Integrating the physics with data analytics for the hybrid modeling of the granulation process. Issue 11 (23rd June 2017)

• Record Type: Journal Article
• Authors: AlAlaween, Wafa' H.; Mahfouf, Mahdi; Salman, Agba D.
• Is Part Of: AIChE journal. Volume 63:Issue 11(2017)
• Page Start: 4761
• Page End: 4773
• Publication Date: 2017-06-23
• Subjects: hybrid model -- data fusion -- fuzzy logic -- Dempster‐Shafer theory -- high shear granulation; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.15831
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract : 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