Bayesian method for state estimation of batch process with missing data

Formulated under the state space framework, most previous methods for the state estimation typically treated batch processes in the same way as continuous ones, and only considered the state transition within a single batch. Considering that the initial state of the current batch is often related to that of the previous one, this paper incorporates the information of the previous batches into the estimation of the current state, where the filtering and smoothing for the previous batch is implemented and then the initial state of the current batch is estimated by treating the smoothed initial state of the previous batch as a “measurement”. To deal with the nonlinear and non-Gaussian property of batch processes, the particle filter method is employed as the key algorithm for filtering and smoothing. In addition, in order to make full use of various measurements, the case of missing data is considered during the implementation of the particle filter algorithm. The proposed method is illustrated and evaluated through the simulation on a penicillin fed-batch fermentation process.

► Particle filtering approach for state estimation of batch processes with missing data.
► State estimation of batch processes with consideration of batch correlation.
► Estimation of initial states via batch correlation.
► Evaluation of the proposed method through a simulated penicillin fed-batch fermentation process.