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Record W1986064063 · doi:10.1179/037195504225006119

Alternative reagents for roasting Suncor oil sands fly ash

2004· article· en· W1986064063 on OpenAlexfundno aff
P. C. Holloway, Thomas H. Etsell

Bibliographic record

VenueMineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy Section C · 2004
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
FundersDivision of Materials ResearchSyncrudeSuncor Energy Incorporated
KeywordsRoastingFly ashEnvironmental scienceOil sandsMetallurgyPulp and paper industryMineralogyGeochemistryGeologyWaste managementMaterials scienceAsphalt

Abstract

fetched live from OpenAlex

Several roasting reagents, including Na2CO3, Na2SO4, NaNO3 and CaO, were tested on Suncor fly ash to try to improve the vanadium extractions achieved with salt roasting. Up to 95% V was extracted using Na2CO3 additions of greater than 50%, while the other reagents generally gave lower vanadium extractions than roasting with NaCl or than roasting with no reagents. Leaching with 100 g/l H2SO4 increased the vanadium extractions, but dissolved large amounts of Al and Si into solution. Leaching with 100 g/l Na2CO3 gave only modest improvements in the vanadium extraction, except for samples roasted with CaO, where up to 70% of the vanadium was leached. Thus, sodium carbonate is the only reagent tested that gave both high vanadium extractions and low levels of impurities in solution; thus, it could be used as an alternative to roasting with NaCl, but higher Na2CO3 additions (> 35%) would required to achieve vanadium extractions of greater than 75%. Characterisation with X-ray diffraction and scanning electron microscopy was also used to study the mineralogy of the roasted ash formed using these reagents.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.772

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)0.0000.000

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 head0.260
Teacher spread0.224 · 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 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

Citations10
Published2004
Admission routes1
Has abstractyes

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Same venueMineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy Section CSame topicMining Techniques and EconomicsFrench-language works237,207