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Record W2914029704 · doi:10.1109/efea.2018.8617096

Barriers to the use of Low GWP Refrigerants in the Refrigeration and Air Conditioning Sector in Mauritius

2018· article· en· W2914029704 on OpenAlexaboutno aff
Raj Kumar Dreepaul, Khalil Elahee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationAir conditioningMontreal ProtocolEnvironmental scienceEnvironmental economicsEnvironmentally friendlyCarbon dioxideHeat pumpProcess engineeringComputer scienceWaste managementEngineeringOzone layerChemistryEconomicsHeat exchangerMeteorologyEcologyMechanical engineeringGeography

Abstract

fetched live from OpenAlex

This paper is concerned with the issue of phasing out of Hydrofluorocarbons (HFCs) in Mauritius following the Kigali amendments of the Montreal protocol. For this purpose, a gap analysis was carried out among the major stakeholders in the Refrigeration and Air Conditioning (RAC) sector in Mauritius concerning the introduction of environment friendly refrigerants. Based on the gap analysis, it was found that the most frequently used refrigerants in the sector were HFCs and, to a lesser extent, hydrochlorofluorocarbons (HCFC). However, because of their negative influence on the environment, natural refrigerants such as ammonia (NH3), carbon dioxide (CO2) and Hydrocarbons (HC), having minimal impact on the environment, are being considered. On the other hand, HFOs, a new family of synthetic refrigerants also having low GWP have just appeared on the market. While these refrigerants have several advantages in terms of energy savings and thermodynamic efficiency, they also presents some drawbacks related namely to safety and availability on the market. In this paper, we discussed the major gaps in the implementation of these aforementioned new alternatives (i.e. NH3, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , HC and HFOs). Additionally, we proposed some solutions to cater for these gaps. Finally, some conclusions are drawn on future possible policy and decision making with regards to the implementation of the above technology.

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 score0.321
Threshold uncertainty score0.252

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 head0.021
GPT teacher head0.230
Teacher spread0.208 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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