Abstract: In batch processes, the key parameters were usually obtained online with low accuracy or offline with large time delay, and a state estimation algorithm was proposed to estimate the key parameters by incorporating delayed measurements with the real-time measurements. Due to the different sampling intervals of these two kinds of measurements, two cases were analyzed, including the case of only real-time measurements available and the case of both real-time and delayed measurements available. Considering the nature of nonlinearity and non-Gaussianity in batch processes, the particle filter algorithm was introduced for the state estimation, and it was further extended by the Bayesian method for the information fusion of these two kinds of measurements. Finally, the proposed method was applied in the beer fermentation process, and the experimental result shows that the proposed method performs well in the state estimation through incorporation of delayed measurements.

Key words: batch fermentation, state estimation, delayed measurements, particle filtering, Bayesian method, forward-backward smoothing