In recent years the fault detection and isolation (FDI) problems have garnered increasing interest due to concerns of environmental protection, stricter regulations and optimization of processes within industrial settings. The use of data-based methods such as Neural Networks (NNs) for purposes of FDI has received significant research interest in the scientific community. However while these data-driven methods present relatively robust and sensitive means of fault detection and isolation they require large amounts of process data for their training so they can work reliably. Within the chemical industry rather than experimental investigations various modeling techniques such as computational fluid dynamics (CFD) may be used to generate this training data. However many of these methods are computationally expensive and less efficient for producing large data sets. In this paper we wish to present an identification algorithm for the development of compartment models (CM) based on CFD results which can be used to reliably generate process data for FDI purposes in a computationally more efficient way.