State Estimationin Batch Process Based on Two-DimensionalState-Space Model.

Most existing methods for the stateestimation in batch processesare similar to those for continuous processes, and these methods usuallyonly consider the state dynamics within a single batch and ignorethe dynamics across batches. In this paper, the state estimation inbatch processes is investigated based on a two-dimensional state-spacemodel by employing the Bayesian recursive algorithm. In addition tothe dynamics along the time dimension (the dynamics within a singlebatch), the batch process is also characterized by the dynamics alongthe batch dimension (batch-to-batch dynamics). In the proposed method,both the batch-to-batch dynamics and the dynamics within a singlebatch are taken into account. The current state is dependent on theprevious states both along the time dimension and along the batchdimension, so the filtering and smoothing for previous batches shouldbe performed before doing current state estimation. In this way, theinformation on measurements from the previous batches as well as fromthe current batch can be incorporated into the estimation. The proposedmethod is illustrated and evaluated through a simple numerical exampleas well as a simulated two-state batch reaction process. [ABSTRACT FROM AUTHOR]