naa a22 4500 510818765 CHVBK 20180411083520.0 cr unu---uuuuu 180411e20130101xx s 000 0 eng 10.1007/s11814-012-0112-2 doi (NATIONALLICENCE)springer-10.1007/s11814-012-0112-2 Correlation of density for binary mixtures of methanol+ionic liquids using back propagation artificial neural network [Elektronische Daten] [Mostafa Lashkarbolooki, Ali Hezave, Aziz Babapoor] Ionic liquids (ILs) are amazing fluids introduced as a replacement for conventional solvents due to their unique properties. Unfortunately, they have several unfavorable features such as high viscosity, which makes pumping them difficult on industrial scale. In this regard, several researchers mix the ionic liquids with each other or some conventional solvents, organic and inorganic compounds, to eliminate those unfavorable features. So the binary properties of the ILs mixtures have been increasingly measured and correlated through the past years. One of the most widely used solvents and additives in the different chemical industries is methanol. In the present investigation, the capability of artificial neural networks for correlating the binary density of the ILs systems containing methanol as a common part (total of 426 experimental data points) has been examined. The results revealed that the best network architecture obtained in this study was feasible to correlate the binary densities of the ILs mixtures with average absolute relative deviation percent (AARD%), average relative deviation percent (ARD%) and correlation coefficient (R2) values of 0.85%, −0.05 and 0.9948, respectively. Korean Institute of Chemical Engineers, Seoul, Korea, 2012 Methanol nationallicence Ionic Liquids nationallicence Binary Density nationallicence Correlation nationallicence Artificial Neural Network nationallicence Lashkarbolooki Mostafa Dashtestan Branch, Islamic Azad University, Borazjan, Iran aut Hezave Ali Dashtestan Branch, Islamic Azad University, Borazjan, Iran aut Babapoor Aziz Department of chemical engineering, Ahar Branch, Islamic Azad University, Ahar, Iran aut Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/1(2013-01-01), 213-220 0256-1115 30:1<213 2013 30 11814 https://doi.org/10.1007/s11814-012-0112-2 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11814-012-0112-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lashkarbolooki Mostafa Dashtestan Branch, Islamic Azad University, Borazjan, Iran aut NATIONALLICENCE 700 1- Hezave Ali Dashtestan Branch, Islamic Azad University, Borazjan, Iran aut NATIONALLICENCE 700 1- Babapoor Aziz Department of chemical engineering, Ahar Branch, Islamic Azad University, Ahar, Iran aut NATIONALLICENCE 773 0- Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/1(2013-01-01), 213-220 0256-1115 30:1<213 2013 30 11814 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer