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Record W4389541295 · doi:10.17118/11143/20829

R718+r717 high temperature cascade heat pump with liquid injection invapor compression processes

2023· article· en· W4389541295 on OpenAlexafffund
Seyed Mojtaba Hosseinnia, Sébastien Poncet, Hakim Nesreddine, Dominique Monney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsHydro-QuébecUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaHydro-QuébecUniversité de Sherbrooke
KeywordsCascadeMaterials scienceCompression (physics)Vapor-compression refrigerationHeat pumpThermodynamicsComposite materialGas compressorChemistryRefrigerantHeat exchangerChromatographyPhysics

Abstract

fetched live from OpenAlex

Abstract: High temperature cascade heat pumps (HTCHP) with water, aka R718, at the high stage (HS) and ammonia, aka R717, at the low stage (LS) exhibit a great potential to satisfy both industrial heating and cooling demands while mitigating the global warming issue via industrial decarbonation/electrification as both refrigerants have extremely low global warming potential. In this study, a HTCHP with R718+R717 as HS/LS pair is proposed, and the thermodynamic analysis of the cycle is presented. Liquid refrigerant injection into the compression processes is the key factor in desuperheating the compression discharge temperature in both high and low stages. Additionally, the proposed cycle can benefit from low grade waste heat and renewable energy sources such as geothermal/ground/solar source in the form of heat recovery for heating the R717 evaporator. Coefficient of performance (COP), volumetric heating capacity (VHC), and COP×VHC maps at different cascade temperature lifts are also presented. The obtained results reveal that at a cascade temperature lift of 180°C (from Teva,R717 = -10°C to Tcon,R718 = 170°C), the cascade cycle can satisfy a 1 MW high temperature heating load with a COP of 1.35 where 438.6 kW cooling load can be delivered at -10°C.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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