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Record W4293761246 · doi:10.3390/min12091087

Reuse of Acid Bioleachate in Bacterial Oxidation of a Refractory Gold Sulfide Concentrate

2022· article· en· W4293761246 on OpenAlexafffund
S.H. Andrianandraina, Houshang Alamdari, Jean-François Blais

Bibliographic record

VenueMinerals · 2022
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité Laval
FundersCanadian Bureau for International Education
KeywordsReuseGold cyanidationSulfideChemistryLeachateRefractory (planetary science)PhosphorusNutrientZincBioreactorPulp and paper industryEnvironmental scienceEnvironmental chemistryMetallurgyWaste managementInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Bacterial pre-oxidation of refractory gold concentrates generates large volumes of leachate and requires a significant supply of nutrients to support bacterial growth. Therefore, bioleachate reuse reduces both water consumption and the nutrients required for the process. However, the efficiency of this method and its benefit need to be further explored. In the present study, two tests on the reuse of bioleachate in new cycles of bacterial oxidation were carried out to evaluate the efficiency and the benefit of bioleachate reuse. Our results showed that the reuse of bioleachates could reduce nitrogen and phosphorus requirements by 40% and 36%, respectively, after a 14-day biooxidation stage in a stirred tank bioreactor. We also showed that the reuse of bioleachate had a positive effect on the recovery of gold in a subsequent 48 h treatment by cyanidation. The gold recovery rate (initial concentration of 44 mg/kg) remained unchanged at 90% after the two bioleachate recirculation loops. The reuse of bioleachate also made it possible to increase the solubilization rates of other metals from the sulfide concentrate. Thus, the solubilization yields of copper (initial concentration of 3587 mg/kg) and zinc (initial concentration of 27,315 mg/kg) increased, respectively, from 14.8% and 40.2% to 37.5% and 99.6% after the two bioleachate recirculation loops.

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 score0.039
Threshold uncertainty score0.493

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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
Published2022
Admission routes2
Has abstractyes

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