Low cost (< $150 kWt−1) and high heat-transfer coefficient particle heat exchangers may enable high temperature operation of high efficiency power cycles (supercritical CO2/air Brayton) [1–3]. Currently, these heat exchangers are cost-prohibitive and require large surface areas due to ineffective particle-particle and particle-CO2 heat transfer. Particle heat transfer media are examples of complex material systems that can display a re-configurable mesostructure during flow or shearing processes. This deformation or rearrangement in the underlying active material can cause a decrease in the thermal transport properties and limit the heat-transfer coefficient. For future adoption, it is critical that we gain a greater understanding of how local (particle-particle) thermophysical properties are affected by system architecture/design. Traditional heat exchanger optimization approaches are limited and often lead to non-feasible design approaches. Here, we employ a stochastic and evolutionary method, particle swarm optimization (PSO), to perform a multi-objective optimization for the particle-to-sCO2 shell-and-plate heat exchanger for two state-of-the-art particulate materials (i.e., Accucast ID50K and CARBO HSP). The objective function for optimization considers the minimum payback period (economics), entropy generation (thermodynamics), and volume (engineering). The results suggest that Accucast ID50K is preferable for a packed bed heat exchanger from the perspective of minimizing payback period and volume, while at a larger entropy generation rate than CARBO HSP.

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