Global sensitivity analysis of a generator-absorber heat exchange (GAX) system's thermal performance with a hybrid energy source: An approach using artificial intelligence models
Created by W.Langdon from
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- @Article{CARDOSOFERNANDEZ:2023:applthermaleng,
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author = "V. Cardoso-Fernandez and A. Bassam and
O. {May Tzuc} and M. A. {Barrera Ch.} and
Jorge {de Jesus Chan-Gonzalez} and M. A. {Escalante Soberanis} and
N. Velazquez-Limon and Luis J. Ricalde",
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title = "Global sensitivity analysis of a generator-absorber
heat exchange ({GAX)} system's thermal performance with
a hybrid energy source: An approach using artificial
intelligence models",
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journal = "Applied Thermal Engineering",
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volume = "218",
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pages = "119363",
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year = "2023",
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ISSN = "1359-4311",
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DOI = "doi:10.1016/j.applthermaleng.2022.119363",
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URL = "https://www.sciencedirect.com/science/article/pii/S1359431122012935",
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keywords = "genetic algorithms, genetic programming,
Generator-Absorber Heat Exchange (GAX), Solar
refrigeration cycle, Hybrid renewable energy system,
Data-driven models, PAWN method, Decision-making
process, Absorption refrigeration",
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abstract = "Generator-absorber heat exchange (GAX) systems
represent a promising alternative to substitute
environmentally harmful refrigeration devices based on
conventional vapor compression, as long as a proper
analysis of thermal performance and the complex
interactions of heat transfer that occur into GAX cycle
is taken in consideration. In this research, a cooling
process based on a GAX system that uses ammonia-water
working fluid and a hybrid source (natural gas-solar)
is studied to analyze the variables that affect the
system's thermal performance. The work's novelty is the
hybridization between artificial intelligence (AI)
modeling and the global sensitivity analysis (GSA)
developed with the PAWN method. Experimental data was
obtained from a system with a cooling capacity of 10.5
kW (3 Ton), designed to work at heat source
temperatures of 200 degreeC. The measured variables
were the temperatures at generator, heat at evaporator,
and working fluid volumetric flow. Three AI techniques
(artificial neural networks, genetic programming, and
support vector machines) were evaluated for modeling
the thermodynamic cycle. Results obtained from the PAWN
method applied to the artificial neural network, since
it was the best AI model, indicates that the
operational parameters with a greater impact in the
system's performance are the inlet temperature at the
generator (30.7 percent) and the heat measured at the
evaporator for NH3 (27.4 percent), for the first output
COPNH3. For the second output COPH2O, the inlet
temperature at the generator (32.5 percent) and the and
heat measured at the evaporator for H2O (26.7 percent),
have a greater impact for such output. The proposed
IA-GSA methodology contributes to the development of
operational decision-making related to instrumentation,
operation performance, and corrective and/or preventive
maintenance actions of GAX systems. The developed
thermal performance model has potential for
implementation in embedded systems (smart sensors) as a
critical element in control and optimization strategies
to improve the performance of these cycles",
- }
Genetic Programming entries for
V Cardoso-Fernandez
Ali Bassam
Oscar de Jesus May Tzuc
M A Barrera Ch
Jorge de Jesus Chan-Gonzalez
M A Escalante Soberanis
N Velazquez-Limon
Luis J Ricalde
Citations