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Record W2313240783 · doi:10.1021/ef200203f

Role of Dissolving Carbon Dioxide in Densification of Oil Sands Tailings

2011· article· en· W2313240783 on OpenAlexaffabout
Ren Zhu, Qingxia Liu, Zhenghe Xu, Jacob H. Masliyah, Aman Khan

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsTailingsOil sandsDissolutionCarbon dioxideSupersaturationEnvironmental scienceSedimentationSettlingWaste managementSedimentGeologyEnvironmental engineeringMaterials scienceChemistryMetallurgy

Abstract

fetched live from OpenAlex

Carbon dioxide (CO 2 ) was shown as a promising alternative for oil sands tailings treatment with economical and environmental benefits. This study aims to understand the role of CO 2 addition in densification of oil sands tailings. In this study, CO 2 was pressurized into two industrial whole tailings provided by Syncrude Canada Ltd. and Canadian Natural Resource Ltd. The optimal initial settling rate, supernatant clarity and solids content of sediment were achieved at a CO 2 partial pressure of about 100 kPa. The improvement on densification of oil sands tailings by CO 2 was mainly attributed to pH reduction under various CO 2 partial pressures. The zeta potential of fines became less negative with decreasing pH, enhancing coagulation of fine solids. On the other hand, CO 2 bubbles formed by dissolved gas under supersaturation pressure led to a less clear supernatant by disturbing the formed sediments. Supersaturation with nitrogen was applied to the oil sands tailings to verify the influence of dissolved gas on solids sedimentation. The limit to CO 2 sequestration by oil sands tailings was experimentally evaluated.

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 score0.041
Threshold uncertainty score0.405

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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

Citations23
Published2011
Admission routes2
Has abstractyes

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