Organic Rankine cycles power systems are expected to play a substantial role in the future European energy system, as they contribute to reduce the CO2 emissions and dependence on fossil fuels by converting low temperature heat from renewable energy or industrial waste to electrical power. However, the working fluids that they use exhibit poor environmental properties or safety issues, due to their toxicity or flammability. Consequently, many of them are being phased out. As a result, there is an urgent need for alternative working fluids. The main barrier for the introduction of novel working fluids is the lack of an accurate knowledge of their thermophysical behaviour.
This project addresses the development of predictive models for the thermophysical properties of new working fluids. To this end, the project addresses the investigation of the thermophysical behaviour of novel pure fluids, fluid mixtures, and nanofluids (colloidal suspensions of nanoparticles in fluids). The prediction models rely on the use of group contribution methods.
The generalization of the model for pure fluids to any chemical group was found counterproductive as it implied loss of accuracy. Therefore, the developed predictive models were tailored for specific chemical groups. The research results have demonstrated that the predictions from the developed models for pure halogenated olefins outperform those of equivalent available models.
Concerning mixtures, the project addressed the improvement of mixing models for two types of cubic equations of state for mixtures of commercial refrigerants and hydrofluoroolefins. First, new parameters were fitted for the mixing model of a standard Peng Robinson equation of state. Second, parameters for the more complex mixing model of a volume translated Peng Robinson equation of state were fitted. The performance of these models was evaluated with an extensive set of experimental data.
With regards to nanofluids, a broad literature overview of published thermophysical properties of nanofluids showed that data were scarce, preventing the development of reliable predictive models. To advance the knowledge of the thermophysical behaviour of nanofluids, the first comprehensive database of experimental data of nanofluids was created.
