Optimal Precipitation Of Zn+₂ and Ni+₂ From Aqueous Solution: Influence Of Rapid Mixing Parameters
Bibliographic record
Abstract
Wastewater containing Zn+₂and Ni+₂is normally treated by chemical precipitation, coagulation, flocculation followed by clarification.The metal precipitation is influenced by chemical (wastewater pH, coagulant type and dose) and physical (rapid mixing speed and time) parameters. The process usually consists of the rapid dispersal of a coagulant into the wastewater followed by an intense agitation commonly defined as rapid mixing. This study focused on the most important parameters of rapid mixing design: mixing intensity and duration. Simulated aqueous solutions containing 50 ppm Zn+₂and 50 ppm Ni+₂were treated with aluminum sulphate, ferrous sulphate and ferric chloride coagulants at different doses and different rapid mixing times and speeds. Experimental results obtained indicate that ferric chloride at 30 mg/l dose was superior over aluminum sulphate and ferrous sulphate at the same dose in Zn+₂and Ni+₂removals. Rapid mixing time had a strong influence on the metal removal. An optimal combination of rapid mixing parameters was determined as: 60 s at 100 rpm for Zn+₂and 30 s at 80 rpm for Ni+₂removals. Scanning electron microscopy images for Zn+₂and Ni+₂flocs at optimum parameters of rapid mixing show that ferric chloride addition compacts the surface texture of the metals flocs. Flocs formed by Zn+₂are denser and larger than flocs formed by Ni+₂.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".