(327e) Computer-Aided Selection and Design of Working Fluids for Organic Rankine Cycles

Organic Rankine Cycles (ORCs) are receiving increasing attention from the scientific community due to their potential in renewable energy applications. A very large number of working fluids have been proposed for ORCs over the past years. However, the identification of the best working fluids for given heat source and sink temperature profiles remains an open research challenge. A systematic method is needed to guide the identification of high-performance ORC systems for a given application. We will present a systematic methodology to the selection and design of optimal working fluids for Organic Rankine Cycles (ORCs). The approach is based on thermodynamic analysis to identify property-performance relationships, targeted property database searches, computer aided molecular design (CAMD) and ORC optimization techniques. Through thermodynamic analysis we will derive physical property objectives to target searches for those molecules that offer optimum ORC performance with respect to economic, operating, safety and environmental objectives. The methodology enables the quick identification of existing molecules from databases that offer maximum performance in the ORC in terms of electricity cost. The method further enables the identification of novel working fluids for which no experimental physical property data are available. Here, we utilize group contribution methods in combination with multi-objective optimization technology for the generation of optimum working fluid candidates.

For the identified optimum ORC working fluids, the ORCs are optimized to identify the cost-optimal optimal performance of the integrated system with acceptable safety and environmental characteristics. The proposed approach is illustrated with case studies in the design of working fluids for solar and geothermal ORC applications. The methodology systematically and quickly identifies both novel and conventional molecular structures that enable optimum ORC process performances.