Ammonia-Ca-K competitive ion-exchange on zeolites in mining wastewater treatment: batch regeneration and column performance
Bibliographic record
Abstract
This manuscript addresses the treatment explosives-impacted mining wastewaters (EIMWW) using ion-exchange to remove elevated levels of ammonia. Repeated batch loading-regeneration cycles were conducted for two commercially available zeolite media used in the treatment of ammonia-ladenEIMWW to establish the effects of competing ions and regeneration solution composition. The Northern Ontario EIMWW tested contained 3.87 meq/L total ammonia (TA) as well as 2.85 meq/L Kþ and 3.9 meq/L Ca2þ.The media studied were a natural clinoptilolite and a modified clinoptilolite (SIR-600). Five regenerant solutions with different NaCl and KCl concentrations were evaluated. The presence of potassium in the regenerant was found to hinder the TA exchange capacity of both zeolites. The SIR-600 and the natural clinoptilolite used in conjunction with the 10% NaCl solution featured the best TA exchange capacities, 0.46 ± 0.02 meq TA/g and 0.36 ± 0.05 meq TA/g, respectively. The batch tests showed that both media had a slight preference for Kþ over TA. The continuous flow column tests performed using SIR-600 media greatly accentuated the selectivity of Kþ over TA. In reaching the same 0.55 meq TA/L breakthrough level, the same modified zeolite column was able to treat five time more volume of a synthetic TA solution than EIMWW.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".