Evaluation of density, viscosity, surface tension and CO2 solubility for single, binary and ternary aqueous solutions of MDEA, PZ and 12 common ILs by using artificial neural network (ANN) technique. (October 2016)

• Record Type: Journal Article
• Authors: Golzar, Karim; Modarress, Hamid; Amjad-Iranagh, Sepideh
• Is Part Of: International journal of greenhouse gas control. Volume 53(2016:Oct.)
• Page Start: 187
• Page End: 197
• Publication Date: 2016-10
• Subjects: Artificial neural network technique -- CO2 solubility -- Density -- Viscosity -- Surface tension -- N-Methyldiethanolamine -- Piperazine -- Ionic liquid; Greenhouse gases -- Environmental aspects -- Periodicals; Air -- Purification -- Technological innovations -- Periodicals; Gaz à effet de serre -- Périodiques; Gaz à effet de serre -- Réduction -- Périodiques; Air -- Purification -- Technological innovations; Greenhouse gases -- Environmental aspects; Periodicals; 363.73874605
• DOI: 10.1016/j.ijggc.2016.08.008
• Languages: English
• ISSNs: 1750-5836

Abstract

Graphical abstract: Highlights: Predicting the ρ, μ, σ and α C O 2 for single aqueous solution of amine-ILs with ANN technique. Predicting the ρ, μ, σ and α C O 2 for binary aqueous solution of amine-ILs with ANN technique. Predicting the ρ, μ, σ and α C O 2 for ternary aqueous solution of amine-ILs with ANN technique. Using Levenberg-Marquardt and tan-sigmoid respectively as LA and TF in ANN models. Abstract: In this study, density, viscosity, surface tension and CO2 solubility for single, binary and ternary aqueous solutions of N -methyldiethanolamine (MDEA), piperazine (PZ) and 12 common ionic liquids (ILs) were predicted by applying artificial neural network (ANN) technique. The input data included operating temperature (293.15–373.15 K) and pressure (0.177–3938.400 kPa) in addition to the weight fractions of aqueous solutions of MDEA, PZ and ILs which were respectively in the range of (0.0–1.0), (0.0–0.09) and (0.0–1.0) as well as the molecular weight of ILs (148.18–505.00 g/mol) and their acentric factors (0.325–1.261). More than 2600 experimental data points for density, viscosity, surface tension and CO2 solubility were collected from literature. By using the Levenberg-Marquardt back-propagation and tan-sigmoid as learning algorithm and transfer function, respectively, four ANN models were examined to treat these data. It was found that the best ANN architectures for predicting these properties were respectively (7:3:0:1), (7:6:0:1), (7:4:0:1) and (7:10:0:1). The calculated properties were compared with the corresponding experimental data which indicated a negligible error.