Struvite recovery efficiency using flocculation in batch and continuous settling systems for ammonia removal of mining wastewater
Bibliographic record
Abstract
Abstract An approach to remove ammonia from mining wastewater is to precipitate ammonia into struvite, and flocculation was proved to enhance settling of struvite flocs. But the current literature fails to consider flocculent properties of struvite flocs, and previous studies focused only on small volumes. This study evaluates the effect of ammonia concentration and height on removal efficiency of struvite flocs in a batch system and compares removal efficiency of struvite flocs between a batch and a pilot‐scale continuous settling process to evaluate the potential of using flocculation to recover struvite crystals as a stand‐alone method. Removal efficiency of struvite using flocculation is evaluated depending on depth in a batch system for two different ammonia concentrations (45 and 90 ppm) and in a continuous system for different flowrates. It is shown that a higher concentration promotes flocculation and enhances settling velocities of struvite flocs. The difference between the batch and the continuous processes for the same removal efficiency was significantly higher from what has been reported in the literature: in the continuous process, 89% of struvite flocs have been recovered with a surface overflow rate (SOR) of 1.8 m.h−1, whereas, for the same height, the same efficiency corresponds to SOR = 9 m.h−1 in the batch process. The fragile nature of struvite flocs is potentially responsible for such a difference. Practitioner Points Settling velocities of struvite flocs are highly dependant on concentration and depth. Removal efficiency are considerably higher with a batch settling process for the same surface overflow rate. Flocculation enable 89% of struvite fines to be recovered in a continuous settling process with a SOR of 1.8 m.hs−1.
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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 | 0.001 |
| 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 | 0.001 |
| 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".