Leaching of Hydrocarbon-Contaminated Mine Sludge using Concentrated Hydrochloric Acid and Ozone Gas
Bibliographic record
Abstract
The mining of PGM includes ore blasting using explosives and mechanized equipment.However, there are problems associated with mechanized equipment, which include equipment releasing hydrocarbons such as diesel, which contaminate the mine sludge.In this paper, an alternate hydrometallurgical technique for extracting PGMs from mine sludge was investigated by leaching PGM mine sludge with hydrochloric acid and ozone gas as an oxidizing agent.The particle size distribution of the mining sludge was determined using a Mictrotrac analyser, chemical composition using XRF, mineralogy using XRD, surface morphology using SEM, and hydrocarbon content using FTIR.The sample had a particle size distribution (PSD) of 80% passing 50 microns.The highest recoveries were 69.31%Pt, 23.33% Au, and 50.28%Pd.XRF analysis showed high concentrations of Si (27.46%),Fe (25.12%), and Cr (13.75%), with trace elements like Na, P, Ti, V, Mn, and Sr. XRD analysis identified minerals such as chromite, magnesioferrite, quartz, tridymite, aluminosilicates (anorthite, akermanite).FTIR analysis detected functional groups O-H, C-H, and N-H, indicating the presence of hydrocarbons like alcohols, alkanes, and amines.SEM analysis revealed PGMs embedded in a matrix of quartz, chromite, magnesioferrite, and tridymite, often situated between silicate and chromite-based minerals.These findings suggest that hydrometallurgical leaching could serve as a viable approach for PGM recovery from contaminated mine sludge, warranting further optimization and investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".