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Record W2050508872 · doi:10.2118/09-09-19-tn

Centrifugation Options for Production of Dry Stackable Tailings in Surface- Mined Oil Sands Tailings Management

2009· article· en· W2050508872 on OpenAlexaff
R.J. Mikula, V.A. Munoz, Oladipo Omotoso

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsTailingsOil sandsCentrifugeSiltEnvironmental scienceAsphaltWaste managementExtraction (chemistry)GeologyEngineeringChemistryMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract Water availability is beginning to impact oil sands development and, as a result, several technologies to increase the percentage of recycled water are being evaluated. One such option being re-evaluated is the use of centrifuges to produce dry tailings that can accommodate overburden and soil replacement. Previous evaluations of centrifuge performance to capture water from the clay and silt tailings (mature fine tailings) components demonstrated some success but, at the time, at unacceptable costs. A better appreciation of the long-term costs of mature fine tailings storage has prompted a re-evaluation of centrifuge technology. The use of additives to improve centrifuge performance has significantly improved the results that can be achieved. Aside from the obvious positive environmental benefit of reclaiming the fluid fine (mature fine) tailings, the increase in the amount of water recycled will reduce the demand for fresh water from the Athabasca River. This paper discusses a laboratory-scale study of the water chemistry and clay/silt feed properties affecting centrifuge performance, as well as the results of a 20 tonne per hour pilot. Introduction The term dry stackable tailings is commonly used in oil sands tailings management to mean a mineral stream left over from the bitumen extraction process which can be stored without the need for dikes or other fluid containment structures. The use of water in the bitumen extraction process results in mineral tailings streams in the form of slurries or suspensions that require containment. In general, surface mined oil sands tailings fall into three categories: sand tailings, fine tailings and froth treatment tailings. Although the froth treatment tailings have important environmental implications due to their contamination with solvents or diluents from the froth treatment process, they are generally combined with the other tailings prior to discharge into the recycle water area (tailings settling pond). It is typically a straightforward process to create dry stackable tailings from the sand tailings, and they are often used to build the containment for the remaining fluid fine tailings. Another common practice in the surface mined oil sands industry is to define sand as mineral particles larger than 44 micrometres (µm). Fines are therefore smaller than 44 µm, and this definition can be useful when discussing the long-term tailings properties during mine planning. It has been demonstrated very clearly, however, that it is the clay content that determines all of the important properties of the fine tailings or fluid fine tailings (or mature fine tailings when the solids content exceeds about 25 wt%)(1–3). Over the entire mine, of course, the average clay-to-fines ratio is fixed and the fines content can be a useful approximation tool for large-scale planning purposes and the prediction of sand volumes and the fine tailings volume that may require containment. The clay content, often expressed as a clay-to-fines ratio, can vary across an oil sands lease by as much as a factor of four. It is therefore essential that the clay content, clay-to-water ratio or clay-to-fines ratio be understood in order to predict the properties of the fluid fine tailings on a daily or monthly basis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

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