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Record W2034368155 · doi:10.2118/2009-167

Dry Stackable Tailings as a Tailings Management Option: Preliminary Laboratory and Field Experience Using Centrifuges

2009· article· en· W2034368155 on OpenAlexafffundabout
R.J. Mikula, Trong Dang‐Vu, Oladipo Omotoso, Rick Lahaie

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsSyncrude (Canada)Natural Resources Canada
FundersSyncrude
KeywordsTailingsEnvironmental scienceWaste managementMining engineeringGeologyMaterials scienceMetallurgyEngineering

Abstract

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Abstract Water availability and water storage will have impacts on oil sands development as surface-mined production increases. As a result, several technologies designed to increase the percentage of recycle water are being evaluated. One such option is the use of centrifuges to produce dry stackable tailings. Dry stackable tailings can accommodate overburden and soil replacement, and ultimately lead to the restoration of the original boreal forest. Laboratory investigations and lab scale pilot studies have demonstrated that dewatering of mature fine tailings by centrifugation is a function of both the clay content and chemical properties of the tailings being dewatered. The use of additives to improve centrifuge performance has significantly improved the results that can be achieved. Laboratory-scale successes were confirmed with field scale pilot testing recently completed by Syncrude. This paper discusses the laboratory scale centrifuge performance, along with the field pilot results. Introduction Tailings management in surface-mined oil sands is complicated by mine site topography, fines and clay content in the ore, and ore water chemistry(1). The latest ERCB (Alberta Energy Resources Conservation Board) tailings directive proposes to monitor tailings management in much the same way as bitumen production is managed(2). In any case, there will be several management strategies that may be implemented to meet this directive, including such things as composite tailings (consolidated tailings, non-segregating tailings, etc), sand capping, accelerated dewatering, mature fine tailings (MFT) drying, and centrifuged MFT(3). This paper discusses the preliminary results of a recent pilot scale verification of the centrifuged MFT technology. Centrifugation of MFT as a concept was tested in 1991 in order to provide an upper limit to fluid fine tailings handling costs. Although successful it was felt that the capital and operating costs would be prohibitive(4). Centrifuged fluid tailings was tested much more extensively in 1998 to 2001, and was piloted on a 20 ton per hour extraction plant in Vernal, Utah, for the Asphalt Ridge tar sands(5,6). Although there are slight differences in the clay content between Asphalt Ridge and the Athabasca oil sands, it was felt that similar technology would be applicable to the Alberta oil sands industry. Laboratory scale testing confirmed the positive results from Utah, and larger scale testing was quickly commissioned by both Syncrude and Suncor to verify the small scale results(7). With confirmation of the lab or bench scale centrifuge test program, Syncrude initiated a large scale pilot test at approximately 70m3 per hour of MFT (mature fine tailings). This would be equivalent to approximately 700 tonnes of ore per hour, a scale significantly greater than the pilot studies undertaken in Utah. Surface-mined oil sands convention defines sand as the mineral fraction larger than 44 microns and the fines as the mineral fraction smaller than 44 microns(8). The coarse tailings are predominantly made up of the sand fraction, and the fine tailings are predominantly made up of fines. It has been demonstrated that it is the clay fraction, as a size and mineral that defines the tailings properties, and in particular, the tailings volume and water holding capacity(9).

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 score0.472
Threshold uncertainty score0.858

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 head0.012
GPT teacher head0.220
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.

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

Citations0
Published2009
Admission routes3
Has abstractyes

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