State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal control for fermentation process. A novel nonlinear state estimation method for fermentation pro...State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal 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 CI（F （SCKF） algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model 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-fine state estimation for fermentation process can be achieved by the proposed method with higher esti- mation accuracy and better stability.展开更多
基金Supported by the National Natural Science Foundation of China(61503019)the Beijing Natural Science Foundation(4152041)Beijing Higher Education Young Elite Teacher Project(YETP0504)
文摘State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal 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 CI（F （SCKF） algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model 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-fine state estimation for fermentation process can be achieved by the proposed method with higher esti- mation accuracy and better stability.
