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Modelling and Simulation in EngineeringVolume 2013 2013, Article ID 475478, 16 pages

Research ArticleDepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA

Received 15 October 2012; Revised 12 February 2013; Accepted 19 February 2013

Academic Editor: Weizhong Dai

Copyright © 2013 Anuj V. Prakash et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.

Author: Anuj V. Prakash, Anwesha Chaudhury, and Rohit Ramachandran



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