Reverse engineering of working fluid selection for industrial heat pump based on Monte Carlo sampling and uncertainty analysis

This study presents a novel methodology for the identification of suitable pure component working fluids for heat pumps. Two challenges are addressed: the difficulties in solving a complex product-process design problem and making it accessible for practical applications, as well as the impact of the working fluid property uncertainties on the solution.

A Monte Carlo sampling is applied to generate sets of different property parameter combinations (virtual fluids), which are subsequently evaluated in the heat pump process model. The distance between the property values of the virtual fluid and the uncertainty bound of the properties of real fluids (collected from a database) are calculated.

The fluids that are closest to the top-performing virtual fluids are further analyzed through evaluation in the cycle and subsequent uncertainty propagation of the respective input property uncertainties to the model output uncertainties. The methodology has been applied to an industrial heat pump system used for waste heat recovery from a spray drying facility in dairy industry.

To remain focused on the validation of underlying concepts of the methodology, the study considered screening only among cyclic hydrocarbon working fluids. The compounds identified by the methodology had a low global warming potential (