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Optimisation par essaims particulaires et par algorithme génétique d’un système de conditionnement d’air à éjecteur hybride.

Optimization of a hybrid ejector air conditioning system with PSOGA.

Auteurs : WANG H., CAI W., WANG Y.

Type d'article : Article

Résumé

In this paper, a model-based optimization strategy for a hybrid ejector air conditioning system is presented. By investigating the operating characteristics of the energy consuming components, the energy consumption models for generator, compressor, fans and pump are developed with experimental validation. The validation results indicate the proposed models are accurate enough for optimization issue. Then, the optimization problem is formulated with objective to achieve minimum energy consumption while meets the required cooling loads at the same time. The physical constraints and interaction between components are also illustrated. In order to solve this optimization problem, a PSOGA optimization approach is proposed. PSOGA takes the advantages of both particle swarm optimization (pso) and genetic algorithm (ga) with fast convergent speed and high accuracy. The cooling load is obtained based on corresponding temperature collected from 6 am to 20 pm on a single day. The comparison result between traditional on-off control and proposed PSOGA indicates the better performance of PSOGA with more energy saving achieved. Additional, the energy saving effect achieves its highest value at 32°C with 17%.

Détails

  • Titre original : Optimization of a hybrid ejector air conditioning system with PSOGA.
  • Identifiant de la fiche : 30020832
  • Langues : Anglais
  • Source : Applied Thermal Engineering - vol. 112
  • Date d'édition : 05/02/2017
  • DOI : http://dx.doi.org/10.1016/j.applthermaleng.2016.10.192

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