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Record W2793955945 · doi:10.1504/ijret.2018.10011074

Solar sorption cooling for residential air-conditioning applications

2018· article· en· W2793955945 on OpenAlexaff
Paul Henshaw, Julia Aman, 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
KeywordsAir conditioningSorptionEnvironmental scienceConditioningEnvironmental engineeringWaste managementMeteorologyEngineeringChemistryPhysicsMechanical engineeringMathematicsAdsorption

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2018
Admission routes1
Has abstractyes

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