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Record W3015737013 · doi:10.5004/dwt.2020.25105

Performance of bubble column humidification-dehumidification (HDH) desalination system

2020· article· en· W3015737013 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDesalination and Water Treatment · 2020
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDesalinationAirflowVolumetric flow rateBubbleEnvironmental scienceWater desalinationWater columnEnvironmental engineeringProduction rateColumn (typography)Materials scienceThermodynamicsMechanicsProcess engineeringChemistryMechanical engineeringEngineeringPhysicsGeologyMembrane

Abstract

fetched live from OpenAlex

ABSTRACT Bubble column humidification and dehumidification (HDH) system is considered one of the promising and new techniques for enhancing the performance of the HDH desalination systems. In this paper, we experimentally examine the performance of a bubble column water and air heated HDH systems. The effect of water column height in the humidifier, water temperature, and air temperature and flow rate on gain output ratio (GOR), production and effectiveness are investigated and discussed. Results show that the system can produce 0.6 L/h freshwater and GOR can reach 0.95. At low temperatures, increasing airflow rate leads to an increase in the production at a high rate than the rate of increase in heat input. Therefore, GOR slightly increases at a higher airflow rate. Furthermore, GOR increases with increasing water temperature in the dehumidifier because the decrease in input energy needed to cool water in the dehumidifier has more impact than the decrease in the production.

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.

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 score0.266
Threshold uncertainty score0.761

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 head0.041
GPT teacher head0.262
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