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Record W4367154988 · doi:10.36487/acg_repo/2355_28

Selection and operation of Metso Outotec second generation paste thickener at the New Afton Mine

2023· article· en· W4367154988 on OpenAlexaff
Sam Carlberg, Simon C. Courtenay, Jennifer Katchen

Bibliographic record

VenuePaste/˜Pœaste · 2023
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsBanff CentreGeomechanica (Canada)University of Alberta
Fundersnot available
KeywordsSelection (genetic algorithm)Computer scienceProcess engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The New Afton Mine has been in commercial operation since 2012 with the New Afton Tailings Storage Facility (NATSF) used as the primary tailings deposition site. In order to process future underground B3 and C-Zone ores and extend the lifespan of the current operation to 2030, new thickened and amended tailings (TAT) facilities were installed. For the TAT facility, a high yield stress and solids concentration material was required for the mill tailings stream to facilitate amendment with cement and deposition to the Historic Afton open pit (APTSF). A 45 m diameter Metso Outotec second generation paste thickener was selected to achieve the target slurry characteristics and was commissioned in early 2022. This paper discusses the drivers behind the selection of the TAT process for in-pit tailings deposition as well as the selection of the paste thickener. Early operating results and the process optimisation required to achieve the desired overflow and underflow targets are reviewed. Early operating performance is compared to the original design test work. Furthermore, the paper details the value of Metso Outotec second generation paste thickener technology in achieving operational targets for tailings deposition at the New Afton Mine.

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 score0.518
Threshold uncertainty score0.464

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 head0.019
GPT teacher head0.213
Teacher spread0.193 · 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
Published2023
Admission routes1
Has abstractyes

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