A novel, structure‐tracking monte carlo algorithm for spray fluidized bed agglomeration

Abstract

A stochastic modeling approach to fluidized bed agglomeration is presented. First, the fundamentals of a general Monte Carlo algorithm are discussed, namely the correlation of real with simulation time and the size of the simulation box. Two different approaches regarding the morphological description of particle structure development are then compared for different theoretical test cases. The first approach (concept of positions) is a fast and effective geometrical discretization to follow properties such as primary particle number per agglomerate throughout the simulation. The second approach is a new three‐dimensional (3‐D) structure algorithm, which allows a complete, unrestricted description of the spatial evolution of particles related to physical microprocesses. The 3‐D algorithm enables to apply distributed particle and droplet sizes, which is not possible in the concept of positions. In the perspective, the structural growth of particles can be connected to process conditions, opening new possibilities for process and product design. © 2011 American Institute of Chemical Engineers AIChE J, 58: 3016–3029, 2012

A new framework for population balance modeling of spray fluidized bed agglomeration

“…However, in the CNMC method, as explained by Dernedde et al (2011Dernedde et al ( , 2012, only one among all possible events (collisions and droplet depositions) is simulated in a single time step. The number of particles is regulated in each time step.…”

“…during the simulation. In the recent works of Terrazas-Velarde et al (2009, 2011a, 2011b, Dernedde et al (2011), andTsotsas (2012), two different MC methods, namely the constant volume and the constant number method, have been utilized, respectively. Therefore, it is important to briefly discuss the appropriateness of both methods prior to choosing one of them for simulating the SFBG process.…”

Modeling of layering growth processes using a Monte Carlo approach

“…The value of the time step follows from (4), presented by Dernedde et al (2012), assuming exponentially distributed time steps. The variable r represents a uniformly distributed random number from the interval ]0, 1[ (excluding 0 and 1).…”

“…More details regarding implementation details are given by Dernedde et al (2012), who used a Monte-Carlo approach to investigate spray fluidized bed agglomeration.…”

“…Another modeling approach, which can be applied to particulate growth processes, is based on a Monte Carlo method, see van Peborgh Gooch and Hounslow (1996), Kruis et al (2000), Terrazas-Velarde et al (2011), Dernedde et al (2012) and Zhao et al (2007). In this method the evolution of a finite sample of a real particle population due to a sequence of micro-processes is tracked.…”