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Record W4400528419 · doi:10.1016/j.mineng.2024.108824

Mass balance and economic study of a treatment chain for rare earths, base metals and precious metals recovery from used smartphones

2024· article· en· W4400528419 on OpenAlexafffund
Salmata Diallo, Lan Huong Tran, Dominic Larivière, Jean-François Blais

Bibliographic record

VenueMinerals Engineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPrecious metalBase metalBalance (ability)Base (topology)Rare earthMetallurgyChemistryEnvironmental scienceNatural resource economicsWaste managementMaterials scienceEngineeringMetalEconomicsMedicine

Abstract

fetched live from OpenAlex

In the present study, a comprehensive hydrometallurgical process was developed for the recovery of several types of metals from used cell phone printed circuit board assemblies. The overall process is divided into four stages of selective leaching of rare earth elements (REE), copper, nickel, silver and finally gold. After solubilization, the metals were purified using precipitation, solvent extraction, electrodeposition, and cementation techniques. Laboratory pilot-scale tests of the complete smart phone printed circuit board processing line showed REE, copper, nickel, silver, and gold recovery efficiencies of 78.0 %, 98.8 %, 81.8 %, 100 % and 96.1 %, respectively. The purity of the products obtained is evaluated at 94.8 % for rare earth oxides (70.0 % Nd2O3, 18.5 % Pr2O3, 2.8 % Dy2O3 and 2.4 % Sm2O3), 99.8 % for elemental copper, 94.3 % for nickel oxide, 93.8 % for silver and 89.3 % for gold. A scenario involving the installation of a plant processing 1 t/h of waste was evaluated using SuperPro Designer software. For a total investment of $53.22 million and an operating cost of $18.23 million per year, the main revenue is $65.77 million per year, with a return on investment of 91.6 % and a payback period of 1.1 years.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.575

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations10
Published2024
Admission routes2
Has abstractyes

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