Modelling of the Phase Equilibrium and Physical Properties of Complex Mixtures Using Advanced Thermodynamic Methods

Modelling of phase equilibrium and physical properties is fundamental to process design and simulation in various areas of industrial or scientific importance. Various industrial processes involve complex mixtures of different nature
It is important to understand and describe the partitioning of the constituting components of these mixtures into different phases, which change with the process conditions and the mixture composition, and the properties of the resulting phases. This common challenge calls for a general thermodynamic framework.

The present PhD project is focused on modeling of the phase equilibrium and thermophysical properties of complex multicomponent mixtures using advanced thermodynamic models such as SAFT (Statistical Association Fluid Theory)-family models and the COSMO (COnductor-like Screening MOdel)-type models.

Although these models have already demonstrated some inspiring results and appear to be very promising in many applications, some of their deficiencies remain unsolved.  For instance, original SAFT theory does not account for intramolecular interactions and cooperativity in hydrogen bonding.


Therefore, introduction of the cooperativity may improve the performance of SAFT-type models. Another promising approach is to utilize the spectroscopic data in association term of the model. COSMO-type models have high computational costs for quantum chemistry simulations and improvements of computational efficiency need to be introduced.


These models often perform qualitatively correct in many applications but still require further improvements and developments to produce quantitatively correct results especially for associating compounds.

We will investigate how to improve the modelling accuracy and/or computational efficiency of these advanced models for complex mixtures. The recent findings from quantum chemistry calculations and spectroscopic studies will be considered in the modelling efforts. In addition, we will also explore the possibility of applying artificial neural network and machine learning in the property prediction.
The study will benefit our modeling of complex systems in classical areas, like CCS and flow assurance, as well as in pharmaceutical and biotechnical areas where sophisticated molecules are prevailing.


Main supervisor:

Assoc. Prof. Wei Yan

Co- supervisor:

Prof. Erling H. Stenby


Daria Grigorash
PhD student
DTU Chemistry


Wei Yan
Associate Professor
DTU Chemistry
+45 45 25 23 79


Erling Halfdan Stenby
Head of Department, Professor
DTU Chemistry
+45 45 25 20 12