Pre-treatment of a Hydrocarbon Contaminated Platinum group Minerals Mine Sludge using a Non-ionic Surfactant
Bibliographic record
Abstract
In South Africa, the Merensky, Upper Group 2 (UG-2), and Platreef are the three primary platinum group mineral (PGM) reefs found in the Bushveld Igneous Complex (BIC).PGM output is now mostly sourced from Platreef and UG-2 reefs.Most of the PGM mines have become highly mechanised, leading to the production of mine sludge contaminated with hydrocarbons.This study investigated the removal of the hydrocarbons using a non-ionic surfactant in preparation for the flotation process.The contaminated mine sludge was characterised for hydrocarbon content, mineralogical and elemental composition.Response Surface Methodology-Box-Behnken (RSM-BBD) design of experiments was used to design the washing experimental runs.The results revealed that the sample contained 9.32g/t of total four elements (Pt, Pd, Rh and Au) with most of the PGMs found in the sulphide form.Chromite and quartz are the common gangue minerals that were found in the sample.Fourier transform infrared spectroscopy showed that there were hydrocarbons present in the sample.The washing experiments showed that Triston-100 (TX-100) can be used to remove hydrocarbons from the contaminated PGMs mine sludge.The optimum washing conditions with a hydrocarbon removal efficiency of 91.86% were found to be 12.25 g/kg surfactant concentration, 150 minutes of washing time and a pH of 12.
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 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".