Multi-phase analysis framework for handling batch process data

摘要

Principal component analysis (PCA) and partial least squares (PLS) are bilinear modelling tools which have been successfully applied to three-way batch process data for monitoring and quality prediction. Most modelling approaches in the literature are based on a fixed model structure. The approach proposed in this paper, named the Multi-phase (MP) analysis framework, provides the flexibility to adjust the model structure to the dynamic nature of the process under study. The existence of several phases, with dynamics of different order and changes in the correlation structure among variables, is effectively identified. This adjustment of the model structure to the features of the process yields performance improvements in several applications, such as the on-line monitoring and final quality prediction, as shown when comparing the MP models with various well-established modelling approaches. Also, the MP approach provides a set of valuable tools for process understanding and data handling. Data from two processes, a fermentation process and a waste-water treatment process, are used to illustrate the capabilities of the proposed modelling framework.