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Record W2006856196 · doi:10.1080/09593330801984290

REMOVAL OF HEAVY METALS FROM OIL SLUDGE USING ION EXCHANGE TEXTILES

2008· article· en· W2006856196 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Technology · 2008
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil sludgeAcetoneVanadiumIon exchangeChemistryCadmiumAqueous solutionAqueous two-phase systemWaste managementRefineryPulp and paper industryEnvironmental chemistryInorganic chemistryIonOrganic chemistry

Abstract

fetched live from OpenAlex

Development of a new simple and economic method for heavy-metal removal from oil sludge using ion exchange textiles was the main objective of this research. Three experimental stages were developed for this purpose using the bottom tank oil sludge from the Shell Canada refinery in Montreal, Canada. The first stage consisted of the direct application of ion exchange to oil sludge. The second stage included the pretreatment of oil sludge with organic solvents prior to the application of ion exchange process. The third stage included the pretreatment of oil sludge with an aqueous solution in order to extract heavy metals to the aqueous phase and then apply ion exchange textiles to the aqueous phase. Best results were obtained when acetone was used as an organic solvent leading to a total removal of vanadium while cadmium, zinc, nickel, iron, copper by 99%; 96%; 94%; 92% and 89%, respectively.

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 categoriesInsufficient payload (model declined to judge)
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 score0.029
Threshold uncertainty score1.000

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)0.0010.000

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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