TY - JOUR
T1 - Electrical conductivity of ammonium and phosphonium based deep eutectic solvents
T2 - Measurements and artificial intelligence-based prediction
AU - Bagh, F. S.Ghareh
AU - Shahbaz, K.
AU - Mjalli, F. S.
AU - AlNashef, I. M.
AU - Hashim, M. A.
N1 - Funding Information:
This research was funded by University of Malaya research grant number HIR-MOHE D000003-16001 and by the Deanship of Scientific Research at King Saud University through group project no. RGP-VPP-108 in collaboration with the petroleum and chemical engineering department, engineering faculty in Sultan Qaboos University, Oman.
PY - 2013/10/25
Y1 - 2013/10/25
N2 - The evaluation of deep eutectic solvents (DESs) as a new generation of solvents for various practical application requires an insight of the main physical, chemical, and thermodynamic properties. In this study, the experimental measurements of the electrical conductivity of two classes of DESs based on ammonium and phosphonium salts at different compositions and temperatures were reported. The results revealed that electrical conductivity of DESs has temperature-dependency. In addition, molar conductivities of ammonium and phosphonium salts in DESs were obtained using DESs experimental values of electrical conductivities. The feasibility of using an artificial neural network (ANN) model to predict the electrical conductivity of ammonium and phosphonium based DESs at different temperatures and compositions was also examined. A feed-forward back propagation neural network with 8 hidden neurons was successfully developed and trained with the measured electrical conductivity data. The results indicated that among the different networks tested, the network with 8 hidden neurons had the best prediction performance and gave the smallest value of Normalized Mean Square Error (NMSE) (0.0010) and acceptable values of Index of Agreement (IA) (0.9999) and Regression Coefficient (R2) (0.9988). The comparison of the predicted electrical conductivity of DESs by the proposed model with those obtained by experiments confirmed the reliability of the ANN model with an average absolute relative deviation (AARD%) of 4.40%.
AB - The evaluation of deep eutectic solvents (DESs) as a new generation of solvents for various practical application requires an insight of the main physical, chemical, and thermodynamic properties. In this study, the experimental measurements of the electrical conductivity of two classes of DESs based on ammonium and phosphonium salts at different compositions and temperatures were reported. The results revealed that electrical conductivity of DESs has temperature-dependency. In addition, molar conductivities of ammonium and phosphonium salts in DESs were obtained using DESs experimental values of electrical conductivities. The feasibility of using an artificial neural network (ANN) model to predict the electrical conductivity of ammonium and phosphonium based DESs at different temperatures and compositions was also examined. A feed-forward back propagation neural network with 8 hidden neurons was successfully developed and trained with the measured electrical conductivity data. The results indicated that among the different networks tested, the network with 8 hidden neurons had the best prediction performance and gave the smallest value of Normalized Mean Square Error (NMSE) (0.0010) and acceptable values of Index of Agreement (IA) (0.9999) and Regression Coefficient (R2) (0.9988). The comparison of the predicted electrical conductivity of DESs by the proposed model with those obtained by experiments confirmed the reliability of the ANN model with an average absolute relative deviation (AARD%) of 4.40%.
KW - Ammonium
KW - Artificial neural network
KW - Deep eutectic solvents
KW - Electrical conductivity
KW - Phosphonium
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U2 - 10.1016/j.fluid.2013.07.012
DO - 10.1016/j.fluid.2013.07.012
M3 - Article
AN - SCOPUS:84880899848
SN - 0378-3812
VL - 356
SP - 30
EP - 37
JO - Fluid Phase Equilibria
JF - Fluid Phase Equilibria
ER -