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 the 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 (<0.1) but a high flammability (lower flammability
limit <1.8 vol %). Cyclopentane showed the best performance with
a coefficient of performance of 3.06 ± 0.05. The sampling-based
reverse engineering method identified top performing working fluids,
but avoided solving complex and computationally demanding molecular
design problems, and took into account the real fluid property uncertainties.