Data-driven modeling and optimal control of the production of Fructo-Oligosaccharides by Aureobasidium Pullulans. (September 2019)

• Record Type: Journal Article
• Authors: Fekih-Salem, R.; Schorsch, J.; Dewasme, L.; Castro, C.; Hantson, A.-L.; Kinnaert, M.; Vande Wouwer, A.
• Is Part Of: Journal of process control. Volume 81(2019)
• Page Start: 136
• Page End: 149
• Publication Date: 2019-09
• Subjects: Mathematical modeling -- Parameter estimation -- Identifiability -- Maximum likelihood principal component analysis -- Optimal control -- Prontryagin maximum principle; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.07.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Dynamic model of FOS production by Aureobasidium Pullulans . Minimum number of reactions using MLPCA. Parameter identifiability using generating series. Elegant optimization by Pontryagin principle. Optimization validation using nonlinear programming. Abstract: The first objective of this study is to derive a macroscopic dynamic model of the production of Fructo-Oligosaccharides (FOS) by Aureobasidium pullulans based on sets of experimental data collected from batch and fed-batch cultures. The model should be of low dimension, so as to be identifiable based on the available data, and so as to be suitable for optimization and control purposes. A maximum likelihood principal component analysis is used to determine the appropriate number of reactions and the corresponding stoichiometry. Further, products of Monod factors are chosen to describe the reaction kinetics. The model parameters are estimated using a weighted least-squares method, and model simplification achieved by eliminating parameters associated to large uncertainties, are performed in a step-by-step, systematic way. In addition, the model structural identifiability is confirmed using generating series and the software GenSSI. Identification is successfully achieved, leading to satisfactory direct and cross-validation results. The second objective is to exploit the model and to maximize the FOS concentration at an a priori undetermined time using Pontryagin maximum principle. The optimal feed rate is in the form of a bang-bang control, which is easily implemented in practice.