Data-Based Modeling and Analysis of Bioprocesses: Some Real Experiences

Document Type

Article

Publication Date

9-1-2003

Abstract

Data-generated models find numerous applications in areas where the speed of collection and logging of data surpasses the ability to analyze it. This work is meant to addresses some of the challenges and difficulties encountered in the practical application of these methods in an industrial setting and, more specifically, in the bioprocess industry. Neural network and principal component models are the two topics that are covered in detail in this paper. A review of these modeling technologies as applied to bioprocessing is provided, and four original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.

Identifier

0142123229 (Scopus)

Publication Title

Biotechnology Progress

External Full Text Location

https://doi.org/10.1021/bp015514w

ISSN

87567938

PubMed ID

14524724

First Page

1591

Last Page

1605

Issue

5

Volume

19

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