MétaCan
Menu
Back to cohort
Record W2556349552 · doi:10.1021/acs.iecr.6b03357

An Innovative Process for the Recovery of Consumed Acid in Rare-Earth Elements Leaching from Phosphogypsum

2016· article· en· W2556349552 on OpenAlexafffund
Mugdha Walawalkar, Connie K. Nichol, Gisele Azimi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphogypsumLeaching (pedology)Phosphoric acidChemistryRare earthAnhydriteSulfateElectrowinningEnvironmental scienceGypsumMetallurgyMineralogyMaterials scienceRaw materialElectrolyteSoil science

Abstract

fetched live from OpenAlex

Many technologies relied upon by modern society, such as portable electronics and renewable energy systems, require the use of rare-earth elements (REEs). The global demand for REEs is increasing rapidly, and new developments for their recovery from secondary sources have been sparked. Phosphogypsum (PG), the byproduct of phosphoric acid production, is considered a secondary source for REEs. This research builds upon previous studies investigating the hydrometallurgical recovery of REEs from PG via acid leaching. The current study put the emphasis on developing processes to recover consumed acid in the leaching process. Here we propose an innovative process that relies upon the addition of calcium sulfate anhydrite seeds to the leached solution. Anhydrite seeding results in the rejection of calcium sulfate from the leached solution and reduced calcium concentration. Because the REE leaching efficiency is controlled by the solubility limit of PG, which is correlated to the calcium concentration, the drop increases the potential of the solution to leach more REEs. Thus, the solution can be recycled and reused as the leachant in subsequent leaching steps. This novel process is a promising technique to recycle consumed acid, lowering the operating costs and improving the efficiency.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.101
GPT teacher head0.371
Teacher spread0.270 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicExtraction and Separation ProcessesFrench-language works237,207