Electrolytes play a big role in many industrial processes, for instance, salts can increase the inhibitory effect of methanol, ethanol and glycols on the formation of gas hydrates, and have an effect of the gas solubility water-hydrocarbon mixtures. Salts also play a role in many chemical industries such as waste-water treatment, energy storage in batteries, desalination, etc.
When designing such processes and productions there is a need to know how the physical properties of the mixture in use will behave over the temperature and pressure range that is in use, and while such knowledge could be obtained through extensive experiments, with very high cost and time use as a consequence, it is more suitable to relay on trusted thermodynamics models.
Modelling electrolyte systems are typically done by activity coefficient models such as the e-NRTL, and extended UNIQUAC, however such models have difficulty handling high pressures, in which case there is a need for an Equation of State. Several Equations of State for electrolytes have been developed over the year, but relatively few have been proposed that can handle mixtures of both hydrogen bonding compounds as well as salts or electrolytes.
An extension to the Cubic Plus Association (CPA) Equation of Sate has been under development in a previous PhD project (Development of an Electrolyte CPA Equation of State for Application in the Petroleum and Chemical Industries, Bjørn Maribo-Mogensen). A model has been proposed as a result of this study however this model is only in the early stage of development, especially in terms parameterization.
In this PhD project we will continue on the patch towards a fully developed model, by initially focusing on the parameterization of the already proposed model. This includes estimating parameters for a variety of solvents, on the basis of several types of experimental data, and all parameters will be validated to all kinds of phase equilibria. Based on the extensive parameterization, the model will be evaluated and possibly revised if this is found to improve the model.
Supervisor: Prof. Georgios Kontogeorgis, gk@kt.dtu.dk
Co-supervisor: Assoc. Prof. Kaj Thomsen: kth@kt.dtu.dk