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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2018

22-12-2016 | Original Article

Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Authors: Amin Daryasafar, Navid Daryasafar, Mohammad Madani, Mahdi Kalantari Meybodi, Mohammad Joukar

Published in: Neural Computing and Applications | Issue 1/2018

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Abstract

Carbon dioxide injection is a known promising and economical technology for improving oil recovery. Despite its immense effect on oil recovery, the application of this technique in modern recovery industry has been limited due to poor solubility of n-alkanes in supercritical CO2. Therefore, it is very consequential to investigate the solubility of different n-alkanes in supercritical CO2. Since experimental methods for measuring the solubility of n-alkanes in supercritical CO2 at different temperatures and pressures are not economical and usually take a long time, feasibility of applying intelligent tools in the solubility prediction of different n-alkanes in supercritical CO2 at pressures up to 45.9 MPa was conducted in this study. For this purpose, two models including an artificial neural network and an adaptive neuro-fuzzy interference system (ANFIS) both trained with particle swarm optimization (PSO) algorithm were used for simulating this process. Calculated mole fractions of n-alkanes in supercritical CO2 from ANFIS–PSO model were excellently consistent with actual measured values. Moreover, comparison between these models and Chrastil semiempirical correlation show superiority and accuracy of the proposed ANFIS–PSO approach. Results of this study indicate that ANFIS–PSO method is a powerful technique for predicting solubility of n-alkanes in supercritical CO2.
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Metadata
Title
Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide
Authors
Amin Daryasafar
Navid Daryasafar
Mohammad Madani
Mahdi Kalantari Meybodi
Mohammad Joukar
Publication date
22-12-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 1/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2793-7

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