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

M. Nazmul Karim, David Hodge, Laurent Simon

Research output: Contribution to journalArticle

  • 19 Citations

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.

Original languageEnglish (US)
Pages (from-to)1591-1605
Number of pages15
JournalBiotechnology Progress
Volume19
Issue number5
DOIs
StatePublished - Sep 2003
Externally publishedYes

Profile

Fermentation
Industry
Technology
bioprocessing
application methods
neural networks
logging
fermentation
case studies
industry
prediction
monitoring

ASJC Scopus subject areas

  • Food Science
  • Biotechnology
  • Microbiology

Cite this

Data-Based Modeling and Analysis of Bioprocesses : Some Real Experiences. / Karim, M. Nazmul; Hodge, David; Simon, Laurent.

In: Biotechnology Progress, Vol. 19, No. 5, 09.2003, p. 1591-1605.

Research output: Contribution to journalArticle

Karim, M. Nazmul; Hodge, David; Simon, Laurent / Data-Based Modeling and Analysis of Bioprocesses : Some Real Experiences.

In: Biotechnology Progress, Vol. 19, No. 5, 09.2003, p. 1591-1605.

Research output: Contribution to journalArticle

@article{29b93b7c159440ba8656b5d8a09307e5,
title = "Data-Based Modeling and Analysis of Bioprocesses: Some Real Experiences",
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.",
author = "Karim, {M. Nazmul} and David Hodge and Laurent Simon",
year = "2003",
month = "9",
doi = "10.1021/bp015514w",
volume = "19",
pages = "1591--1605",
journal = "Biotechnology Progress",
issn = "8756-7938",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

TY - JOUR

T1 - Data-Based Modeling and Analysis of Bioprocesses

T2 - Biotechnology Progress

AU - Karim,M. Nazmul

AU - Hodge,David

AU - Simon,Laurent

PY - 2003/9

Y1 - 2003/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0142123229&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0142123229&partnerID=8YFLogxK

U2 - 10.1021/bp015514w

DO - 10.1021/bp015514w

M3 - Article

VL - 19

SP - 1591

EP - 1605

JO - Biotechnology Progress

JF - Biotechnology Progress

SN - 8756-7938

IS - 5

ER -