Data driven approaches to modeling and analysis of bioprocesses: some industrial examples

Data-generated models find numerous applications in areas where the speed of collection and logging data surpasses the ability to analyze it. This work 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 networks 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 three original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.