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Record W4372346426 · doi:10.31428/10317/11580

Optimización energética de los refrigerantes R152a, R1234yf, R290, R1270, R600a y R744, como alternativa al R134a en un armario de refrigeración vertical

2023· article· es· W4372346426 on OpenAlexaboutno aff
Sánchez Daniel, Andreu Nácher Alejandro, Calleja Anta Daniel, Nebot Andrés Laura, Vidan Falomir Francisco, Llopis Doménech Rodrigo, Cabello Ramón, Larrondo Sancho Rafael

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Con la entrada en vigor de la normativa Europea F-Gas (2014) y la posterior enmienda de Kigali al protocolo de Montreal (2019), los equipos frigoríficos autónomos han sufrido cambios para poder sustituir los refrigerantes originales R134a y R404A, a otras sustancias con menor impacto medioambiental tales como R290, R600a o incluso R744 (CO2). Sin embargo, existen otras sustancias que también pueden ser consideradas como alternativas, como es el caso de los fluidos puros R1270, R152a y R1234yf. En esta ponencia se analiza energéticamente cómo funciona un armario de refrigeración vertical autónomo, cuando la carga de refrigerante es optimizada para los fluidos anteriormente mencionados. Dicha optimización busca minimizar el consumo energético del equipo en unas condiciones ambientes de 30ºC y 60% (Clase climática III), manteniendo una temperatura promedio de producto en torno a 3ºC. Tomando como referencia el refrigerante R134a, los resultados obtenidos muestran un ahorro energético del 27.5 %, 26.3 %, 13.7 %, 3.9 % y 1.2 %, para los refrigerantes R290, R1270, R152a, R744 y R600a, respectivamente, mientras que el uso del refrigerante R1234yf, supone un incremento del 4. 1%.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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