Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and subsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely, the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes, and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB's Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU, respectively. Results indicate significant reduction in computational time for the same accuracy using multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in place of reduced order models) for control and optimization of particulate processes. 1. Introduction Modeling and simulation are powerful tools universally employed in designing, analyzing, and controlling particulate processes. These particulate processes such as crystallization, granulation, milling, and polymerization are some of the major unit operations carried out in the manufacture of bulk commercial products like pharmaceuticals, detergents, fertilizers, and polymers. Research work focusing on the modeling and simulation of these particulate processes, specifically those involving granular materials, has been growing at a steady pace over the last few decades [1–3]. This is a significant achievement in itself, considering the fact that these systems are inherently dynamic in behavior and are driven by complex microscale phenomena [4]. Although the underlying mechanisms of such processes are yet to be thoroughly grasped, granulation, a particle design process, is one such area where substantial progress has been made over the years [5]. The approaches for modeling such systems are as numerous as they are varied: Discrete Element Modeling (DEM) [6], Population Balance Modeling (PBM) [3, 7–13], hybrid models by combining PBM with DEM [14], PBM with Volume of Fluid (VoF) methods [15], and PBM with Computational Fluid Dynamics (CFD) [16], to name a few. Of the aforementioned, the most widely used are the DEM and PBM methods. Population
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