Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to\nsimulate this process experimentally and theoretically. In this work, the simulation of steamdistillation is performed on sixteen sets\nof crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive\nneurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these\nsets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models\nare highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing\nthe performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of\nstate. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method\nindicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods
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