(711E) A Multi-scale Computational Framework for Property Prediction of Fluid Mixtures

Fluids are present in many industrial products and processes, with applications ranging from upstream oil and gas exploration and production to fuels and additives as well as pharmaceuticals and drug delivery. Improvements in industrial processes currently rely on accurate modeling of heat and fluid flow modeling requiring reliable data as numerical input. Due to the difficulties to get sufficient accuracy on the experimental data over certain ranges of temperatures and pressure, accurate estimates of physical and chemical properties of fluids increasingly make use of powerful atomistic simulation and multi-scale techniques.

The term multi-scale modeling comprises varying methodologies and levels of theory¹, starting at the molecular level and reaching “up” to continuum methods and equations of state.

The physical and chemical characteristics of a material, which relate to a given property or process, determine the time and length scales at which simulations need to occur. In turn, the property- or process-relevant length and time scales require simulations using a certain level of theory, such as quantum or statistical mechanics. Hence, for all practical applications, simulations proceed by decomposing a complex system or process into parts or steps, which are studied individually. Thus, gained knowledge and data are then combined to predict the behavior of the full system or process.

Here, we will illustrate (through the use of efficient simulation workflows) the value of simulations at different scales for determining a broad range of properties including vapor-liquid equilibria (VLE)², solubility³, surface tension, viscosity, thermal conductivity, and heat capacity, for organic fluids of industrial relevance. We will illustrate efficient protocols for deriving simple and easy-to-use correlations for properties of interest⁴, based on large datasets obtained by simulation.

Specific examples will be used to illustrate the accuracy, application range, and limit of the underlying computational approaches and provide perspectives and development trends.

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2. Yiannourakou, M.; Ungerer, P.; Lachet, V.; Rousseau, B.; Teuler, J. M., United atom forcefield for vapor-liquid equilibrium (VLE) properties of cyclic and polycyclic compounds from Monte Carlo simulations. Fluid Phase Equilib. 2019, 481, 28-43.
3. Yiannourakou, M.; Rozanska, X.; Minisini, B.; de Meyer, F., Molecular simulations for improved process modeling of an acid gas removal unit. Fluid Phase Equilib. 2022, 560, 113478.
4. Ungerer, P.; Yiannourakou, M.; Mavromaras, A.; Collell, J., Compositional Modeling of Crude Oils Using C10–C36 Properties Generated by Molecular Simulation. Energy & Fuels 2019.