Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation

The performance of an organic Rankine cycle (ORC) relies on process design and operation. Simultaneous optimization of design and operation for a range of working fluids (WFs) is therefore a promising approach for WF selection. For this, deterministic global process optimization can guarantee to identify a global optimum, in contrast to local or stochastic global solution approaches. However, providing accurate thermodynamic models for a large number of WFs while maintaining computational tractability of the resulting optimization problems are open research questions. We integrate accurate thermodynamic and transport properties via artificial neural networks (ANNs) and solve the design problems with MAiNGO in a reduced-space formulation. We illustrate the approach for an ORC process for waste heat recovery of a diesel truck. After an automated preselection of 122 WFs, ANNs are automatically trained for the 37 selected WFs based on data retrieved from the thermodynamic library CoolProp. Then, we perform deterministic global optimization of design and operation for every WF individually. Therein, the trade-off between net power generation and investment cost is investigated by multiobjective optimization. Further, a thermoeconomic optimization finds a compromise between both objectives. The results show that, for the given conditions, monoaromatic hydrocarbons are a promising group of WFs. In future work, the proposed method and the trained ANNs can be applied to the design of a variety of energy processes.