| Issue |
E3S Web Conf.
Volume 120, 2019
2019 2nd International Conference on Green Energy and Environment Engineering (CGEEE 2019)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 9 | |
| Section | Industrial Technology Innovation | |
| DOI | https://doi.org/10.1051/e3sconf/201912001003 | |
| Published online | 27 September 2019 | |
Prediction of Density and Speed of Sound of Binary Ionic Liquid and Ketone Mixtures Using Artificial Neural Network
1 Mapua University, School of Chemical Engineering and Chemistry, 1002 Manila, Philippines
2 De Lasalle University, Gokongwei College of Engineering, Chemical Engineering Department, 2401 Manila, Philippines
* Corresponding author: reddenrose@gmail.com
The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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