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Record W4254830715 · doi:10.1504/ijret.2018.090111

Solar sorption cooling for residential air-conditioning applications

2018· article· en· W4254830715 on OpenAlexaff
Julia Aman, Paul Henshaw, David S.‐K. Ting

Bibliographic record

VenueInternational Journal of Renewable Energy Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCoefficient of performanceChillerCondenser (optics)Absorption refrigeratorEvaporatorAir conditioningSolar air conditioningThermodynamicsSorptionEnvironmental scienceCooling capacityChiller boiler systemNuclear engineeringWater chillerWater coolingSolar energyAdsorptionAbsorption (acoustics)Materials scienceChemistryEngineeringRefrigerantRefrigerationElectrical engineeringPhysicsHeat exchangerComposite material

Abstract

fetched live from OpenAlex

This paper presents the comparison of two sorption cooling systems for providing air conditioning in a residential building that can be driven by a flat plate solar collector. A thermodynamic model has been developed for each system to compare the energy balance in each component and the coefficient of performance (COP). Analyses have been performed for 10 kW water-ammonia absorption and activated carbon-ammonia adsorption chillers. For both systems, the first law efficiencies have been compared and the optimum efficiency has been investigated under different operating conditions. Analysis revealed that under any operating condition, the COP is always higher for the absorption chiller and its maximum value is 0.6, which is almost twice that of the adsorption chiller (COP = 0.35), for 10 kW systems operating at evaporator and condenser/absorber temperatures of 2°C and 30°C, respectively. The adsorption system requires a higher energy input to produce the same cooling effect as compared to the absorption system.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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 designNot applicable
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

Citations3
Published2018
Admission routes1
Has abstractyes

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