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Record W4415401354 · doi:10.3390/pr13103374

Performance Evaluation of the Ultrasonic Humidification Process for HDH Desalination Applications

2025· article· en· W4415401354 on OpenAlexafffund
Aurora C. Duran, Keny Parisheck, Mostafa H. Sharqawy

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsDesalinationAirflowUltrasonic sensorEfficient energy useEnergy consumptionProcess (computing)Renewable energyRangingAdaptability

Abstract

fetched live from OpenAlex

Water scarcity remains a critical global challenge, driving the need for efficient small-scale desalination technologies. This study presents experimental research on the performance evaluation of an innovative ultrasonic humidifier designed for the humidification–dehumidification (HDH) desalination process. A prototype was designed, incorporating a 1.7 MHz piezoelectric transducer. The efficiency of the humidifier, the vapor production rate, and the specific energy consumption were evaluated based on two operating parameters: water temperature, ranging from 30 °C to 60 °C, and airflow rate, ranging from 20 to 120 L/min. The results show that humidification efficiency increases with airflow rate, reaching values above 95% at a temperature of 60 °C, with an airflow rate of 60 L/min, decreasing slightly at higher flow rates. The system demonstrated optimal performance at 60–80 L/min, balancing high efficiency and vapor production with moderate energy demand. These findings demonstrate that ultrasonic humidification is a viable alternative, especially in decentralized applications, due to its low thermal energy requirements, compact design, and adaptability to intermittent renewable energy sources.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.036
GPT teacher head0.332
Teacher spread0.295 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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