کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5409097 | 1506535 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A robust modeling approach to predict the surface tension of ionic liquids
ترجمه فارسی عنوان
یک روش مدل سازی قوی برای پیش بینی تنش سطحی مایعات یونی
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کلمات کلیدی
1-Octyl-3-methylimidazolium tetrafluoroborate1-octyl-3-methylimidazolium chloride1-butyl-4-methylpyridinium tetrafluoroborate1-Butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide1-Butyl-3-methylimidazolium methylsulfate1-Hexyl-3-methylimidazolium tetrafluoroborateTrihexyltetradecylphosphonium chloride1-Ethyl-3-methylimidazolium trifluoromethanesulfonate1-Ethyl-3-methylimidazolium ethylsulfate1-Butyl-3-methylimidazolium trifluoromethanesulfonate1-Butyl-3-methylimidazolium dicyanamide1-Butyl-3-methylimidazolium thiocyanateARDGCMANN1-ethyl-3-methylimidazolium dicyanamide1-butyl-3-methylimidazolium chlorideRMSERBF1-Ethyl-3-methylimidazolium tetrafluoroborate - 1-اتیل-3-methylimidazolium tetrafluoroborate1-Octyl-3-methylimidazolium hexafluorophosphate - 1-اکتیل-3-methylimidazolium hexafluorophosphate1-Butyl-3-methylimidazolium hexafluorophosphate - 1-بوتیل-3-methylimidazolium hexafluorophosphate1-Butyl-3-methylimidazolium tetrafluoroborate - 1-بوتیل-3-methylimidazolium tetrafluoroborate1-Hexyl-3-methylimidazolium hexafluorophosphate - 1-هگزیل -3-methylimidazolium hexafluorophosphate1-hexyl-3-methylimidazolium chloride - 1-هگزیل 3-methylimidazolium کلریدGenetic algorithm - الگوریتم ژنتیکstandard deviation - انحراف معیارQSPR - برقراری ارتباط کمی بین ساختار و خواص مولکولCost function - تابع هزینهSensitivity analysis - تحلیل حساسیتQuantitative structure–property relationship - رابطه ساختاری و مالکیت کمیGroup contribution method - روش مشارکت گروهیArtificial Neural Network - شبکه عصبی مصنوعیAARD - طبیعتRadial basis function - عملکرد پایه شعاعیLSSVM - لسومIonic liquid - مایع یونیaverage relative deviation - میانگین انحراف نسبیPrediction - پیش بینیSurface tension - کشش سطحیLeast square support vector machine - کمترین مربع دستگاه بردار پشتیبانی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
چکیده انگلیسی
The objective of the present study is to develop a mathematical model based on the least square support vector machine to predict the surface tension of ionic liquids (ILs). Molecular weight, reduced temperature, reduced pressure, critical compressibility factor and acentric factor are selected as input parameters and the surface tension is designated as the output parameter. An extensive database including 868 experimental data points for surface tension of 61 ILs are considered to develop the LSSVM model which the adjustable parameters of the model are calculated by a genetic algorithm programming. The proposed model exhibits good accuracy with the average absolute relative deviation of 1.41, 1.74 and 1.54% for train set, test set, and total data, respectively. The Leverage method is employed to check the validity of the model. The results show that the majority of data points are located in a standard error domain demonstrating statistical acceptability of the model and only 3.6% of data points are recognized as suspected data. To evaluate the effects of input parameters on the surface tension of ILs, a sensitivity analysis is performed. The results show that the critical compressibility factor of ILs with relative importance of 22.97% has the greatest effect on the surface tension of ionic liquids. Finally, the effects of temperature and the total number of carbon in cation side alkyl chain on the surface tension are predicted by the proposed model. The model exhibits a monotonic decrease for surface tension with increasing temperature. Moreover, it well predicts the effects of the total number of carbon atoms in cation side alkyl chain on the surface tension.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Molecular Liquids - Volume 236, June 2017, Pages 344-357
Journal: Journal of Molecular Liquids - Volume 236, June 2017, Pages 344-357
نویسندگان
Saeid Atashrouz, Hamed Mirshekar, Ahmad Mohaddespour,