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Record W4366085487 · doi:10.11159/enfht23.175

Performance Improvement Of Solar-Assisted Desiccant Cooling System By Changing Collector Type And Stage Number

2023· article· en· W4366085487 on OpenAlexaff
Mehran Bozorgi, Kasra Ghasemi, Syeda Humaira Tasnim, Shohel Mahmud

Bibliographic record

VenueProceedings of the World Congress on Momentum, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDesiccantStage (stratigraphy)Environmental scienceMaterials scienceProcess engineeringComputer scienceEngineeringGeologyComposite material

Abstract

fetched live from OpenAlex

Solar energy systems have been recognized as a significant component of HVAC systems during the last two decades, providing thermal and electrical energy for a variety of applications.Meanwhile, solar-assisted cooling systems present a great opportunity to provide thermal comfort conditions in hot and humid climate weather.In this research, a solar-assisted desiccant cooling system is presented and its performance in the hot and humid climate of Jakarta, Indonesia is evaluated using the TRNSYS 18 software.To improve and optimize the efficiency of the system, the number of stages in the cycle is changed from one to three.Furthermore, as the novelty of the research, the effect of different types of solar collectors including Direct Absorption Solar Collector (DASC), Photovoltaic Thermal (PVT), and evacuated tube collector is investigated and the COP of the system is compared.According to the results, the suggested system with two sets of desiccant and heat wheels has a greater COP.Additionally, the system with one stage has a higher COP than the system with three stages.Furthermore, using PVT solar collectors is suggested for these systems as they can provide both thermal and electrical energy for the system.PVT collector increases the system's COP to 1.3, while the evacuated tube and direct absorption solar collectors lower it to 1.215 and 1.199, respectively.

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 score0.105
Threshold uncertainty score0.590

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Momentum, Heat and Mass TransferSame topicAdsorption and Cooling SystemsFrench-language works237,207