Granulation processes can be modeled with the help of population balance models. Numerous studies have considered mechanisms such as coalescence or breakage to predict the evolution of granulation with respect to time. Although these mechanisms play a fundamental role in representing the real behavior of such systems, it has become apparent that the flow pattern of the particles also needs to be modeled. This work investigates granulation with a rotor processor by means of a multiscale model involving a population balance model at the process scale, a compartmental approach that takes into account specific flow behaviors in different zones of the equipment, and a discrete element based model at the particle scale. The population balance model is based on an event-driven Monte-Carlo algorithm and a time-continuous Markov chain to reproduce the particle motion in each compartment. The markov chain properties are obtained from a discrete element simulation of the particle flow dynamics in the whole domain. The results show that the proposed approach improves the accuracy of the population balance model when the particle flow is considered. (c) 2012 Elsevier Ltd. All rights reserved.