Computer-aided modeling and simulation are a crucial step in developing, integrating, and optimizing unit operations and\r\nsubsequently the entire processes in the chemical/pharmaceutical industry. This study details two methods of reducing the\r\ncomputational time to solve complex process models, namely, the population balance model which given the source terms can be\r\nvery computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of\r\nmany particulate processes, and its efficient and quick simulation would be very beneficial.The first method illustrates utilization\r\nof MATLAB�s Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up\r\ncomputations on the CPU andGPU, respectively. Results indicate significant reduction in computational time for the same accuracy\r\nusing multicore CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger\r\nproblems despite the limitations of lower clock speed and device memory. This lends credence to the use of highfidelity models (in\r\nplace of reduced order models) for control and optimization of particulate processes.
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