2026-04-29T13:50:12Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112522026-01-22T01:05:31Zcom_10259_9737com_10259_5086com_10259_2604col_10259_10448

2026-01-21T09:37:30Z urn:hdl:10259/11252 Artificial neural network modeling and VFT correlation of experimental dynamic viscosity of the 2-(2-ethoxyethoxy)ethanol + 2-propanol binary mixtures at high pressures and temperatures Lifi, Mohamed Bazile, Jean Patrick Daridon, Jean Luc Galliero, Guillaume Aguilar Romero, Fernando Muñoz Rujas, Natalia Oxygenated additives Alkoxyethanol Alcohol Dynamic viscosity Vogel−Fulcher−Tammann (VFT) correlation Artificial Neural Network (ANN) model This work reports experimental dynamic viscosity (n) data for the binary mixtures of 2-(2-ethoxyethoxy)ethanol (Diethylene glycol monoethyl ether) and 2-propanol at high temperatures and high pressures. A falling-body viscometer was employed to carry out the measurements for seven compositions of the studied binary mixture, as well as for pure 2-propanol, over a temperature range of 293.15–353.15 K and pressures up to 70 MPa, with an experimental uncertainty of +-3%. At 0.1 MPa, the dynamic viscosity was measured using a classical Ubbelohde capillary viscometer with an uncertainty of +-1%. The high-pressure dynamic viscosity experimental data were correlated using the Vogel-Fulcher-Tammann (VFT) equation. To describe the viscosity of the binary system as a function of composition, temperature, and pressure, a combining rule based on the logarithm of viscosity was employed. Additionally, an Artificial Neural Network (ANN), a machine learning approach known for its accuracy in capturing nonlinear relationships, was applied to model the dynamic viscosity of the studied binary mixture. The dynamic viscosity behaviour of the analysed binary system was attributed to variations in free volume and the disruption or weakening of hydrogen bonds. 2026-01-21T09:37:30Z 2026-01-21T09:37:30Z 2025-12 info:eu-repo/semantics/article 0016-2361 https://hdl.handle.net/10259/11252 10.1016/j.fuel.2025.137776 eng Fuel. 2025, V. 409, p. 137776 https://doi.org/10.1016/j.fuel.2025.137776 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Elsevier