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This book systematically explores advanced granulation technologies and their interplay with physicochemical properties in pharmaceutical manufacturing. Using a model-driven approach, it integrates computational fluid dynamics (CFD), discrete element modeling (DEM), and artificial intelligence/machine learning (AI/ML) to optimize process parameters for high drug-loaded formulations. Case studies on high shear seeded granulation and fluidized bed layering granulation demonstrate improved mechanical properties, dissolution profiles, and production yields. The work bridges theoretical modeling with industrial applications, offering actionable insights for developing robust, scalable manufacturing processes. A dedicated chapter on AI-driven digital twins highlights future directions for real-time process control.
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Engleski
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