REVERSE ENGINEERING OF FLUID SELECTION FOR ORCs USING CUBIC EQUATIONS OF STATE

asme-orc2015 Tracking Number 8

Fluid selection for thermodynamic cycles like organic Rankine cycles remains an actual topic. Generally the search for a working fluid is based on experimental approaches or on a not very systematic trial and error approach. An alternative theory based reverse engineering approach is proposed and investigated here: The process should start with a model process, designed with respect to the boundary conditions and with (abstract) properties of the fluid needed to fit into this process, best described by some general equation of state and the corresponding fluid-describing parameters. These should be analyzed and optimized with respect to the defined model process, which also has to be optimized simultaneously. The degrees of freedom of the process are restricted to some crucial state variables with variation regimes defined with respect to the boundary conditions like the heat source, heat sink, technical restrictions etc. Knowing the optimal fluid parameters, real fluids can be selected or even synthesized which have fluid defining properties in the optimum regime like critical temperature Tc or ideal gas capacities of heat cp, also allowing to find new working fluids, not considered so far. The number and kind of the fluid-defining parameters is mainly based on the choice of the used equation of state (EOS). In the present work the cubic Peng-Robinson equation was chosen due to its moderate numerical expense, sufficient accuracy and a general availability of the fluid-defining parameters for many compounds. The considered model-process is designed for a typical geothermal heat source with a temperature level of 423.15 K. The objective functions are the thermal efficiency and the net power output relative to the volumetric flow rate at turbine entrance (VSP) as a function of critical pressure pc, Tc, acentric factor and cp. Also, some crucial process variables have to be regarded as a problem variable. The results give clear hints regarding optimal fluid parameters of the analyzed process and deepen the thermodynamic understanding of the process. Finally, for the thermal efficiency optimization a strategy for screening large databases is explained. Several fluids from different substance groups were found to have high thermal efficiencies. These fluids will also have to fulfill further criteria, prior to their usage, but the method appears to be a good base for fluid selection.