Electrochemical Oxidation of Ammonia-Laden Wastewater in the Mining Industry
Bibliographic record
Abstract
Ammonia is a common contaminant in municipalities where human waste causes nitrification of local water bodies. In mining, ammonia contamination occurs as a byproduct of biological water treatment, from the use of ammonium nitrate fuel oil in explosives, and from the exposure of ammonia rich soils during the excavation process. In particular, gold mine effluent represents a significant source of ammonia and nitrogen-based contaminants. Current biological and abiotic treatment processes are difficult to employ at the scale required at mine sites due to the high operating costs, or are limited in effectiveness due to a lack of natural resources required to facilitate the treatment. This article evaluates the use of electrooxidation as a cost effective alternative to treating ammonia-laden wastewater in mining applications. Two mixed metal oxide electrodes are assessed in this article: IrO$_{2}$/Ti and RuO$_{2}$/Ti anodes. A Monte Carlo simulation is performed to determine a probabilistic range of capital and operating expenditure for a mining operation deploying an electrooxidation wastewater treatment system. The lowest capital cost of operating the electrochemical treatment occurs at a current density of 200 A/m$^{2}$, where the number of cells required for treatment is minimized.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".