Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process.
The results presented in this article demonstrate the potential of artificial intelligence tools for predicting the endpoint of the granulation process in high-speed mixer granulators of different scales from 25L to 600L. The combination of neurofuzzy logic and gene expression programing technologies allowed the modeling of the impeller power as a function of operation conditions and wet granule properties, establishing the critical variables that affect the response and obtaining a unique experimental polynomial equation (transparent model) of high predictability (R2> 86.78%) for all size equipment. Gene expression programing allowed the modeling of the granulation process for granulators of similar and dissimilar geometries and can be improved by implementing additional characteristics of the process, as composition variables or operation parameters (e.g., batch size, chopper speed). The principles and the methodology proposed here can be applied to understand and control manufacturing process, using any other granulation equipment, including continuous granulation processes.
Departamento de Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain. Electronic address: m.landin@usc.es.
Mariana
M
Landin
D000465:Algorithms; D001185:Artificial Intelligence; D004339:Drug Compounding / Q000379:methods*; D007785:Lactose / Q000737:chemistry; D010316:Particle Size; D011208:Powders; D013213:Starch / Q000737:chemistry; D013607:Tablets
D011208:Powders; D013607:Tablets; D013213:Starch; D007785:Lactose
granulation; simulations; solid dosage form; tableting; wetting