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Record W4416891704 · doi:10.1002/cjce.70194

Kinetic investigation on the recovery of copper and cobalt from sulphuric acid plant electrofilter dust

2025· article· en· W4416891704 on OpenAlexvenueno aff
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Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsCobaltCopperLeaching (pedology)Sulfuric acidDissolutionCopper extraction techniquesMetalChemical reaction

Abstract

fetched live from OpenAlex

Abstract Several waste dusts collecting equipment options are available for air pollution control in plants, the most commonly used being the electrofilter. Industrial wastes such as electrofilter dust are evaluated as a secondary resource owing to their critical metal contents. In this work, the results of studies on the extraction of cobalt and copper from electrofilter dust generated in sulphuric acid production plants are presented. The chemical and mineralogical compositions of the dust were investigated using instrumental analytical methods. The electrofilter dust consists of the main phases of haematite, quartz, and anhydrite. Also, electrofilter dust contains 1.43% copper and 0.28% cobalt. It was found that the process of copper and cobalt dissolution is characterized by chemical kinetics. When the acid leaching was applied in 1 M sulphuric acid in the presence of 0.05 M hydrogen peroxide, with the temperature 80°C and 2 h of leaching time, stirring rate of 200 rpm and liquid to solid ratio 20/1 v/w, cobalt and copper extraction achieved 88% and 98% respectively. As a result of studying the kinetics of acid leaching with sulphuric acid in the presence of oxidant hydrogen peroxide, activation energy and the type of reaction to chemical controlled model were determined.

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 score0.220

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 head0.010
GPT teacher head0.171
Teacher spread0.161 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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