Flocculation Optimization of Orthophosphate with FeCl<sub>3</sub> and Alginate Using the Box–Behnken Response Surface Methodology
Bibliographic record
Abstract
A coagulation–flocculation process was employed to remove orthophosphate (P i ) in aqueous media using a ferric chloride (FeCl 3 ) and alginate flocculant system. Jar tests were conducted, and the response surface methodology (RSM) was used to optimize the P i removal variables. The Box–Behnken design was used to evaluate the effects and interactions of four independent variables: pH, FeCl 3 dose, alginate dose, and settling time. The RSM analysis showed that the experimental data followed a quadratic polynomial model with optimum conditions at pH 4.6, [FeCl 3 ] = 12.5 mg·L –1, [alginate] = 7.0 mg·L –1, and a 37 min settling time. Optimum conditions led to a P i removal of 99.6% according to the RSM optimization, in good agreement with experimental removal (99.7 ± 0.7%), at an initial concentration of 10.0 mg P i /L. The isotherm adsorption data at the optimized conditions were analyzed by the pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models and several isotherms models (Langmuir, Freundlich, and Sips). The PFO kinetic model and Langmuir isotherm model yielded the best fit to the isotherm results. The maximum adsorption capacity of the flocculant system was 83.6 mg·g –1 . The flocculation process followed electrostatic charge neutralization and an ion-binding adsorption mechanisms.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".