Prediction of plasticization pressure of polymeric membranes for CO2 removal from natural gas

被引:47
作者
Ahmad, A. L. [1 ]
Adewole, J. K. [1 ]
Leo, C. P. [1 ]
Ismail, S. [1 ]
Sultan, A. S. [2 ]
Olatunji, S. O. [3 ]
机构
[1] Univ Sains Malaysia, Sch Chem Engn, Seberang Prai Selatan 14300, Pulau Pinang, Malaysia
[2] King Fahd Univ Petr & Minerals, Ctr Petr & Minerals, Dhahran 31261, Saudi Arabia
[3] Univ Dammam, Coll Comp Sci & Informat Technol, Dept Comp Sci, Dammam, Saudi Arabia
关键词
High pressure gas separation; CO2 - induced plasticization; Structure - property relationship; Natural gas processing; Aggressive feed gas; PENETRANT-INDUCED PLASTICIZATION; TRANSPORT-PROPERTIES; CARBON-DIOXIDE; POLYIMIDE MEMBRANES; CROSS-LINKING; SORPTION; SEPARATION; DIFFUSION; OPTIMIZATION; POLYSULFONES;
D O I
10.1016/j.memsci.2015.01.039
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Membrane separation of CO2 from natural gas components is known to be more efficient at high feed gas pressure. However, one of the main challenges of high pressure operations is penetrant - induced plasticization phenomenon. An approximate model for predicting the plasticization pressure was developed using the fundamental theory of dual - mode sorption model and the total immobilization assumption of the penetrant concentration in the Langmuir sorption site The assumption reduces the permeability - pressure relationship to system of quadratic equations whose coefficients were used to derive an expression for estimating the plasticization pressure. The resulting expression showed that, the plasticization pressure is a multivariable function of polymer properties. The support vector (SVR) algorithm was then used to correlate between the plasticization pressure, and the fractional free volume and the glass transition temperature of the polymers. Results obtained revealed that, with a careful combination of these two properties, the SVR can be successfully employed to predict the plasticization pressure of membrane used in high pressure CO2 removal from natural gas. The correlation coefficient for the training and the testing are 0.8837 and 0.9433, respectively. (C) 2015 Elsevier B.V. All rights reserved
引用
收藏
页码:39 / 46
页数:8
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