کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
202016 460581 2012 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using artificial neural network to predict the ternary electrical conductivity of ionic liquid systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
Using artificial neural network to predict the ternary electrical conductivity of ionic liquid systems
چکیده انگلیسی

The unique physical properties of ionic liquids play a decisive part in many of their applications. Therefore, the ability to predict the physical properties of ionic liquids is extremely important for the rational design of proper ionic liquids with specific properties. In practice, the processes involving ionic liquids usually contain other components, in addition to the ionic liquids. Therefore, in addition to pure component properties, knowledge of the physical properties of mixtures are also crucial for various applications. In the present study, the feasibility of using a feed-forward multi-layer perceptron neural network (MLPNN) model was investigated to predict the electrical conductivity of the ternary mixtures of 1-butyl-3-methylimidazolium hexafluorophosphate ([bmim][PF6]) + water + ethanol and [bmim][PF6] + water + acetone in the temperature range from 288.15 K to 308.15 K, consisting of 104 data points. Not only were different networks, namely the linear and the hyperbolic tangent sigmoid transfer functions, considered in this study, but also the effects of the number of hidden layers, hidden neurons and the training algorithm were investigated on the accuracy of the results using 78 data points as training data to minimize the average absolute relative deviation percent (AARD%), mean square error (MSE) and correlation coefficient (R2). Among the various cases studies, statistical analyses indicated the best configuration of the network to include one hidden layer and seven neurons in the hidden layer. The optimum network was then validated using 26 data points (test data) not used in the training stage which indicated the good interpolative ability of the trained network with AARD% = 1.44, MSE = 2.87 × 10−8 and R2 = 0.9981.


► The use of artificial neural networks to predict electrical conductivity was studied.
► Complex ternary mixtures with one component being an ionic liquid were investigated.
► Artificial neural networks (ANNs) are excellent tools for correlations of such systems.
► ANNs are also accurate for prediction of electrical conductivity of ternary systems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fluid Phase Equilibria - Volume 314, 25 January 2012, Pages 128–133
نویسندگان
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