Journal article
Prediction of properties of new halogenated olefins using two group contribution approaches
Department of Mechanical Engineering, Technical University of Denmark1
Thermal Energy, Department of Mechanical Engineering, Technical University of Denmark2
Department of Chemical and Biochemical Engineering, Technical University of Denmark3
CAPEC-PROCESS, Department of Chemical and Biochemical Engineering, Technical University of Denmark4
The increasingly restrictive regulations for substances with high ozone depletion and global warming potentials are driving the search for new sustainable fluids with low environmental impact. Recent research works have pointed out the great potential of fluorine- and chlorine-based olefins as refrigerants and solvents, due to their environmentally-friendly features.
However there is a lack of experimental data of their thermophysical properties. In this work we present two models based on a group contribution method, using a classical approach and neural networks, to predict the critical temperature, critical pressure, normal boiling temperature, acentric factor, and ideal gas heat capacity of organic fluids containing chlorine and/or fluorine.
The accuracy of the prediction capacity of the two models is analyzed, and compared with equivalent methods in the literature. The models showed an average reduction of the absolute relative deviation for all the studied properties of more than 50%, compared to other methods. In addition, it was observed that the neural-network-based model yielded a better accuracy than the classical approach in the prediction of all the properties, except for the acentric factor, due to the lack of experimental data for this property.
Language: | English |
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Year: | 2017 |
Pages: | 79-96 |
ISSN: | 18790224 and 03783812 |
Types: | Journal article |
DOI: | 10.1016/j.fluid.2016.10.020 |
ORCIDs: | Montagud, Maria E. Mondejar , Abildskov, Jens , Woodley, John and Haglind, Fredrik |
Acentric factor Critical pressure Critical temperature Group contribution methods Ideal gas heat capacity Neural network Normal boiling temperature Olefins SDG 12 - Responsible Consumption and Production