Population Balances: Theory and Applications to Particulate Systems in Engineering

By D. Ramkrishna. 2000

1,356 citations recorded by the source corpus.

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Citations in the corpus

Population balance modelling of polydispersed particles in reactive flows (2010, methodology): “Further details of analytical and perturbation methods can be found in the review of Drake [53] as well as in the book by Ramkrishna [194].”
Simulations of Population Balance Systems with One Internal Coordinate using Finite Element Methods (2009, background): “where Cnuc [kmol/m3] denotes amodel nucleation constant, d̃p,0 [m] the smallest particle diameter (the nuclei size) and the nucleation rate B̃nuc(c̃C) [1/(m 3s)] is modeled by (Ramkrishna, 2000)”
Numerical solution of the spatially distributed population balance equation describing the hydrodynamics of interacting liquid–liquid dispersions (2004, methodology): “Stochastic methods Stochastic approaches as di8erentiated from >nite di8erence methods are simulation techniques designed to arti>cially realize the system behavior through the generation of random numbers used for the identi>cation of the probability functions governing the system behavior (Ramkris”
Part I: Dynamic evolution of the particle size distribution in particulate processes undergoing combined particle growth and aggregation (2004, methodology): “The time evolution of the PSD is commonly obtained from the solution of the general population balance equation (PBE) governing the dynamic behavior of a particulate process (Hulburt and Katz, 1964; Ramkrishna, 2000).”
A volume‐consistent discrete formulation of aggregation population balance equations (2016, methodology): “On apparatus and plant level, the population balance framework [4] is used to study the dynamics of aggregate formation.”
DETERMINATION OF BREAKAGE RATES USING SINGLE DROP EXPERIMENTS (2012, methodology): “A more detailed discussion of the population balance in general can be found in Ramkrishna (2000).”
Mathematical Modeling of Microbial Community Dynamics: A Methodological Review (2014, background): “Internal states of cells most frequently considered in PBMs include the variables such as cell mass, age, and morphology that should be specified to determine birth and death processes or the rate of change of certain variables of interest [118].”
High resolution algorithms for multidimensional population balance equations (2004, methodology): “This has motivated many researchers to develop specialized algorithms for solving population balance equations (for example, see Ramkrishna, 1985; Nicmanis and Hounslow, 1998; Ramkrishna, 2000, and citations therein).”