKF (SCKF) algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithmis provided and the implementation of the proposed algorithmis constructed. Then a nonlinear state spacemodel for fermentation process is established and the SCKF algorithmincorporating delayedmeasurements based on fermentation processmodel is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-line state estimation for fermentation process can be achieved by the proposedmethod with higher estimation accuracy and better stability.

Key words: Nonlinear state estimation, Fermentation process, Cubature Kalman filter, Delayed measurements, Sample-state augmentation