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Title

The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

Year: 1393
COI: JR_IJOGST-3-3_005
Language: EnglishView: 191
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Authors

Ali Khazaei - Thermodynamics Research Laboratory, School of Chemical Engineering, Iran University of Science & Technology, Tehran, Iran
Hossein Parhizgar - Young Researchers and Elites Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Mohammad Reza Dehghani - Thermodynamics Research Laboratory, School of Chemical Engineering, Iran University of Science & Technology, Tehran, Iran

Abstract:

In this work, artificial neural network (ANN) has been employed to propose a practical model forpredicting the surface tension of multi-component mixtures. In order to develop a reliable modelbased on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures atdifferent temperatures was employed. These systems consist of 777 data points generally containinghydrocarbon components. The ANN model has been developed as a function of temperature, criticalproperties, and acentric factor of the mixture according to conventional corresponding-state models.80% of the data points were employed for training ANN and the remaining data were utilized fortesting the generated model. The average absolute relative deviations (AARD%) of the model for thetraining set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively.Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory hasproved the high prediction capability of the attained model.

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This Paper COI Code is JR_IJOGST-3-3_005. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/835350/

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Khazaei, Ali and Parhizgar, Hossein and Dehghani, Mohammad Reza,1393,The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks,https://civilica.com/doc/835350

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Type of center: دانشگاه دولتی
Paper count: 22,547
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