Journal article
Integration of first-principle models and machine learning in a modeling framework: An application to flocculation
Department of Chemical and Biochemical Engineering, Technical University of Denmark1
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark2
PROSYS - Process and Systems Engineering Centre, Department of Chemical and Biochemical Engineering, Technical University of Denmark3
Technical University of Denmark4
In this paper, an integrated hybrid modeling approach with first-principles is implemented to model a flocculation process. The application of the framework is demonstrated through a laboratory-scale flocculation case of silica particles in water. In this modeling framework, it is demonstrated that the integration of first-principles models and machine-learning approaches accurately predicts the dynamics of the system.
The first-principles model used in this study incorporates a population balance and mass balance models combined with the kinetic expressions of the agglomeration and breakage phenomena. The prediction of such modeling framework is compared with a fully first-principles model, and moreover with a hybrid model that was developed in a prior work, which used a population balance model as the first principles model and a deep learning algorithm for the determination of the flocculation kinetic parameters.
Language: | English |
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Year: | 2021 |
Pages: | 116864 |
ISSN: | 18734405 and 00092509 |
Types: | Journal article |
DOI: | 10.1016/j.ces.2021.116864 |
ORCIDs: | Nazemzadeh, Nima , Anamaria Malanca, Alina , Fjordbak Nielsen, Rasmus , Gernaey, Krist V. and Soheil Mansouri, Seyed |