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Record W7075293473

Dewatering of oil sands tailings with an electrokinetic geocomposite

2017· other· en· W7075293473 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsDewateringTailingsOil sandsConsolidation (business)Electrokinetic phenomenaBauxiteTailings damComminutionDrainage
DOInot available

Abstract

fetched live from OpenAlex

The oil sands industry generates large quantities of mineral waste, such as fluid fine tailings (FFT), whose disposal is often challenging. The use of planar electrokinetic geocomposites (eGCPs) in FFT disposal areas could improve in-situ dewatering by allowing water to drain after it is expulsed during consolidation and by permitting the use of electro-osmosis (EO) to displace a significant portion of the remaining water.\nFour dewatering experiments involving mature fine tailings (MFT) are presented here: in three of them increasing normal stresses are applied on the MFT followed by EO; in the fourth test, EO is applied before the normal stress is increased. The results show that eGCPs adequately filter MFT; the particles did not clog or blind the filter. MFT were significantly dewatered under an applied normal stress followed by EO. EO was very efficient at extracting the more tightly bound water that remained after mechanical dewatering. In one configuration, a significant improvement of the shear strength from nearly 0 kPa to a mean value of 25 kPa was obtained, which is significantly higher than the 10 kPa required by Alberta regulations. The final solids content in that case was about 70%, starting from an initial solids content of 45%. The energy required for that test was 10.6 kW h per dry tonne.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.666
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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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