The current methods applied to material screening for
adsorption-based
heat pumps are based on a fixed set of temperatures or their independent
variation, providing a limited, insufficient, and unpractical evaluation
of different adsorbents. This work proposes a novel strategy for the
simultaneous optimization and material screening in the design of
adsorption heat pumps by implementing a meta-heuristic approach, particle
swarm optimization (PSO). The proposed framework can effectively evaluate
variable and broad operation temperature intervals to search for viable
zones of operation for multiple adsorbents at once. The criteria for
selecting the adequate material were the maximum performance and the
minimum heat supply cost, which were considered the objective functions
of the PSO algorithm. First, the performance was assessed individually,
followed by a single-objective approximation of the multi-objective
problem. Next, a multi-objective approach was also adopted. With the
results generated during the optimization, it was possible to find
which adsorbents and temperature sets were the most suitable according
to the main objective of the operation. The Fisher–Snedecor
test was applied to expand the results obtained during PSO application
and a feasible operating region built around the optima, enabling
the arrangement of close-to-optima data into practical design and
control tools. This approach allowed for a fast and intuitive evaluation
of multiple design and operation variables.