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Record W4410452314 · doi:10.1016/j.jece.2025.117131

Value adding to acid mine drainage: Synthesis of high purity alumina and recovery of gypsum

2025· article· en· W4410452314 on OpenAlexfundno aff
Cameron J. Johnston, Rachel A. Pepper, David M. Hunter, Wayde N. Martens, Sara J. Couperthwaite

Bibliographic record

VenueJournal of environmental chemical engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsnot available
FundersDepartment for Industry and SkillsQueensland University of TechnologyInnovative Manufacturing CRCCanadian Anesthesia Research FoundationDepartment of Natural Resources
KeywordsGypsumAcid mine drainageValue (mathematics)DrainageEnvironmental scienceWaste managementMineralogyGeologyMetallurgyMaterials scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

With the depletion of natural resources, research into the recovery of valuable materials from various mine wastes is increasing. Reprocessing of mine waste provides an avenue to generate new revenue streams while alleviating pressure on storage requirements and reducing the environmental impact of the mining operation. The presented research demonstrates the recovery and synthesis high purity gypsum and high purity alumina (HPA) from acid mine drainage via lime precipitation, acid extraction and crystallisation. Acid mine drainage (pH 3.62) was neutralised using lime to two different pH targets of 6.5 and 8.5, the upper and lower limits of the ANZECC guidelines for agricultural and livestock water. The lime precipitation residue was washed using 20% hydrochloric acid (HCl), followed by water, to produce a gypsum product with a purity of 99.9 wt.%. Leachates produced from this stage were sparged with gaseous hydrogen chloride to precipitate aluminium chloride hexahydrate (ACH). To improve the purity of ACH the crystals were redissolved and recrystallised a further two times prior to thermal decomposition at 1200 o C to produce an α-alumina with a purity of 99.99 wt%.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.463

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 head0.002
GPT teacher head0.181
Teacher spread0.178 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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