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Record W4308718594 · doi:10.3390/en15218082

An Overview of Environment-Friendly Refrigerants for Domestic Air Conditioning Applications

2022· article· en· W4308718594 on OpenAlexaboutno aff
Kutub Uddin, Bidyut Baran Saha

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantMontreal ProtocolAir conditioningRefrigerationVapor-compression refrigerationEnvironmental scienceGlobal warmingGlobal-warming potentialHVACOzone layerEnvironmentally friendlyProcess engineeringEnvironmental economicsWaste managementGreenhouse gasEngineeringClimate changeMeteorologyOzoneMechanical engineeringEconomicsGeographyHeat exchangerEcology

Abstract

fetched live from OpenAlex

Low global warming potential (GWP) refrigerants for the next-generation air conditioning systems have been investigated with target domestic applications. High-GWP refrigerants are mostly used in climate control applications such as heating, ventilation and air conditioning (HVAC) and refrigeration systems. The majority of refrigerants are responsible for significant environmental issues such as ozone layer depletion and global warming. The Montreal Protocol and the Kyoto Protocol have been implemented to address such issues. In the meantime, authorities in many countries have taken the initiative to phase out the usage of environmentally harmful refrigerants in vapor compression refrigeration systems. Following the global warming mitigation scheme by many signatory countries, research interest has been focused on finding alternative refrigerants with low or ultra-low GWP. This study considered the research trend and development of low-GWP refrigerants while examining system performance, safety issues, and the equivalent environmental impact as the critical assessment parameters. Here, the focus is primarily set on the potential of refrigerant blends (HFCs + HFOs) where the GWP value of 300 is set as the threshold value. Targeted for domestic heat pump systems, the performance of such systems using various refrigerant blends is collated and discussed. Many blends offer innovative drop-in replacements for R410A-conforming F-gas regulations. The technical difficulties and realistic remedies for the existing refrigerants are also discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.401

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 head0.020
GPT teacher head0.274
Teacher spread0.254 · 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

Citations34
Published2022
Admission routes1
Has abstractyes

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