(45f) Fast Monte Carlo Algorithm for Heterogeneous Population Balance Models: Effect of Reactor Dissipation Energy Distribution and Blending Time On Metal Nanoparticle Formation

To study the scale-up of particulate processes from laboratory scale to industrial scale, the effects of heterogeneities on the final particle properties needs to be understood. The ability of Monte Carlo, MC, to track individual events/particles and to model complex multidimensional particulate processes makes it a very important tool for this task.  Most of the existing MC strategies are developed for the homogeneous case and very little attention has been given to the heterogeneous case. In this work, I develop efficient time-driven and event-driven Monte Carlo methodologies for the simulation of population balance models under heterogeneous conditions. A new stochastic procedure called particle bundle flow (PBF) is utilized to model the transfer of particles [1]. To simulate the population dynamics inside each cell or compartment, the PEMC and t-PEMC algorithms are utilized [2, 3].  The resulting algorithm is very fast and robust.The algorithm is applied to study the formation of silver nanoparticles [4] in a large reactor utilizing compartment models. The parameters of the compartment models are extracted CFD simulations. The MC ability to track single events was utilized to study the impact of turbulence and stability factor on the generation of large particles. Controlling the production of oversized particles is very important in many areas like printable electronics. This information can be utilized to optimize the industrial reactors.  [1] Irizarry R. (2012)  submitted to  Ind. Eng. Chem. Res..[2] Irizarry R. (2007) Chem. Eng. Sci. 63,  7665-7675.[3] Irizarry R. (2011)  Chem. Eng. Sci. 66, 4059–4069.[4] Irizarry R. (2010)  Ind. Eng. Chem. Res., 49, 5588–5602.