Due to the complexity of hydrodynamics and mass transfer phenomena involved in rotating disk contactors, plus the lack of accurate information of crude oil composition, simulation of a propane deasphalting plant in a common process simulator can be a very difficult task. Two different approaches are presented in this work, aiming the development of a reliable simulation model for propane deasphalting rotating disc contactor in a chemical process simulator. The first approach consists in the development of a black box-type model, based on feedforward artificial neural networks (FANN) to predict the RDC performance. The second one involves a recharacterisation of vacuum residue feed and a simulation of the RDC with the process simulator algorithms. Comparing the results for both approaches with real operating data, a maximum relative error of 9% was found in the FANN predictions and a maximum relative error of 4.5% was found in the second approach, showing that both have good predictive performances. For this particular case and concerning the objectives of this research, the K-UOP methodology, based on fundamental chemistry, is more suitable than FANN approach because it can be applied in a wider range of operating conditions of the extraction unit. However, in similar extraction problems with a wider range of process variables as training data set, the FANN could be an alternative methodology due to its simplicity and easy use in simulation processes.