Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable...

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Vydáno v:The Korean journal of chemical engineering Ročník 35; číslo 7; s. 1556 - 1569
Hlavní autoři: Soleimani, Reza, Saeedi Dehaghani, Amir Hossein, Shoushtari, Navid Alavi, Yaghoubi, Pedram, Bahadori, Alireza
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2018
Springer Nature B.V
한국화학공학회
Témata:
Artificial neural networks
Back propagation algorithms
Back propagation networks
Bayesian analysis
Binary mixtures
Biotechnology
Catalysis
Chemistry
Chemistry and Materials Science
Computational fluid dynamics
Data points
Design engineering
Industrial Chemistry
Industrial Chemistry/Chemical Engineering
Ionic liquids
Materials Science
Mathematical models
Model accuracy
Neural networks
Polymer
Predictions
Solvents
Surface tension
화학공학
Ionic Liquids
Surface Tension
Binary Mixtures
Artificial Neural Network
Prediction
ISSN:0256-1115, 1975-7220
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Popis
Shrnutí:Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.
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ISSN:0256-1115
1975-7220
DOI:10.1007/s11814-017-0326-4