Monitoring of batch processes through state-space models

Abstract

This paper will focus on the development of an improved and more general monitoring framework for batch processes. A subspace identification method will be used to extract “within-batch” and “between-batch” correlation information from historical operation data in the form of a state-space model. A simple monitoring procedure can be formed around the state and residuals of the model using scalar statistical metrics. The proposed state-space monitoring framework will deliver a powerful alternative over existing multivariate methods. Since the “between-batch” correlation is modeled explicitly, the algorithm is likely to be more effective than the traditional multivariate methods for detecting small mean shifts, slow drifts, and changes in the correlation structure. The framework is general enough to allow for several formulations, including an off-line formulation, an on-line formulation, or a more flexible formulation that allows for the use of both on-line and off-line information in a single framework.