Wafa' H. AlAlaween

TL;DR: A new integrated network based on Artificial Intelligence is proposed to model a high shear granulation (HSG) process, which successfully predicts the properties of the granules produced by HSG, and outperforms also other modelling frameworks in terms of modelling performance and generalization capability.

TL;DR: It is shown that the newly proposed model successfully predicts the granule size and enhances the understanding of the TSG process, and outperforms the standard FLS and the Artificial Neural Network with an overall improvement of approximately 16% and 29% in R2, respectively.

TL;DR: 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, and a new fusion model based on integrating fuzzy logic theory and the Dempster-Shafer theory is developed.

TL;DR: Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their blend concentrations to improve flowability with minimal experimental effort and material resulting in the minimization of the time required.

TL;DR: A new systematic modelling framework which uses machine learning for describing the granulation process is presented, and an interval type‐2 fuzzy model is elicited in order to predict the properties of the granules produced by twin screw granulation (TSG) in the pharmaceutical industry.