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Record W3199636278 · doi:10.36487/acg_repo/2115_31

Paste backfill continuous pour: Red Lake operations case study

2021· article· en· W3199636278 on OpenAlexaff
Jeffrey Oke, Katie Hawley, Tikou Belem, Ali Saadatmand Hashemi

Bibliographic record

VenuePaste/˜Pœaste · 2021
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsTailingsConsolidation (business)CementitiousGeotechnical engineeringCementShrinkageMaterials scienceEngineeringComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Cemented paste backfill (CPB) has become known as a superior secondary ground support technique and mine tailings storage method for stoping. Extensive scientific research has been conducted by the authors on CPB to provide the Red Lake operation (RLO) of Evolution Mining with an optimised backfill placement process. CPB mechanical/viscous behaviour is governed by its plasticity, the tailings particle size distribution, particle shape, mineralogical composition, water content, and the type and dosage of binder used (e.g. cement and/or supplementary cementitious material). For the CPB design, it is necessary to have a thorough understanding of the combined effects of filling rate, self-weight consolidation (volume shrinkage/settlement), and binder hydration rate for each specific paste mixture. Due to these complex factors and interactions, a rational CPB material design process was assessed to demonstrate the safety aspects related to a continuous pour. For this purpose, an extensive field monitoring program was required to quantify the CPB performance and characteristics. There are two different ways of optimising the CPB design to maximise placement rate: (i) optimising the type and amount of binder added to the system, and (ii) optimising the CPB placement process underground. Optimisation of binder type and dosage is relatively easy as the required backfill stand-up strength is based on block dimensions, stope stability, and extraction sequencing. Optimisation of backfill placement is more difficult, as the understanding of the viscous/pseudo-plastic behaviour of the CPB is required throughout the pour. Typically, a three-stage (plug–cure–main) pouring strategy is implemented to mitigate the lack of understanding of the CPB behaviour during placement. The CPB plug (i.e. height of initial pour regime) is designed to mitigate the pressure on the backfill fence. Once this CPB plug has cured and reached the required shear strength, the backfill fence is no longer required, and the remainder of the stope void (main) can be filled. However, if the curing time of the in situ paste is accelerated due to the binder exothermic hydration process (volume shrinkage) and backfill self-weight consolidation (water drainage), it is possible to have a more aggressive pour regime provided that all the required design parameters, site procedures, company protocols, and critical controls are met. RLO implemented instrumentation, and an early-age CPB strength testing program to evaluate whether their CPB can be poured continuously or at least more aggressively. Four stopes were instrumented with total earth pressure cells (TEPCs) and piezometers to capture the pressures acting on the fill fence structures and the strengthening response of the CPB plug within the stopes. This paper summarises the results of each of the tests performed. Based on the results obtained from this study, it was concluded that RLO can safely conduct continuous CPB pours with appropriate safeguards and protocols in place. It is important to note that this paper is a summary of the CPB performance and characteristics in RLO longhole stopes and does not reflect site-specific safety procedures, protocols, and critical controls required for a more aggressive pouring regime.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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 designNot applicable
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

Citations8
Published2021
Admission routes1
Has abstractyes

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