Population Balance-monte Carlo Simulation For Gas-to-particle Synthesis Of Nanoparticles And Inverse Problem Methodology

Nanoparticles synthesis via aerosol route has recently attracted the growing interestsof the scientific and industrial communities because it can produce high-puritynanoparticles with specially tailored chemical and physical property, which were theelement for nanomaterials. The process simulation of nanoparticle synthesis via thegas-phase method is essential to understand the detailed dynamic evolution ofnanoparticles within a very short time period under high temperature. The task is,however, very challengeable up to now as the conversion of the gaseous precursor to theend-use nanoparticle is a complex physicochemical process involving nucleation of theparticulate phase, agglomeration between particles and sintering under industrialproduction conditions. What’s more, the frequently used dynamic models may not beeffective in many cases, which imply the significance of identification of effective ones.In this paper, population balance-Monte Carlo method was employed to simulate theevolution of size distribution involved in the nanoparticle preparation process via hightemperature aerosol reactor. The difficulty lies in the several dynamic events involvedand complicated industrial conditions, which were solved by coupling with differentadvanced models, leading to high computational accuracy and at the same time relativelylow CPU time.Coupled with inverse problem methodology, the kinetic model parameters in thesintering model, nucleation model and agglomeration model were identified, making thesimulation results close to the experimental measurements. It was worth noting that alinear relation between two uncertain parameters in the characteristic sintering timemodel was first found which provides a new perspective for the sintering model.