(47e) Efficient Simulation of Population Balance Models Via Parallel and Distributed Computing

Computer-aided modelling and simulation is a crucial step in developing, integrating and optimizing unit operations and subsequently, entire processes, in the Chemical/pharmaceutical industry. Industries actively involved in the modelling and simulation of unit operations like granulation, blending or crystallization, are increasingly recognizing the need for minimizing simulation run-times of models that are computationally intensive. These simulations typically involve multiple iterations over several increasing or different data sets for prolonged time periods. MATLAB Parallel and Distributed Computing [1] and the Graphics Processing Unit (GPU) Toolboxes affords a convenient way of speeding up these calculations by allowing the programmer to fully exploit the parallel processing capabilities of today’s multi-core multi-processor desktops.

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 the parallel/distributed computing toolbox and the second method makes use of GPU computing to speed up simulations. Results show significant reduction in computational time for the same accuracy. This lends credence to the use of high-fidelity models (in place of reduced order models) for control and optimization of particulate processes.

 

References

1. Piotr Luszczek: Parallel Programming in MATLAB. IJHPCA 23(3): 277-283 (2009)