Data-based modeling and analysis of bioprocesses: some real experiences.
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.
Karim
M Nazmul
MN
Department of Chemical Engineering, Colorado State University, Fort Collins, Colorado 80523, USA. karim@engr.colostate.edu
Hodge
David
D
Simon
Laurent
L
D000465:Algorithms; D001185:Artificial Intelligence; D018407:Bacterial Physiological Phenomena; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D004798:Enzymes / Q000096:biosynthesis; D005246:Feedback; D007218:Industrial Microbiology / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D011994:Recombinant Proteins / Q000096:biosynthesis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes
D004798:Enzymes; D011994:Recombinant Proteins