Open AccessPrediction of the binary density of the ILs+ water using back-propagated feed forward artificial neural network
Author(s) -
Safar Ali Shojaee,
Zeinolabedini Ali Hezave,
Mostafa Lashkarbolooki,
Zeinab Sadat Shafipour
Publication year - 2014
Publication title -
chemical industry and chemical engineering quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.189
H-Index - 26
eISSN - 2217-7434
pISSN - 1451-9372
DOI - 10.2298/ciceq121128014s
Subject(s) - artificial neural network , binary number , relative standard deviation , absolute deviation , biological system , ionic liquid , mathematics , binary classification , pattern recognition (psychology) , computer science , algorithm , artificial intelligence , statistics , chemistry , support vector machine , arithmetic , biochemistry , detection limit , biology , catalysis
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