Optimization of Enhanced Ultrafiltration Conditions for Cd with Mixed Biosurfactants Using the Box-Behnken Response Surface Methodology
Bibliographic record
Abstract
A mixture of the environmentally friendly biosurfactants rhamnolipids and sophorolipids was used as a source of micelles in this study. The Box-Behnken design and response surface methodology was used to investigate the influence of factors on micellar-enhanced ultrafiltration (MEUF). Simulated Cd-containing wastewater was used for testing. Based on single-factor experiments, the initial Cd2+ concentration, biosurfactant mixing ratio (α) and pH were chosen as influential variables, and both the Cd2+ rejection coefficient and permeation flux were used as responses. A predictive model based on a quadratic polynomial regression equation was established to determine the optimized enhanced ultrafiltration conditions for Cd. The results show that the regression equation is extremely significant and fits the data accurately. The optimal enhanced ultrafiltration conditions are as follows: initial Cd2+ concentration of 10.0 mg/L, α of 0.30 and pH of 9.58. Under these conditions, the rejection coefficient and the permeation flux of Cd2+ are 99.14% and 37.36 L/m2·h, respectively. The experimental results confirm that the experimental values agree well with the values predicted by the model. Further, these results provide theoretical support for using MEUF to treat heavy metal-containing wastewater when biosurfactants are used for micelle formation.
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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) | 0.000 | 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".