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A new ecosystem of models from Molecular Thermodynamics - predicting mixture properties

Objective

Imagine, we could design chemical processes solely based on molecular properties. This proposal offers a new ecosystem of equation-of-state models for predicting fluid mixtures. A recent theoretical approach from Molecular Thermodynamcis (the uv-theory) gives an entirely new perspective on thermodynamic equations of state because: (1) substances with rich experimental data, such as water, can be described with arbitrary accuracy – similar to empirical multiparameter equations of state. The so-obtained highly accurate equation of state remains compatible with other substances for the prediction of mixtures within a SAFT model framework (Statistical Associating Fluid Theory). This concept is radically new, compared to the status quo, where a user must choose between a highly accurate equation of state for a pure substance or a model to represent mixtures. Further (2), the proposed model does not assume molecules to be entirely flexible, as existing SAFT-models do. Molecules are accurately described as chains of fused spherical interaction sites, similar to force fields used in molecular simulations. The model framework is developed from theories of Molecular Thermodynamics which is why the model will also predictive for inhomogeneous systems, interfacial properties and specifically for adsorption in microporous materials. The applicant is convinced that a powerful new model-ecosystem should be further developed collaboratively, as a community effort, which is why the model will be available as open-source software from the outset of the project.

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Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2025-ADG

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Host institution

UNIVERSITY OF STUTTGART
Net EU contribution
€ 2 498 450,00
Address
KEPLERSTRASSE 7
70174 Stuttgart
Germany
Region
Baden-Württemberg Stuttgart Stuttgart, Stadtkreis
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Beneficiaries (1)