Pattern matching in historical batch data using PCA

Abstract

The article seeks to answer the question: how can relevant information be extracted from huge historical databases? A pattern-matching methodology has been evaluated in a case study for a batch fermentation process. The proposed approach is both data driven and unsupervised. The new approach relies on PCA and a new similarity factor based on distance between the two datasets. The computational requirements are modest, allowing large databases to be processed in a relatively small amount of time.