کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8129093 | 1523019 | 2014 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم زمین و سیاره ای (عمومی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions](/preview/png/8129093.png)
چکیده انگلیسی
Absorption of carbon dioxide (CO2) in aqueous solutions can be improved by the addition of other compounds. However, this requires a large amount of equilibrium data for solubility estimation in a wide ranges of temperature, pressure and concentration. In this paper, a model based on an artificial neural network (ANN) was proposed and developed with mixtures containing monoethanolamine (MEA), diethanolamine (DEA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP), methanol, triethanolamine (TEA), piperazine (PZ), diisopropanolamine (DIPA) and tetramethylensulfone (TMS) to predict solubility of CO2 in mixed aqueous solution (especially in binary and ternary mixtures) over wide ranges of temperature (298.15-453.15Â K), pressure (0.604-19,914Â kPa), overall concentration (18.986-80 percent) and apparent molecular weight of the mixture (20.99-78.50Â g/mol). The performance accuracy of the network was evaluated by regression analysis on estimated and experimental data, which were not used in network training. The optimal neural network was trained by the Levenberg-Marquardt back-propagation algorithm and the Gauss-Newton method with combination of a Bayesian regularization technique contains two hidden layers, having 8 and 4 neurons, respectively. Tan-sigmoid function was used as the transfer function of hidden and output layers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Natural Gas Science and Engineering - Volume 21, November 2014, Pages 19-25
Journal: Journal of Natural Gas Science and Engineering - Volume 21, November 2014, Pages 19-25
نویسندگان
M.E. Hamzehie, S. Mazinani, F. Davardoost, A. Mokhtare, H. Najibi, B. Van der Bruggen, S. Darvishmanesh,