A direct formulation of the integrated design problem as mixed integer nonlinear program (MINLP) introduces one discrete degree of freedom for each considered working fluid resulting in a prohibitive problem size and complexity. (11) therefore conclude that the working fluid selection should be directly integrated into the design process of any ORC system. The selection of different working fluids can be due to either different objective functions or different sets of preselected working fluids. (11) recently showed in their review that different studies recommend different working fluids for the same temperature levels. Thus, systematic methods for working fluid selection are desired. (2, 8-10) However, as the thermophysical properties of the working fluid influence the optimal process parameters in a complex way, working fluid selection today still depends on experience and heuristic knowledge. Working fluid selection is generally recognized as a crucial decision in the design of an ORC, since the performance of the cycle directly depends on the thermophysical properties of the working fluid. A sound holistic design of process and working fluid is achieved. The framework is used for the optimization of a geothermal ORC in a case study. To allow for the prediction of caloric properties, a quantitative structure–property relationship (QSPR) for the ideal gas heat capacity is proposed that relies on pure component parameters of PC-SAFT. The simultaneous optimization is achieved by exploiting the rich molecular picture underlying the PC-SAFT equation of state in a continuous-molecular targeting approach (CoMT-CAMD). We therefore present a framework for the holistic design of ORCs enabling the simultaneous optimization of the process and the working fluid based on process performance.

Such an approach can lead to suboptimal solutions, since the optimal fluid might be excluded by the heuristics.

Today, process optimization and working fluid selection are typically addressed separately in a two-step approach: working fluids are selected using heuristic knowledge subsequently, the process is optimized. The objective is to maximize process performance by optimizing both process parameters and the working fluid.

To make the best use of the diverse low temperature heat sources, the cycle is tailored to each application. Organic Rankine Cycles (ORCs) generate power from low temperature heat.

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