Electrical conductivity of ammonium and phosphonium based deep eutectic solvents

Measurements and artificial intelligence-based prediction

F. S Ghareh Bagh, K. Shahbaz, F. S. Mjalli, I. M. AlNashef, M. A. Hashim

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

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%.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalFluid Phase Equilibria
Volume356
DOIs
Publication statusPublished - Oct 25 2013

Fingerprint

artificial intelligence
intelligence
Ammonium Compounds
eutectics
Eutectics
Artificial intelligence
electrical resistivity
predictions
Neural networks
neurons
Neurons
Salts
salts
regression coefficients
performance prediction
Electric Conductivity
Chemical analysis
Backpropagation
chemical properties
Mean square error

Keywords

  • Ammonium
  • Artificial neural network
  • Deep eutectic solvents
  • Electrical conductivity
  • Phosphonium

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Physical and Theoretical Chemistry
  • Physics and Astronomy(all)

Cite this

Electrical conductivity of ammonium and phosphonium based deep eutectic solvents : Measurements and artificial intelligence-based prediction. / Bagh, F. S Ghareh; Shahbaz, K.; Mjalli, F. S.; AlNashef, I. M.; Hashim, M. A.

In: Fluid Phase Equilibria, Vol. 356, 25.10.2013, p. 30-37.

Research output: Contribution to journalArticle

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