Prediction of solubility of solid compounds in supercritical CO₂ using a connectionist smart technique

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• 作者：Mohammad Reza Dadkhah^a ; ^b ; Afshin Tatar^c ; ^{Afshin.Tatar@gmail.com} ; Armin Mohebbi^d ; Ali Barati-Harooni^e ; Adel Najafi-Marghmaleki^e ; Mohammad M. Ghiasi^f ; Amir H. Mohammadi^f ; ^g ; ^h ; ^{a.h.m@irgcp.fr} ; ^{amir_h_mohammadi@yahoo.com} ; Fathollah Pourfayazⁱ
• 关键词：Supercritical CO2 ; Solubility ; Solid ; Model ; Adaptive Neuro-Fuzzy Inference System (ANFIS) ; Conjugate of hybrid and particle swarm optimization (CHPSO)
• 刊名：The Journal of Supercritical Fluids
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：120
• 期：part_P1
• 页码：181-190
• 全文大小：3556 K
• 卷排序：120

文摘

An ANFIS model was developed for estimation of solubility of several solid species in supercritical CO2. A total of 795 data points were collected from literature and used for model development. The Genetic Algorithm was implemented to optimize the radius of influence of FIS. The tuning parameters of FIS structure were optimized by a method namely CHPSO method. The accuracy of the proposed model was successfully evaluated using graphical and statistical analysis.