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Comparative Study on Evaporative Cooling, Air Conditioning, and Environment-Friendly Refrigerants

2025· article· en· W4407757042 on OpenAlexaboutno aff
Jyoti Soni, Alok Choube, P. K. Jhinge

Bibliographic record

VenueInternational Research Journal of Multidisciplinary Scope · 2025
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantAir conditioningEvaporative coolerEnvironmental scienceConditioningEnvironmentally friendlyRefrigerationWaste managementProcess engineeringMeteorologyEngineeringMechanical engineeringGeographyMathematicsBiologyHeat exchangerEcology

Abstract

fetched live from OpenAlex

In this research article, a complete analysis of the thermodynamic performance of various refrigerants in air conditioning units is presented, along with a system of evaporative air conditioning that has been suggested for use in a variety of climate situations, including moderately dry, hot, and arid, and humid climates. Most air conditioning systems use refrigerants such as R22 and R407C, which must be phased out due to their adverse effect on the environment like ozone depletion, greenhouse effect climate change, etc. as per the Montreal Protocol and Kyoto Protocol. The current literature survey presents that refrigerants R1234yf and R1234ze (e) are a more reliable longterm replacement for R22 in air conditioners. In the world, 15% of the total electricity consumption is used in VCRbased systems and these cooling systems are responsible for increasing global warming by 10% globally. In scenarios of 2040, overall energy consumption will grow by 10% per person, as per the International Energy Agency (IEA 2021). Thus, this research study also comprehensively assesses energy-efficient cooling systems at minimum cost. Evaporative cooling systems are quite effective and can be used with liquid or solid desiccant units to maintain various climatic zones. The novel solution is better than the air conditioning system from the cost point of view.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.401
Teacher spread0.346 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Research Journal of Multidisciplinary ScopeSame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207