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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Prediction of sour gas compressibility factor using an intelligent approach
Fuel Processing Technology, Volume 116, Year 2013
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Description
Compressibility factor (z-factor) values of natural gasses are essential in most petroleum and chemical engineering calculations. The most common sources of z-factor values are laboratory experiments, empirical correlations and equations of state methods. Necessity arises when there is no available experimental data for the required composition, pressure and temperature conditions. Introduced here is a technique to predict z-factor values of natural gasses, sour reservoir gasses and pure substances. In this communication, a novel mathematical-based approach was proposed to develop reliable model for prediction of compressibility factor of sour and natural gas. A robust soft computing approach namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization tool was proposed. To evaluate the performance and accuracy of this model, statistical and graphical error analyses have been used simultaneously. Moreover, comparative studies have been conducted between this model and nine empirical correlations and equations of state. The obtained results demonstrated that the proposed CSA-LSSVM model is more robust, reliable and efficient than the existing correlations and equations of state for prediction of z-factor of sour and natural gasses. © 2013 Elsevier B.V.
Authors & Co-Authors
Kamari, Arash
Iran, Tehran
Islamic Azad University
Hemmati-Sarapardeh, Abdolhossein
Iran, Tehran
Sharif University of Technology
Iran, Tehran
Amirkabir University of Technology
Mirabbasi, Seyed Morteza
Iran, Tehran
Amirkabir University of Technology
Nikookar, Mohammad
Iran, Tehran
Tarbiat Modares University
Mohammadi, Amir H.
France, Paris
Institut de Recherche en Génie Chimique et Pétrolier Irgcp
South Africa, Durban
University of Kwazulu-natal School of Chemical Engineering
Statistics
Citations: 92
Authors: 5
Affiliations: 6
Identifiers
Doi:
10.1016/j.fuproc.2013.06.004