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Record W4401583704 · doi:10.3390/min14080822

Study on Column Leaching Behavior of Low-Grade High Calcium and Magnesium Copper Ore

2024· article· en· W4401583704 on OpenAlexaff
Jingyuan Xu, Shuchen Qin, Chaozhen Zheng, Honghong Sun, Baojun Yang, Sanping Liu, Guanzhou Qiu, Derek O. Northwood, Kristian E. Waters, Hao Ma

Bibliographic record

VenueMinerals · 2024
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsMcGill UniversityUniversity of Windsor
Fundersnot available
KeywordsLeaching (pedology)Sulfuric acidCopperChemical reactionChemistryLixiviantInorganic chemistryMagnesiumCalciumDiffusionChemical kineticsKineticsGeology

Abstract

fetched live from OpenAlex

This paper studies the process mineralogy, mechanism, and kinetics of column leaching behavior of low-grade high-calcium–magnesium copper ore. The effect of sulfuric acid concentration, leach solution spraying intensity, and material particle size on column leaching kinetics is discussed. The kinetic analysis of column leaching of copper indicates that sulfuric acid concentration has a significant impact. As sulfuric acid concentration increases, the limiting step of reaction shifts from chemical reaction control to a combination of chemical reaction and diffusion mixing control. Spraying intensity also affects copper column leaching; increasing intensity shifts the limiting step from diffusion control to mixing control, thereby mitigating the effects of diffusion control. Regarding other elements, it is found that iron leaching is primarily controlled by chemical reaction, while calcium leaching is mainly controlled by chemical reaction. As sulfuric acid concentration increases from 10 g/L to 20 g/L, the limiting step for calcium leaching shifts from chemical reaction control to chemical reaction and diffusion-mixing control.

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 score0.535
Threshold uncertainty score0.450

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 head0.032
GPT teacher head0.290
Teacher spread0.257 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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