The AmmEl Process for the Treatment of Ammonia in Wastewater
Bibliographic record
Abstract
Mine and mill effluents are often contaminated with ammonia-N due to use of ammonia based compounds, such as ammonia-based explosives (ANFO), flotation reagents in milling, cyanide destruction, and as a pH regulator (uranium precipitation). Ammonia is known to be toxic to aquatic species and has been listed as a toxic compound by Environment Canada. Enpar Technologies Inc. has developed a novel patented ion-exchange/electrochemical technology, the AmmEl System, to treat ammonia in wastewater streams. The AmmEl System uses a two-stage approach to treat ammonia; the first stage utilizes ion-exchange to remove ammonia from the wastewater followed by electrochemical oxidation of the captured ammonia directly to N2 gas. The AmmEl system has been pilot tested at the City of Guelph wastewater treatment plant. Two separate wastewater streams were tested; Case (I) as a pre-treatment system for a high strength wastewater stream (700 mg/L NH3-N) and Case (II) to treat a low strength effluent stream (5 mg/L NH3-N). In the Case I test, the AmmEl system was able to remove greater than 93 % of the ammonia from the wastewater. In Case II, the AmmEl system was effective at removing the ammonia-N from the wastewater stream to less than the 1 mg/L target level, producing an average concentration of 0.6 mg/L in the treated effluent. The AmmEl system has been proven to be an effective method for removing ammonia from a variety of waste streams and is applicable for the treatment ammonia in mine/mill effluent. The system is a robust and cost effective alternative to existing ammonia removal technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".