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On the solution of bivariate population balance equations for aggregation: X–discretization of space for expansion and contraction of computational domain

Authors
Journal
Chemical Engineering Science
0009-2509
Publisher
Elsevier
Publication Date
Volume
70
Identifiers
DOI: 10.1016/j.ces.2011.10.005
Keywords
  • Population Balance
  • Particulate Processes
  • Mixing
  • Agglomeration
  • Mathematical Modelling
  • Discretization Methods
Disciplines
  • Computer Science

Abstract

Abstract A new structured discretization of 2D space, named X-discretization, is proposed to solve bivariate population balance equations using the framework of minimal internal consistency of discretization of Chakraborty and Kumar [2007, A new framework for solution of multidimensional population balance equations. Chem. Eng. Sci. 62, 4112–4125] for breakup and aggregation of particles. The 2D space of particle constituents (internal attributes) is discretized into bins by using arbitrarily spaced constant composition radial lines and constant mass lines of slope −1. The quadrilaterals are triangulated by using straight lines pointing towards the mean composition line. The monotonicity of the new discretization makes is quite easy to implement, like a rectangular grid but with significantly reduced numerical dispersion. We use the new discretization of space to automate the expansion and contraction of the computational domain for the aggregation process, corresponding to the formation of larger particles and the disappearance of smaller particles by adding and removing the constant mass lines at the boundaries. The results show that the predictions of particle size distribution on fixed X-grid are in better agreement with the analytical solution than those obtained with the earlier techniques. The simulations carried out with expansion and/or contraction of the computational domain as population evolves show that the proposed strategy of evolving the computational domain with the aggregation process brings down the computational effort quite substantially; larger the extent of evolution, greater is the reduction in computational effort.

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