Chemical Industry and Chemical Engineering Quarterly 2014 Volume 20, Issue 3, Pages: 325-338
https://doi.org/10.2298/CICEQ121128014S
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Prediction of the binary density of the ILs+ water using back-propagated feed forward artificial neural network
Shojaee Safar Ali (Department of Chemical Engineering, Amirkabir University of Technology of Iran, Iran)
Hezave Zeinolabedini Ali (Islamic Azad University, Dashtestan Branch, Borazjan, Iran)
Lashkarbolooki Mostafa (Islamic Azad University, Dashtestan Branch, Borazjan, Iran)
Shafipour Zeinab Sadat (Islamic Azad University, Dashtestan Branch, Borazjan, Iran)
In this study, feasibility of a back-propagated artificial neural network to
correlate the binary density of ionic liquids (ILs) mixtures containing water
as the common solvent has been investigated. To verify the optimized
parameters of the neural network, total of 1668 data were collected and
divided into two different subsets. The first subsets consisted of more than
two-third (1251 data points) of data bank was used to find the optimum
parameters including weights and biases, number of neurons (7 neurons),
transfer functions in hidden and output layer which were tansig and purelin,
respectively. In addition, the correlative capability of network was examined
using testing subset (417 data points) not considered during the training
stage. The overall obtained results revealed that the proposed network is
accurate enough to correlate the binary density of the ionic liquids mixtures
with average absolute relative deviation (AARD %) and average relative
deviation (ARD %) of 1.56% and -0.04 %, respectively. Finally, the
correlative capability of the proposed ANN model was compared with one of the
available correlations proposed by Rodríguez and Brennecke.
Keywords: artificial neural network, binary density, prediction, back propagated, feed forward