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Rare Earth Elements in Alberta Oil Sand Process Streams

2017· article· en· W2604540618 on OpenAlexaboutno aff
Elliot Roth, Tracy L. Bank, Bret Howard, Evan Granite

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
FundersNational Energy Technology Laboratory
KeywordsTailingsOil sandsOil shaleFraction (chemistry)STREAMSInductively coupled plasma mass spectrometryRare earthChemistryEnvironmental scienceEnvironmental chemistryMass fractionMineralogyMass spectrometryGeologyMaterials scienceChromatography

Abstract

fetched live from OpenAlex

The concentrations of rare earth elements in Alberta, Canada oil sands and six oil sand waste streams were determined using inductively coupled plasma mass spectrometry (ICP–MS). The results indicate that the rare earth elements (REEs) are largely concentrated in the tailings solvent recovery unit (TSRU) sample compared to the oil sand itself. The concentration of lanthanide elements is ∼1100 mg/kg (1100 ppm or 0.11 wt %), which represents a >20× increase in the concentration compared to the oil sand itself and a >7× increase compared to the North American Shale Composite (NASC). The process water, which is used to extract the oil from oil sands, and the water fraction associated with the different waste streams had very low concentrations of REEs that were near or below the detection limits of the instrument, with the highest total concentration of REEs in the water fraction being less than 10 μg/L (ppb). Size and density separations were completed, and the REEs and other potentially interesting and valuable metals, such as Ti and Zr, were concentrated in different fractions. These results give insights into the possibility of recovering REEs from waste streams generated from oil sand processing.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.995

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)0.0060.000

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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.

Study designObservational
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

Citations40
Published2017
Admission routes1
Has abstractyes

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