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Analyse énergétique et exergétique d’un cycle frigorifique à absorption d’ammoniac-eau dans un environnement d’évaporation sous le point de congélation : apprentissage automatique et optimisation paramétrique.

Energy and exergy analysis of a subfreezing evaporator environment ammonia-water absorption refrigeration cycle: Machine learning and parametric optimization.

Auteurs : AL-RBAIHAT R., ALAHMER H., ALAHMER A., ALTORK Y., AL-MANEA A., EAYAL AWWAD K. Y.

Type d'article : Article de la RIF

Résumé

The coefficient of performance (COP) and exergy efficiency of a single and double-effect ammonia-water absorption refrigeration system powered by compound parabolic concentrating collectors were analyzed under various operating situations. A novel method was proposed using support vector machine regression and particle swarm optimization to identify optimal operating parameters. The optimal pressure-temperature conditions, including evaporator pressure (Pe), generator pressure (Pg), and absorber temperature (Ta) that maximize the COP and exergy efficiency while minimizing generator temperature (Tg) and evaporator temperature (Te), were investigated. The generator temperature was the main independent variable, ranging from 370 to 470 K. The findings demonstrated that the gain in COP and exergy efficiency caused by raising the generator temperature to more than 430 K is not cost-effective. The COP increased when the evaporator temperature increased along the investigated range of generator temperatures but yielded lower exergy efficiency in all cases. The exergy destruction rate in condenser, pump, recooler, reheater, and expansion valves is insignificant compared to other components. The generator has the highest exergy destruction rate regardless of operating conditions, making it the most crucial component of the absorption system. The optimization process findings showed that, at Pe = 2.8 bar, Pg = 14.5 bar, and Ta = 303.15 K, the maximum COP and exergy efficiency were 0.8483 and 0.3605, respectively, concerning the minimization of Tg and Te, which were 408 and 267 K, respectively. The model produced an acceptable performance with a high prediction accuracy (coefficient of determination > 0.99 and mean square error < 0.0064).

Documents disponibles

Format PDF

Pages : p. 182-204

Disponible

  • Prix public

    20 €

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    Gratuit

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Détails

  • Titre original : Energy and exergy analysis of a subfreezing evaporator environment ammonia-water absorption refrigeration cycle: Machine learning and parametric optimization.
  • Identifiant de la fiche : 30031999
  • Langues : Anglais
  • Sujet : Technologie
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 154
  • Date d'édition : 10/2023
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2023.07.010

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