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Record W2606352188 · doi:10.3390/en10040532

Exergoeconomic Performance Comparison and Optimization of Single-Stage Absorption Heat Transformers

2017· article· en· W2606352188 on OpenAlexaff
S. S. Mahmoudi, S. Salehi, Mortaza Yari, Marc A. Rosen

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCoefficient of performanceHeat exchangerExergySolverAbsorption refrigeratorCondenser (optics)Exergy efficiencySecond law of thermodynamicsThermodynamicsTransformerEvaporatorMathematicsProcess engineeringMathematical optimizationMaterials scienceEngineeringRefrigerantPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Three single-stage absorption heat transformer (SSHT) configurations are modeled, analyzed and compared from the viewpoints of thermodynamics and economics, using the Engineering Equation Solver (EES) software. In addition, a multi-objective optimization is carried out for the three configurations to specify the optimal design point considering the second law efficiency and the product unit cost as two objective functions. The configurations differ from one another considering the number of heat exchangers used in them. The results show that the coefficient of performance (COP) and exergy coefficient of performance (ECOP) for configuration 3 are around 35% and 30% higher than the corresponding values for configuration 1, respectively. Also, configuration 2 is found to be more economic with a product unit cost of about 21% and 5% lower than those for configurations 1 and 3, respectively. Furthermore, it is observed that relatively higher absorber temperatures can be achieved by configurations 2 and 3 compared to configuration 1. It is concluded from the multi-objective optimization that the conditions at which the evaporator, condenser and absorber temperatures are 86.51 °C, 39.03 °C and 123.1 °C, respectively, represents an optimal solution.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.220
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2017
Admission routes1
Has abstractyes

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