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.