Degradation of surfactant used in iron mining by oxidation technique: Fenton, photo‐Fenton, and H<sub>2</sub>O<sub>2</sub>/UV—A comparative study
Bibliographic record
Abstract
Abstract Reuse of water in mining helps reduce the volume of tailings directed to dams, avoiding overloads and ruptures, as occurred in Brumadinho, Brazil. Water reuse in mining requires treatment mainly for removing the surfactant substances used. Photo‐Fenton and UV/H2O2 showed 96% to 98% degradation results of anionic surfactants within 5 minutes, suggesting this technique is faster than biological systems that can take days. This paper aims to study the degradation of a surfactant used in the flotation process by UV/H2O2, Fenton, and photo‐Fenton oxidation techniques. The compound was characterized by FTIR and MALDI‐TOF. In degradation experiments, the variation in reactants concentrations was evaluated with hydrogen peroxide, iron sulphate heptahydrate, and oxalic acid. We used a synthetic solution of surfactant in the reverse flotation of ore with 180 mg/L. The reaction was monitored with TOC analysis and a spectrophotometer throughout the reaction. The UV/H2O2 and Fenton system were studied by varying peroxide and iron concentrations, with 120 minute tests. Additionally, photo‐Fenton concentrations, the pH variation (1.5‐8.0), temperature (15°C, 21°C, and 60°C), and time were evaluated. The results showed the most efficient degradation was that using photo‐Fenton, which achieved total TOC removal using 4500 mg/L of peroxide and 364 mg/L of iron for 330 minutes, while the UV/H2O2 system achieved 29% and 49% TOC removal of the Fenton. It is verified that the oxidative processes can be applied to degrade the surfactants present in the water recovered from the flotation processes.
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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".