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Multi‐phase analysis framework for handling batch process data
Author(s) -
Camacho José,
Picó Jesús,
Ferrer Alberto
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1151
Subject(s) - partial least squares regression , computer science , process (computing) , principal component analysis , flexibility (engineering) , data mining , process modeling , bilinear interpolation , work in process , machine learning , artificial intelligence , engineering , mathematics , statistics , operating system , operations management , computer vision
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 amongc 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. Copyright © 2008 John Wiley & Sons, Ltd.