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The Scientific World Journal
Volume 2014, Article ID 246589, 8 pages
http://dx.doi.org/10.1155/2014/246589
Research Article

Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

Petroleum Department, Petroleum University of Technology, P.O. Box 6198144471, Ahwaz, Iran

Received 27 February 2014; Revised 15 March 2014; Accepted 29 March 2014; Published 28 April 2014

Academic Editor: Nirupam Chakraborti

Copyright © 2014 Amin Daryasafar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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