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Feasibility of Using Coal Fly Ash for Mine Waste Containment

2009· article· en· W2127439227 on OpenAlexafffundabout
Muluken B. Yeheyis, Julie Q. Shang, Ernest K. Yanful

Bibliographic record

VenueJournal of Environmental Engineering · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Environment
KeywordsFly ashLeachateBentoniteHydraulic conductivityAcid mine drainageCoalEffluentWaste managementCoal miningEnvironmental scienceChemistryPulp and paper industryEnvironmental chemistryGeologyEnvironmental engineeringGeotechnical engineeringSoil scienceSoil water

Abstract

fetched live from OpenAlex

This study investigates the feasibility of using coal fly ash and fly ash-bentonite mixtures as a barrier material for mine waste. The hydraulic conductivity of the coal fly ash was measured to be in the order of 2×10−9 m/s when it was permeated with deionized water, and this value decreased significantly when the permeant was switched to acid mine drainage (AMD). The addition of bentonite to coal fly ash lowered the hydraulic conductivity during water permeation but no further significant change was observed upon switching the permeant to AMD. Chemical analyses on the effluent from the hydraulic conductivity tests indicated that heavy metals present in AMD were attenuated and were well below the leachate criteria set by the Ontario Government. X-ray diffraction and scanning electron microscopy analyses results of postpermeation samples showed significant structural differences and formation of secondary minerals after AMD permeation. The results of this study suggest that the addition of 10% bentonite to coal fly ash reduced the hydraulic conductivity of the coal fly ash to less than 1×10−9 m/s and improved the chemical compatibility for mine waste containment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.202
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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 routes3
Has abstractyes

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