No abstract

No abstract

No abstract

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, a total of 1668 data points were collected and divided into two different subsets. The first subset, consisting of more than two thirds (1251 data points) of the data bank, was used to find the optimum parameters including weights and biases, number of neurons (7 neurons), transfer functions in hidden and output layers, which were tansig and purelin, respectively. In addition, the correlative capability of network was examined using a 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 Rodriguez and Brennecke.

No abstract