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Particulate processes involving handling of solids are ubiquitous in various industries and are typically operated inefficiently due to a lack of adequate mechanistic process understanding. A primary application of such processes is found in the pharmaceutical industry that involve manufacturing of solid dosage forms (e.g. tablets). Granulation is a critical particulate process that plays an important role in the overall manufacture of pharmaceutical drugs. This work focuses on improved mathematical modeling of granulation processes using the population balance framework. One of the primary objectives deals with model development for granulation followed by development of improved numerical techniques for reduced computational overheads associated with the solution techniques for solving population balance models. This study is also aimed at identifying the influence of various operating parameters on the final granule properties through the development of a novel semi-mechanistic kernel and an integrated mesoscale model framework that can effectively capture key granulation dynamics. A compartmentalized model has also been formulated for a high-shear wet granulator that can capture the inhomogeneities (with respect to liquid and particle velocity) within the vessel. This integrated, compartment-based model can be further extended for open loop optimization and control of the granulation process, which can aid at obtaining an optimal recipe for the operation of the granulation process. This work will make a significant contribution towards improved understanding of the granulation process and is aimed at mitigating the current inefficient operation of the process.