The behaviour of real particulate processes, among them crystallization, is determined by the interaction of multiple process phenomena, which all have to be modelled to fully describe the process. The traditional way of modelling only accounts for the process kinetics under the assumption of ideal mixing. However, often this is not sufficient to obtain a rigorous process description. In order to consider an anisotropic flow field in the crystallizer, the fluid dynamics has to be modelled. Different approaches to solve the corresponding simulation problem are discussed in the literature (Kramer et al., 2000; Bermingham, 2003; Marchisio et al., 2003; Wang and Fox, 2004). If micro-mixing is of a minor concern, as it is in cooling or evaporation crystallization, a compartmental approach can be applied. The crystallizer is subdivided into interconnected ideally mixed compartments. The different length scales of the computational fluid dynamics (CFD) grid and the compartments require the use of scale integration methods (aggregation, disaggregation). Aggregation methods provide the averaging of the local properties (temperature and so on) to use them for the computation of crystallization kinetics in the compartments. Disaggregation methods, here based on Gaussian smoothing or on spline interpolation, are needed to update the properties on the CFD grid. To exploit the full benefit of current commercial simulation technology, the process phenomena are implemented in appropriately selected specialized software tools. The full process description is then obtained by a software-technological integration. In this study, FLUENT and Parsival are used for the modelling of fluid dynamics and crystallization, respectively. During the coupled simulation the problem is represented by a flowsheet and is solved by means of an iterative modular simulation algorithm. The presented method is evaluated for an exemplary crystallization process. The implemented framework for the multi-scale coupled simulation enables a rigorous description of the crystallization process accounting for an anisotropic flow field. The software-technological coupling approach allows the distribution of the computational load on specialized software.