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Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps.

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This study introduces a new particle swarm optimization (PSO) method for selecting materials for adsorption heat pumps. It efficiently screens adsorbents across wide temperature ranges to optimize performance and reduce costs.

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Area of Science:

  • Thermodynamics
  • Materials Science
  • Chemical Engineering

Background:

  • Current material screening for adsorption heat pumps is limited by fixed temperature evaluations.
  • Existing methods offer insufficient and impractical assessments of adsorbent performance.

Purpose of the Study:

  • To develop a novel strategy for simultaneous optimization and material screening in adsorption heat pump design.
  • To implement a meta-heuristic approach, particle swarm optimization (PSO), for evaluating adsorbents.

Main Methods:

  • Utilized particle swarm optimization (PSO) to evaluate variable and broad operation temperature intervals.
  • Defined objective functions for PSO based on maximum performance and minimum heat supply cost.
  • Applied both single-objective and multi-objective optimization approaches.

Main Results:

  • Identified optimal adsorbents and temperature sets for adsorption heat pump operation.
  • Generated feasible operating regions using the Fisher-Snedecor test for practical application.
  • Enabled fast and intuitive evaluation of multiple design and operation variables.

Conclusions:

  • The proposed PSO framework offers an effective and practical method for material screening and design optimization of adsorption heat pumps.
  • The approach facilitates the development of practical design and control tools by analyzing optimal and near-optimal conditions.