Operational parameters optimization for remediation of crude oil-polluted water in floating treatment wetlands using response surface methodology
Bibliographic record
Abstract
The application of floating treatment wetlands (FTWs) is an innovative nature-based solution for the remediation of polluted water. The rational improvement of water treatment via FTWs is typically based on multifactorial experiments which are labor-intensive and time-consuming. Here, we used the response surface methodology (RSM) for the optimization of FTW's operational parameters for the remediation of water polluted by crude oil. The central composite design (CCD) of RSM was used to generate the experimental layout for testing the effect of the variables hydrocarbon, nutrient, and surfactant concentrations, aeration, and retention time on the hydrocarbon removal in 50 different FTW test systems planted with the common reed, Phragmites australis. The results from these FTW were used to formulate a mathematical model in which the computational data strongly correlated with the experimental results. The operational parameters were further optimized via modeling prediction plus experimental validation in test FTW systems. In the FTW with optimized parameters, there was a 95% attenuation of the hydrocarbon concentration, which was very close to the 98% attenuation predicted by the model. The cost-effectiveness ratio showed a reduction of the treatment cost up to $0.048/liter of wastewater. The approach showed that RSM is a useful strategy for designing FTW experiments and optimizing operational parameters.
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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 | 0.003 | 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".