459119 Cyberinfrastructure Enabled Parallelization of Population Balance Models for Efficient Simulation of Granulation Processes

Tuesday, November 15, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Anik Chaturbedi1, Franklin Bettencourt1, Srinivas Mushnoori1, Subhodh Karkala1, Shantenu Jha2, Marianthi Ierapetritou1 and Rohit Ramachandran1, (1)Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, (2)Department of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ

Cyberinfrastructure Enabled Parallelization of Population Balance Models for Efficient Simulation of Granulation Processes

Anik Chaturbedi, Franklin Bettencourt, Srinivas Mushnoori, Subhodh Karkala, Shantenu Jha, Marianthi Ierapetritou, Rohit Ramachandran 

Rutgers, the State University of New Jersey, Piscataway, NJ, USA 08854

Particulate processes, in general, are complex to understand and control efficiently. Granulation is one of the most widely used particulate process and is used to produce commercial products such as pharmaceuticals, catalysts, fertilizers etc. In granulation, multiple rate processes, such as aggregation, breakage, liquid addition, nucleation, consolidation, layering take place simultaneously. The incorporation of these multi-dimensional physics is a challenge in any model formulation. Previous research has shown that a population balance model (PBM) can be used to simulate granulation processes and to predict the evolution of macroscopic properties of the system such as particle size distribution (PSD), average composition and porosity. Due to the multitude of dimensions present and the associated integrals, population balance models can become computationally intensive. Moreover, for modeling continuous granulation, additionally, spatial variation of particle properties has to be incorporated in already complex population balance models [1]. This increases the computational cost even more. To tune these models for accurately simulating real systems and eventually move towards real-time prediction and control of these processes we will need to massively speed up these simulations. Some previous research has been done on the parallelization of PBMs using either the MATLAB Distributed Computing Toolbox [2] or Message Passing Interface (MPI) [3].

In this work, a 3D PBM (distributed solid volume, lumped liquid and gas volumes) and two additional spatial dimensions has been parallelized using a simpler Message Passing Interface (MPI) approach and a more complex hybrid MPI+OpenMP approach. The speed up and scale up of these parallelization techniques have been studied. Also, a pilot job system, RADICAL-Pilot has been used to efficiently parallelize and distribute computational work among different computational units in a National Science Foundation-supported distributed computing resource, Stampede and Rutgers University School of Engineering High Performance Computer (SOE HPC). Parallel simulation of PBMs through RADICAL-Pilot will not only aid in more efficient and user friendly implementation of these parallel PBMs but also enable coupling of PBMs with micro-physics models such as discrete element models (DEM) to increase the accuracy of PBMs and accurately capture the multi-scale dynamics of complex particulate processes. All codes were compiled with the ‘–ofast’ option. Initial runs show a 40 times speedup for the hybrid code compared to the serial one on 128 cores on 8 nodes. Also the hybrid code resulted in 80% less memory usage compared to the MPI code, which presents an advantage in simulating larger systems with same computing resources. This work will eventually lead to the incorporation of these fast and more mechanistic population balances in real-time applications such as control and optimization.

  References

[1]

D. Barrasso, S. Walia and R. Ramachandran, "Multi-component population balance modeling of continuous granulation processes: A parameteric study and comparison with experimental trends," Powder Technology, pp. 85-97, 2013.

[2]

A. V. Prakash, A. Chaudhury, D. Barrasso and R. Ramachandran, "Simulation of population balance model-based particulate processes via parallel and distributed computing," Chemical Engineering Research and Design, pp. 1259-1271, 2013.

[3]

R. Gunawan, I. Fusman and R. D. Braatz, "Parallel High-Resolution Finite VolumeSimulation of Particulate Processes," AIChE Journal, pp. 1449-1458, 2008.

 


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