Population balance modeling of volume and time dependent spray fluidized bed aggregation kernel using Monte Carlo simulation results

This paper seeks to extend the work of Hussain et al. (A new framework for population balance modeling of spray fluidized bed agglomeration, Particuology 19 (2015) 141-154) to develop a detailed one-dimensional population balance modeling (PBM) of the spatially homogeneous spray fluidized bed aggregation (SFBA) process. A new mathematical model of the volume and time dependent aggregation kernel is developed based on process specific microscopic mechanisms. In addition, the population balance equations of other important process related parameters (total number of available droplets and size distribution of wet particles) are presented. The developed PBM contains a new mathematical model which estimates the death rate of binder droplets due to the drying mechanism. For the verification of the developed PBM, a constant number Monte Carlo (MC) algorithm is used, which simulates important micro-mechanisms of the SFBA process (droplet addition, droplet drying, volume dependent particle collisions, aggregation, and rebound). The MC algorithm is capable of analyzing the effects of each microscopic event on the aggregation behavior. Volume dependency in particle collisions is used, while mimicking the SFBA process in the MC simulation algorithm. Furthermore, the volume and time dependent probability of successful wet position collisions is successfully extracted using the MC simulations and then transferred to the development of the PBM. Finally, the accuracy of the proposed PBM is verified by comparing its results against the predictions of the MC simulations. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper aims to develop a detailed one-dimensional population balance modeling (PBM) of spray fluidized bed aggregation (SFBA) process by combining mathematical modeling and Monte Carlo algorithm simulation. The accuracy of the proposed PBM is successfully verified through the comparison with Monte Carlo simulations, demonstrating the effectiveness of the developed model in simulating the aggregation behavior.