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Record W4364361670 · doi:10.1680/jgein.22.00333

Chemical durability of bituminous geomembranes (BGMs) in heap leach pad applications

2023· article· en· W4364361670 on OpenAlexaff
F.B. Abdelaal, A. Samea

Bibliographic record

VenueGeosynthetics International · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsQueen's University
Fundersnot available
KeywordsHeap leachingAsphaltGeomembraneMaterials scienceLeaching (pedology)Ultimate tensile strengthDissolutionMetallurgyCopperComposite materialChemical engineeringEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

The degradation behaviour of a 4.8 mm thick elastomeric bituminous geomembrane (BGM) immersed in pH 0.5, 9.5, and 11.5 synthetic mining solutions is examined over 26 months at 22, 40, 55 and 70°C. The low pH solution simulates the leach solutions found in copper, nickel, and uranium heap leach pads while the two high pH solutions simulate the chemistry and pH found in gold and silver heap leaching facilities. The mechanical, rheological, and chemical properties are examined at different incubation times to assess the degradation in the BGM at different temperatures. It is shown that the degradation rates of all properties are faster in pH 11.5 and 9.5 than in pH 0.5. Additionally, the BGM started to exhibit degradation in its mechanical properties even with a slightly degraded bitumen coat in all the mining solutions at elevated temperatures. The time to nominal failure of the BGM is predicted at different field temperatures using Arrhenius modelling. Due to the relatively fast degradation in the mechanical properties of the BGM, especially at temperatures above 50°C, the tensile strains in the BGM in the field should be limited so it can meet the required liner design life of heap leaching applications.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.457

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 head0.250
Teacher spread0.238 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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