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Integrating the physics with data analytics for the hybrid modeling of the granulation process
Author(s) -
AlAlaween Wafa' H.,
Mahfouf Mahdi,
Salman Agba D.
Publication year - 2017
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15831
Subject(s) - process (computing) , computer science , population , granulation , fuzzy logic , data mining , artificial intelligence , engineering , demography , geotechnical engineering , sociology , operating system
A hybrid model based on physical and data interpretations to investigate the high shear granulation (HSG) process is proposed. This model integrates three separate component models, namely, a computational fluid dynamics model, a population balance model, and a radial basis function model, through an iterative procedure. The proposed hybrid model is shown to provide the required understanding of the HSG process, and to also accurately predict the properties of the granules. Furthermore, a new fusion model based on integrating fuzzy logic theory and the Dempster‐Shafer theory is also developed. The motivation for such a new modeling framework stems from the fact that integrating predictions from models which are elicited using different paradigms can lead to a more robust and accurate topology. As a result, significant improvements in prediction performance have been achieved by applying the proposed framework when compared to single models. © 2017 American Institute of Chemical Engineers AIChE J , 2017