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Record W1591491397 · doi:10.1115/imece2014-39949

Evaluation of Cooling Water Storage for Liquid-Desiccant Air Conditioning System

2014· article· en· W1591491397 on OpenAlexaff
Danial Salimizad, Chris McNevin, Stephen C. Harrison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsTRNSYSDesiccantAir conditioningEvaporative coolerLiquid desiccantEnvironmental scienceMoistureCooling towerProcess engineeringHumidityWater coolingNuclear engineeringMaterials scienceWaste managementMeteorologyEngineeringMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Liquid-desiccant (LD) dehumidification technology has been used to extract moisture from humid air while attempting to consume less electricity than traditional air-conditioning methods. An evaporative cooling tower (ECT) was used as a cooling device to reject the latent heat gained by the system to regenerate the desiccant solution. The performance of an ECT was evaluated both experimentally and through TRNSYS simulations to investigate optimal operating conditions. The ECT often operated in humid conditions which resulted in reduced heat rejection rates and ineffective operation. To improve performance, cooling water storage (CWS) was investigated as a way to reduce ECT usage during periods of higher ambient humidity. To undertake this study, the complete LD system, incorporating CWS, was modelled in TRNSYS for a range of typical operating conditions. The results indicated that operation of the CWS system reduced the electrical power consumption and increased the electrical coefficient of performance (COPE) of the liquid desiccant air conditioning unit system by up to 16%. The total cooling rate improved by up to 6%. Smaller gains in COPT and solar fraction were also found in the simulation results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.242
Teacher spread0.221 · 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
Published2014
Admission routes1
Has abstractyes

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