MétaCan
Menu
Back to cohort
Record W2075485580 · doi:10.1002/cjce.20407

Study of cyclic operation of RO desalination process

2010· article· en· W2075485580 on OpenAlexvenueno aff
Mushir Ali, Abdelhamid Ajbar, Emad Ali, Khalid Alhumaizi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersKing Saud University
KeywordsDesalinationVolumetric flow ratePermeationMaterials scienceReverse osmosisFlow (mathematics)MechanicsWork (physics)AmplitudeThermodynamicsEnvironmental scienceMembraneChemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The performance of a tubular reverse osmosis (RO) process for water desalination was investigated when the unit was periodically forced. The study was performed using a dynamic model that was developed and validated in a previous work of the authors [Al‐haj Ali et al., Desalination; 245, 194–204 (2009)]. The forcing was carried out through feed pressure using both symmetric and asymmetric rectangular pulses. For a symmetric pulse, it was found that the permeate flow rate increased monotonically with the forcing amplitude. The improvement in the permeate flow rate was limited by the constraints on the operating pressure. For an asymmetric pressure pulse, the permeate flow rate reached a clear maximum for some values of ratio of pulse width to the period. For both pulses, the model predicted that the enhancement in the permeate flow rate was attributed to the reduction in the effect of concentration polarisation. Moreover, the trends obtained with both symmetric and asymmetric shapes were consistent with the experimental results reported in the literature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicMembrane Separation TechnologiesFrench-language works237,207