Reconstruction-based Monte Carlo method for accurate and efficient breakage simulation

Monte Carlo (MC) methods have been proven effective in population balance modeling; yet, in dealing with par-ticle breakage problems, their performances in terms of statistical noise and computational efficiency still de-mand further improvement. In this work, we propose a time-driven MC method based on weighted particles that are reconstructed periodically by matching the shape of the number density function, which is defined on an adaptive subdomain mesh. By accounting for the weighted particles of all subdomains in a global sense rather than only a selected few in the traditional ways, this solver yields promising agreement with the analytical solu-tions of both binary-and multi-breakage cases. Compared with existing methods, the proposed method is able to resolve the particle size distribution as well as moments with much lower statistical noise, and is more suitable for long-time simulations. Moreover, its accuracy is less dependent on the weighted particle number or time in-crement. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The proposed time-driven Monte Carlo method based on weighted particles shows promising agreement with analytical solutions for both binary and multi-breakage cases, resolving particle size distribution with lower statistical noise and being suitable for long-time simulations. Its accuracy is less dependent on weighted particle number or time increment.