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

Autor: Navid Daryasafar, Amin Daryasafar, Mohammad Madani, Mahdi Kalantari Meybodi, Mohammad Joukar
Rok vydání: 2016
Předmět:
Zdroj: Neural Computing and Applications. 29:295-305
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-016-2793-7
Popis: 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.
Databáze: OpenAIRE
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