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Data‐Based Modeling and Analysis of Bioprocesses: Some Real Experiences
Author(s) -
Karim M. Nazmul,
Hodge David,
Simon Laurent
Publication year - 2003
Publication title -
biotechnology progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.572
H-Index - 129
eISSN - 1520-6033
pISSN - 8756-7938
DOI - 10.1021/bp015514w
Subject(s) - bioprocess , context (archaeology) , computer science , biochemical engineering , artificial neural network , principal component analysis , data collection , data science , process engineering , systems engineering , machine learning , artificial intelligence , engineering , statistics , mathematics , paleontology , chemical engineering , biology
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.