Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N_222(n)]Tf₂N) by Means of Artificial Intelligence Techniques

Abstract

In this work, thermophysical properties of quaternary ammonium-based ionic liquids (ILs) including density, surface tension, and viscosity are produced by two powerful artificial intelligence techniques: genetic function approximation (GFA) and artificial neural network (ANN). In proposed GFA and ANN models, the critical temperature and water content of studied ILs ([N 222(n) ]Tf 2 N with n ¼ 5, 6, 8, 10, and 12) as well as operation temperature were given as the input parameters and the density, surface tension, and viscosity were predicted as the output results. The obtained results reveal that the selected input parameters are appropriate for prediction of thermophysical properties of quaternary ammonium-based ILs. In addition, the high statistical quality represented by various criteria and the low prediction errors of the presented models indicate that they can accurately predict the density, surface tension, and viscosity of new ILs without recourse to experimental data.

Evaluating the properties of ionic liquid at variable temperatures and pressures by QSPR

“…Y -randomization test was utilized to avoid the possibility of chance correlation in the modelling work. 32,49 Hosted file image9.wmf available at https://authorea.com/users/359155/articles/481358-evaluating-theproperties-of-ionic-liquid-at-variable-temperatures-and-pressures-by-qspr Hosted file image10.wmf available at https://authorea.com/users/359155/articles/481358-evaluating-theproperties-of-ionic-liquid-at-variable-temperatures-and-pressures-by-qspr Hosted file image11.wmf available at https://authorea.com/users/359155/articles/481358-evaluating-theproperties-of-ionic-liquid-at-variable-temperatures-and-pressures-by-qspr Herein, and are the experimental and calculated values of ILs, respectively.n is the total number of data points.…”

“…In the last decade, QSPR has been profusely employed to study the properties of ILs, such as heat capacity, [23][24][25] viscosity, [26][27][28][29] thermal conductivity, 30,31 surface tension, 32,33 and toxicity. 34-37 Lazzús 38 estimated the density in a wide of temperature (253-473 K) and pressure (0.1-250 MPa) range with satisfactory results (AARD =2.00 %).…”

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

“…Mehrabi et al [44] established a genetic algorithm-polynomial neural network and a fuzzy C-means (FCM) based neuro-fuzzy inference system to determine the TC ratio of Al 2 O 3 -based nanofluids based on the concentration and size of the nanoparticles, as well as the temperature. Golzar et al [45] used artificial neural network (ANN) methods and the approximation of general function for the calculation of the thermophysical properties of quaternary ammonium-based ILs in terms of the critical temperature of the ILs and the water content. Soriano et al determined the refractive index of binary solutions of IL systems based on ANN algorithms [46].…”

Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite

“…However, reliable computational prediction methods such as an artificial neural network (ANN) have garnered excessive attraction . For instance, Golzar et al . adopted genetic function algorithm (GFA) and ANN models to predict the density, surface tension, and viscosity of pure quaternary ammonium‐based ionic liquids.…”

“…[19][20][21] However, reliable computational predictionm ethods such as an artificial neural network (ANN)h ave garnered excessive attraction. [22][23][24] Fori nstance, Golzar et al [25] adopted genetic function algorithm (GFA) and ANN models to predict the density,s urface tension, and viscosity of pure quaternary ammonium-based ionic liquids.F otoohi et al [11] studied the prediction of binary mixtures from data reported previously by utilizing 2D EoS,t hat is,R K, SRK, PR, and modified Mohsennia-Modarress-Mansoori( M4) and compared the results with the data predicted by utilizing ANN model predictions.H owever, they claimed that the binary mixtures predicted by utilizing the ANN model showed better agreement and accuracy compared to the EoS studied. Verma and Abhinav [26] studied the differences between numerous ANN model regressions to predict Langmuir equilibrium constant for CO 2 adsorption on coal.…”