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Record W2068465768 · doi:10.5383/ijtee.04.01.014

Modeling Tool for Air Stripping and Carbon Adsorbers to Remove Trace Organic Contaminants

2011· article· en· W2068465768 on OpenAlexvenueno aff
Khaldoon A. Mourad, Ronny Berndtsson, Wa’il Y. Abu-El-Sha’r, Abdalla M Qudah

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAir strippingTRACE (psycholinguistics)Stripping (fiber)Environmental chemistryContaminationEnvironmental scienceWaste managementCarbon fibersChemistryEnvironmental engineeringMaterials scienceEngineeringEcology

Abstract

fetched live from OpenAlex

Removal of trace organic contaminants from aqueous solutions by air strippers (AS) and fixed bed carbon adsorper (FBCA) has been studied.A trace organic treatment tool has been developed to capture and adapt the best-known design procedures and to have all regulated trace organics, their physical and chemical properties, and the corresponding maximum concentration limits.Outputs include the selected treatment method and the final design parameters of air stripper or fixed bed carbon adsorber.Running the model shows that water temperature is a very important factor in designing AS and FBA.It also shows that the best air pressure values, in AS, ranged between 150 -200 ATM.And there is a big relation between the column size and the packing material.On the other hand, it shows that FBA diameter has an obvious effect on the needed volume, and the best values ranged between 1.2 -2.5 m.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.195
Teacher spread0.185 · 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 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

Citations3
Published2011
Admission routes1
Has abstractyes

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Same venueInternational Journal of Thermal and Environmental EngineeringSame topicMembrane Separation TechnologiesFrench-language works237,207