Hybrid Mechanistic Data-Driven Modeling for the Deterministic Global Optimization of a Transcritical Organic Rankine Cycle

Abstract

Global optimization is desirable for the design of chemical and energy processes as design decisions have a significant influence on the economics. A relevant challenge for global flowsheet optimization is the incorporation of accurate thermodynamic models. A promising alternative to conventional thermodynamic property models is the integration of data-driven surrogate models into mechanistic process models and deterministic global optimization in a reduced space. In our previous works, we trained artificial neural networks (ANNs) on thermodynamic data and included the surrogate models in the global flowsheet optimization of subcritical organic Rankine cycles (ORC). In this work, we extend the framework to the optimization of transcritical ORCs operating at a supercritical high pressure level and subcritical low pressure level. We train separate ANNs for supercritical and subcritical thermodynamic properties. ANNs with a small number of neurons can learn the thermodynamic properties to sufficient accuracy. We identify the optimal working fluid among 122 available fluids in the thermodynamic library CoolProp via a deterministic global optimization of the hybrid process model using the solver MAiNGO. The results show that the process can be optimized efficiently and that transcritical operation can enable high power generation.