A stochastic optimization approach for the design of organic fluid mixtures for low-temperature heat recovery

Over 50% of the heat generated in industry is in the form of low-grade heat (with operating temperatures below 370°C). Recovering heat from these sources with standard Rankine cycles (using water as working fluid) is inefficient and expensive. Organic working fluids have become an attractive alternative to mitigate these inefficiencies. In this work, we address the problem of designing flexible multi-component organic fluids capable of withstanding variability in heat source temperatures and efficiencies of individual cycle equipment units. The design problem is cast as a nonlinear stochastic optimization problem and we incorporate risk metrics to handle extreme variability. We show that a stochastic optimization framework allows us to systematically trade-off performance of the working fluid under a variety of scenarios (e.g., inlet source temperatures and equipment efficiencies). With this, it is possible to design working fluids that remain robust in a wide range of operational conditions. We also find that significant flexibility of the working fluid can be obtained by using optimal concentrations as opposed to using single component mixtures. We also find that state-of-the-art nonlinear optimization solvers can handle highly complex stochastic optimization problems that incorporate detailed physical representations of the system.