Feasibility and Statistical Analysis of Sulfanilic Acid Degradation in a Batch Photo-Fenton Process
Bibliographic record
Abstract
Sulfanilic acid (SA) is a representative sulfonated aromatic amine commonly found in industrial effluents, posing significant risks to both human health and the ecosystem. Efficient and cost-effective treatment of SA-containing wastewater is crucial for sustainable environmental management. This study investigates the performance of the photo-Fenton process in degrading SA-containing wastewater. Three process variables are selected to study their effects on percent total organic carbon (%TOC) removal and final pH (pHFinal): initial total organic carbon concentration (TOC0) (150–250 mg/L), Fe2+ concentration (15–85 mg/L), and H2O2 concentration (1000–1500 mg/L). A combination of response surface methodology (RSM) and Box-Behnken design (BBD) is applied to examine both the individual and interactive effects of these variables. A total of 15 experimental trials are conducted, with the center point repeated three times. The results indicate significant interaction effects between Fe2+ and H2O2 concentrations on %TOC removal, while the interaction between TOC0 and H2O2 concentration notably influences pHFinal. The optimal operating parameters to maximize %TOC removal within 45 min of operation are determined as a TOC0 of 54.2 mg/L, an Fe2+ catalyst concentration of 33.7 mg/L, and an H2O2 concentration of 1403 mg/L. Under these conditions, the predicted %TOC removal and pHFinal were 89.2% and 2.93, respectively, which confirmed through validation experiments. Additionally, a correlation between pHFinal, TOC0, and final TOC concentration (TOCFinal) is observed, leading to the development of a linear model capable of predicting TOCFinal based on TOC0 and pHFinal within the experimental space. The latter finding facilitates online monitoring of the process progress.
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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".