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Record W2964157369 · doi:10.1002/cjce.23599

Determination of clay content in Canadian oil sands using x‐ray florescence spectroscopy for diagnosis of ore processability

2019· article· en· W2964157369 on OpenAlexaffvenueabout
An Li, Zhenghe Xu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIlliteOil sandsTailingsAsphaltAshingMineralogyContaminationEnvironmental scienceMaterials scienceChemistryClay mineralsMetallurgy

Abstract

fetched live from OpenAlex

Abstract In this study, a simple and robust method based on the use of x‐ray fluorescence (XRF) spectroscopy to measure potassium content as an indicator of illite in oil sands was proposed and tested. The XRF results of illite determination were compared with those determined using the conventional methylene blue titration (MBT) method. To test the suitability of the XRF method for determining illite content in various streams of oil sands processing as a diagnosis tool, a toluene‐diluted‐bitumen solution was used to contaminate solid surfaces prior to their analysis by the XRF and MBT methods. In order to remove the organic matter from the bitumen‐contaminated solids for evaluating the XRF and MBT method, toluene washing and low temperature ashing were applied to the bitumen‐contaminated solids prior to their analysis. The robustness of the XRF analysis in comparison with the MBT method was further confirmed by analyzing the solids extracted from bitumen froth and tailings stream. The results show that the XRF method was more tolerant to surface contamination and therefore more reliable in determining illite content in oil sands and relevant processing streams. It was also found that the potassium contents measured by the XRF method on solids from 10 ores show a strong correlation with the corresponding fines contents of these ores, indicating that the XRF method can potentially be used to determine the fines content of oil sands and related processing streams.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.988

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 head0.017
GPT teacher head0.224
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

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

Citations10
Published2019
Admission routes3
Has abstractyes

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