Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs

Abstract

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.

Population Balance Models for Pharmaceutical Processes

“…It has been pre-established that with an increase in the dimensionality of the PBM, the computation for the aggregation term increases polynomially [67]. The computational expense associated with calculating the aggregation term has been split or redistributed and the overall solution technique has been sped up by parallelizing the code [67,68].…”