Dr. Ali Aminian

Department:  Department D 2 - Thermodynamics
Phone: (+420) 266052030
Room:  M2206
Location:  Main Building, Dolejškova 1402/5, Praha
Publications: Search in ASEP Database
WoS Researcher ID: AAI-9106-2021
ORCID: http://www.orcid.org/0000-0002-2072-3543

PhD in Chemical Engineering

Recent Publications:

Ali Aminian, Modeling vapor–liquid equilibrium and liquid–liquid extraction of deep eutectic solvents and ionic liquids using perturbed-chain statistical associating fluid theory equation of state. Part II, AIChe Journal, 2022; e17774, https://doi.org/10.1002/aic.17774.

Ali Aminian, David Celný, Erik Mickoleit, Andreas Jäger, Václav Vinš, Ideal Gas Heat Capacity and Critical Properties of HFE-Type Engineering Fluids: Ab Initio Predictions of Cpig, Modeling of Phase Behavior and Thermodynamic Properties Using Peng–Robinson and Volume-Translated Peng–Robinson Equations of State, International Journal of Thermophysics, https://doi.org/10.1007/s10765-022-03006-z.

Václav Vinš, Ali Aminian, David Celný, Monika Součková, Jaroslav Klomfar, Miroslav Čenský, Olga Prokopová, Surface tension and density of dielectric heat transfer fluids of HFE type–experimental data at 0.1 MPa and modeling with PC-SAFT equation of state and density gradient theory, International Journal of Refrigeration, https://doi.org/10.1016/j.ijrefrig.2021.06.029.

Ali Aminian, Modeling the Vapor-Liquid equilibria of binary and ternary systems comprising associating and non-Associating compounds by using Perturbed-Chain Statistical association fluid Theory. Part I, The Journal of Chemical Thermodynamics, https://doi.org/10.1016/j.jct.2021.106563.

Ali Aminian, Estimating the solubility of different solutes in supercritical CO2 covering a wide range of operating conditions by using neural network models, The Journal of Supercritical Fluids, http://dx.doi.org/10.1016/j.supflu.2017.02.007.

Ali Aminian, Predictingthe effective viscosity of nanofluids for the augmentation of heat transfer in the process industries, Journal of Molecular Liquids, http://dx.doi.org/10.1016/j.molliq.2016.12.071.

Ali Aminian, Predicting the effective thermal conductivity of nanofluids for intensification of heat transfer using artificial neural network, Powder Technology, http://dx.doi.org/10.1016/j.powtec.2016.05.040.