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dc.creatorAmaris, Carlos
dc.creatorAlvarez, Maria E.
dc.creatorBourouis, Mahmoud
dc.creatorVallès, Manel
dc.date.accessioned2020-11-12T17:32:16Z
dc.date.available2020-11-12T17:32:16Z
dc.date.issued2020-08-20
dc.identifier.issn1996-1073
dc.identifier.urihttps://hdl.handle.net/11323/7275
dc.description.abstractIn this study, ammonia vapor absorption with NH3/LiNO3 was assessed using correlations derived from a semi-empirical model, and artificial neural networks (ANNs). The absorption process was studied in an H-type corrugated plate absorber working in bubble mode under the conditions of an absorption chiller machine driven by low-temperature heat sources. The semi-empirical model is based on discretized heat and mass balances, and heat and mass transfer correlations, proposed and developed from experimental data. The ANN model consists of five trained artificial neurons, six inputs (inlet flows and temperatures, solution pressure, and concentration), and three outputs (absorption mass flux, and solution heat and mass transfer coefficients). The semi-empirical model allows estimation of temperatures and concentration along the absorber, in addition to overall heat and mass transfer. Furthermore, the ANN design estimates overall heat and mass transfer without the need for internal details of the absorption phenomenon and thermophysical properties. Results show that the semi-empirical model predicts the absorption mass flux and heat flow with maximum errors of 15.8% and 12.5%, respectively. Maximum errors of the ANN model are 10.8% and 11.3% for the mass flux and thermal load, respectively.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceEnergiesspa
dc.subjectBubble absorptionspa
dc.subjectPlate heat exchangerspa
dc.subjectAdvanced surfacesspa
dc.subjectHeat and mass transfer correlationsspa
dc.subjectSemi-empirical modelspa
dc.subjectArtificial neural networksspa
dc.subjectAmmoniaspa
dc.subjectLithium nitratespa
dc.titlePerformance assessment of an NH3/LINO3 bubble plate absorber applying a semi-empirical model and artificial neural networksspa
dc.typearticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.researchgate.net/publication/343784992_Performance_Assessment_of_an_NH3LiNO3_Bubble_Plate_Absorber_Applying_a_Semi-Empirical_Model_and_Artificial_Neural_Networksspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doiDOI: 10.3390/en13174313


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