A new model for prediction of binary mixture of ionic liquids + water density using artificial neural network

Abstract This study highlights the application of artificial neural network for prediction of density values in a binary mixture of water and ionic liquids at different temperature for different imidazolium-based ionic liquids. Two intelligent models named multilayer perceptron model and radial basis function model were developed and the accuracy of two models was examined by different graphical and statistical methods. The input parameters were temperature, water/ionic liquid ratio, molecular weight, critical temperature, critical pressure and boiling point temperature of ionic liquids and the model output was density. The molecular weight, critical temperature, critical pressure and boiling point temperature were used to discriminate among different ionic liquids. Results showed that the both models are accurate and effective in prediction of density of ionic liquid + water mixture. However, based on graphical and statistical representations, the multilayer perceptron model exhibits better performance and is superior compared to radial basis function model.

[1]  J. Gmehling,et al.  Extension and revision of the group contribution method GCVOL for the prediction of pure compound liquid densities , 2003 .

[2]  Li-sheng Wang,et al.  Densities and Viscosities of 1-Butyl-3-methylimidazolium Trifluoromethanesulfonate + H2O Binary Mixtures at T = (303.15 to 343.15) K , 2008 .

[3]  E. Rój,et al.  Simulation of an absorption column performance using feed-forward neural networks in nitric acid production , 1998 .

[4]  J. Esperança,et al.  Densities and Derived Thermodynamic Properties of Ionic Liquids. 3. Phosphonium-Based Ionic Liquids over an Extended Pressure Range , 2006 .

[5]  Yuzhong Zhang,et al.  Poly(isonicotinic acid) modified glassy carbon electrode for electrochemical detection of norepinephrine , 2002 .

[6]  J. Torrecilla,et al.  Density and Molar Volume Predictions Using COSMO-RS for Ionic Liquids. An Approach to Solvent Design , 2007 .

[7]  Alireza Bahadori,et al.  Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models , 2016 .

[8]  R. Santos,et al.  Comparison Between Multilayer Feedforward Neural Networks and Radial Basis Function Network to Detect and Locate Leaks in a Pipeline Transporting Gas , 2013 .

[9]  Mohammad R. Riazi,et al.  Estimation of properties distribution of C7+ by using artificial neural networks , 2011 .

[10]  K. R. Seddon.  Room‐Temperature Ionic Liquids: Neoteric Solvents for Clean Catalysis , 1997 .

[11]  M.N.S. Swamy,et al.  Neural networks in a softcomputing framework , 2006 .

[12]  D. Richon,et al.  Modeling of thermodynamic properties using neural networks: Application to refrigerants , 2002 .

[13]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[14]  Juan A. Lazzús,et al.  ρ–T–P prediction for ionic liquids using neural networks , 2009 .

[15]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[16]  Amin Shokrollahi,et al.  Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs , 2014 .

[17]  C. Ye,et al.  Rapid and accurate estimation of densities of room-temperature ionic liquids and salts. , 2007, The journal of physical chemistry. A.

[18]  J. Coutinho,et al.  Extension of the Ye and Shreeve group contribution method for density estimation of ionic liquids in a wide range of temperatures and pressures , 2008 .

[19]  Cheng-Wu Chen,et al.  RETRACTED: Stability analysis of community and ecosystem hierarchies using the Lyapunov method , 2011 .

[20]  Okan Ozgonenel,et al.  The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice , 2011 .

[21]  A. Mohammadi,et al.  Use of Artificial Neural Networks for Estimating Water Content of Natural Gases , 2007 .

[22]  Herman L. Ammon,et al.  New Atom/Functional Group Volume Additivity Data Bases for the Calculation of the Crystal Densities of C-, H-, N-, O-, F-, S-, P-, Cl-, and Br-Containing Compounds , 2001 .

[23]  Li-sheng Wang,et al.  Densities and Viscosities of 1-Propyl-2,3-dimethylimidazolium Tetrafluoroborate + H2O at T = (298.15 to 343.15) K , 2009 .

[24]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[25]  C. Tarver.  Density estimations for explosives and related compounds using the group additivity approach , 1979 .

[26]  Martin T. Hagan,et al.  Neural network design , 1995 .

[27]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[28]  I. Marrucho,et al.  High-Pressure Densities and Derived Thermodynamic Properties of Imidazolium-Based Ionic Liquids , 2007 .

[29]  Aage Fredenslund,et al.  Group contribution method for the prediction of liquid densities as a function of temperature for solvents, oligomers, and polymers , 1991 .

[30]  L. M. Varela,et al.  Great increase of the electrical conductivity of ionic liquids in aqueous solutions , 2006 .

[31]  Afshin Tatar,et al.  A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater , 2016 .

[32]  Mingfang Yu,et al.  The physical properties of aqueous solution of room-temperature ionic liquids based on imidazolium: Database and evaluation , 2008 .

[33]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[34]  K. Seddon,et al.  Influence of chloride, water, and organic solvents on the physical properties of ionic liquids , 2000 .

[35]  Amin Shokrollahi,et al.  Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir , 2013, Appl. Soft Comput..

[36]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[37]  César Hervás-Martínez,et al.  COVNET: a cooperative coevolutionary model for evolving artificial neural networks , 2003, IEEE Trans. Neural Networks.

[38]  J. Torrecilla,et al.  Effect of Relative Humidity of Air on Density, Apparent Molar Volume, Viscosity, Surface Tension, and Water Content of 1-Ethyl-3-methylimidazolium Ethylsulfate Ionic Liquid , 2008 .

[39]  Joan F. Brennecke,et al.  Temperature and Composition Dependence of the Density and Viscosity of Binary Mixtures of Water + Ionic Liquid , 2006 .

[40]  L. M. Varela,et al.  Density and Derived Thermodynamic Properties of 1-Ethyl-3-methylimidazolium Alkyl Sulfate Ionic Liquid Binary Mixtures with Water and with Ethanol from 288 K to 318 K , 2012 .

[41]  Alireza Bahadori,et al.  Implementing radial basis function networks for modeling CO2-reservoir oil minimum miscibility pressure , 2013 .

[42]  K. Joback,et al.  ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS , 1987 .

[43]  J. A. Lazzús.  Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds , 2009 .

[44]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[45]  Amin Shokrollahi,et al.  Applying a robust solution based on expert systems and GA evolutionary algorithm for prognosticating residual gas saturation in water drive gas reservoirs , 2014 .

[46]  Leonidas A. Constantinou,et al.  New Group-Contribution Method for Predicting Temperature-Dependent Properties of Pure Organic Compounds , 2005 .

[47]  T. Poggio,et al.  Networks and the best approximation property , 1990, Biological Cybernetics.

[48]  Hao Yu,et al.  Advantages of Radial Basis Function Networks for Dynamic System Design , 2011, IEEE Transactions on Industrial Electronics.