Optimization of Operating Parameters for the Selective Flotation of Heavy Metals from Contaminated Fine Sediment Using Response Surface Model
Bibliographic record
Abstract
In this study, the effect of different flotation operating variables, such as pH, pulp density, collector concentration, impeller speed, frother concentration, and air flow rate, on selective flotation of heavy metals, especially Cu, from fine dredged sediment has been evaluated. Parameter optimization was done using the single parameter at a time method and response surface method (RSM) using Box-Behnken design and was assessed in terms of metal removal, metal recovery, metal concentration factor, and mass recovery. Among the operating variables studied, pulp pH, collector concentration, pulp density, and impeller speed were found to have significant effect on metal flotation selectivity. A validation study of the response surface model showed its aptness to predict the optimum values of operating parameters and their interactions on flotation responses which evaluate flotation performance. Flotation experiments under optimum operating parameters showed good flotation selectivity for Cu (3.3 ± 0.2) with a mass recovery of (mass of sediment in the froth) 14.1 ± 1 and Cu removal of 37.4 ± 3.6%.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".