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Record W1208639265 · doi:10.2166/wqrj.2004.029

Determination of System Losses of Geosmin and MIB in Bench-Scale Filtration Apparatus

2004· article· en· W1208639265 on OpenAlexafffund
Sarah L.N. Elhadi, Peter M. Huck, Robin M. Slawson

Bibliographic record

VenueWater Quality Research Journal · 2004
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsUniversity of WaterlooWilfrid Laurier UniversityNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsGeosminFiltration (mathematics)AdsorptionVolatilisationChemistryWater treatmentFilter (signal processing)Pulp and paper industryEnvironmental chemistryChromatographyEnvironmental scienceWaste managementEnvironmental engineeringOdorEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Geosmin and 2-methylisoborneol (MIB) are two of the causative compounds responsible for the earthy/musty odour problem in drinking water. They are a major concern for the water treatment industry because they are difficult to remove by conventional water treatment practices and are fairly resistant to chemical oxidation. Various studies have been conducted at both bench and full scale to examine the removal of these compounds by adsorption and/or biological filtration. Lack of study at realistic odour compound concentrations and disregard for losses of these compounds by other means (i.e., volatilization or adsorption) represent some of the limitations of such studies. The bench-scale investigation reported herein focussed on system design and minimization of system losses. The results indicated that the preferred system design included the use of Teflon feed bottles, Teflon tubing, and a glass filter column in which the influent feed line descended close to the media surface. Such a design minimized system losses of both geosmin and MIB to 42 and 30%, respectively, based on target influent concentrations of 200 ng/L.

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)0.002
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 score0.005
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.075
GPT teacher head0.372
Teacher spread0.297 · 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

Citations17
Published2004
Admission routes2
Has abstractyes

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