Summaries
- Selection of thermodynamic models for the simulation of chemical engineering processes
- The translated consistent cubic equation of state (Cubic model)
- Estimation of thermodynamic properties from machine learning models
- General studies on activity coefficients and cubic equations of state
- Entropy scaling (estimation of viscosity, thermal conductivity and self-diffusion coefficients from equations of state
- SAFT Equations of state
- The PPR78 – E-PPR78 models
- Titre du chaptire
Selection of thermodynamic models for the simulation of chemical engineering processes
French version of the book
English version of the book
The translated consistent cubic equation of state (Cubic model)
Application to radicals, transition states and classical molecules
-> Integrating Solvent Effects into the Prediction of Kinetic Constants Using a COSMO-Based Equation of State: https://doi.org/10.1021/acs.jctc.5c00133
-> Predicting solvation energies of free radicals and their mixtures: A robust approach coupling the Peng-Robinson and COSMO-RS models (open access) : https://doi.org/10.1016/j.molliq.2024.124641
-> Prediction of solvation energies at infinite dilution by the tc-PR cubic equation of state with advanced mixing rule based on COSMO-RS as gE model (open access): https://doi.org/10.1016/j.molliq.2023.122480
Definition of the model and application to pure components and mixtures
-> What Is the Optimal Activity Coefficient Model To Be Combined with the translated–consistent Peng–Robinson Equation of State through Advanced Mixing Rules?: https://doi.org/10.1021/acs.iecr.1c03003 (open access)
-> Use of 300,000 pseudo-experimental data over 1800 pure fluids to assess the performance of four cubic equations of state: SRK, PR, tc-RK, and tc-PR: https://doi.org/10.1002/aic.17518 (open access)
Estimation of thermodynamic properties from machine learning models
Prediction of critical temperatures Tc, critical pressures Pc, acentric factors 𝝎 and normal boiling point 𝑻_𝒆𝒃^°
-> AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach: https://doi.org/10.1186/s13321-025-01062-9 (open access)
General studies on activity coefficients and cubic equations of state
Advanced mixing rules - Collaboration with G. Kontogeorgis, DTU, Danemark
-> Let us rethink advanced mixing rules for cubic equations of state https://doi.org/10.1016/j.fluid.2025.114455 (open access)
-> Can liquid-liquid equilibria be predicted by the combination of a cubic equation of state and a g^E model not suitable for liquid-liquid equilibria?: https://doi.org/10.1016/j.fluid.2024.114249 (open access)
-> The secret of the Wilson equation (open access): https://doi.org/10.1016/j.fluid.2023.114018
Prediction of binary interaction parameters for Van der Waals mixing rules
-> The state of the art of cubic equations of state with temperature-dependent binary interaction coefficients: From correlation to prediction (open access) https://doi.org/10.1016/j.fluid.2022.113697
Entropy scaling (estimation of viscosity, thermal conductivity and self-diffusion coefficients from equations of state
EOS
-> An experiment-design methodology for the selection of optimal experimental conditions for the correlation of transport properties https://doi.org/10.1016/j.fluid.2023.113829
-> Entropy Scaling-Based Correlation for Estimating the Self-Diffusion Coefficients of Pure Fluids https://doi.org/10.1021/acs.iecr.2c01086 (open access)
-> Combining the entropy-scaling concept and cubic- or SAFT equations of state for modelling thermal conductivities of pure fluid (open access) https://doi.org/10.1016/j.ijheatmasstransfer.2022.123286
-> Revisiting the Entropy-Scaling Concept for Shear-Viscosity Estimation from Cubic and SAFT Equations of State: Application to Pure Fluids in Gas, Liquid and Supercritical States https://doi.org/10.1021/acs.iecr.1c01386
SAFT Equations of state
Definition of the I-PC-SAFT model
-> I‑PC-SAFT: An Industrialized Version of the Volume-Translated PCSAFT Equation of State for Pure Components, Resulting from Experience Acquired All through the Years on the Parameterization of SAFT-Type and Cubic Models https://doi.org/10.1021/acs.iecr.9b04660
The PPR78 – E-PPR78 models
PPR78
-> The impressive impact of including enthalpy and heat capacity of mixing data when parameterising equations of state. Application to the development of the E-PPR78 (Enhanced-Predictive-Peng-Robinson-78) model. https://doi.org/10.1016/j.fluid.2022.113456 (open access)
Titre du chaptire
PARAGRAPHE 1
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PARAGRAPHE 2
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