A comparative study on the mineralization of waste butyl acetate by electrochemical oxidation via persulphate/hypochlorite and silver ( <scp>II</scp> ) reagents using <scp>DSA</scp> ® anodes: Statistical optimization by response surface methodology
Bibliographic record
Abstract
Abstract Mediated electrochemical oxidation of butyl acetate (BA) in wastewater was conducted by employing two different techniques based on S 2 O 8 −2 /ClO − and Ag (II) reagents using commercial Dimensionally Stable Anode (DSA)®‐O 2 or DSA®‐Cl 2 anodes. Response surface methodology (RSM) was also engaged to investigate process parameters impacts and their interactions on BA mineralization efficiency and energy consumption. Fourier‐transform infrared spectroscopy (FT‐IR), UV–visible, and gas chromatography (GC) analyses confirmed BA mineralization and the optimum removal conditions were then attained for both systems. Chemical oxygen demand (COD) analysis showed that electro‐oxidation by mixed persulphate/hypochlorite oxidants favours the mineralization of BA (99.7%) compared to the Ag (II) technique (96.56%). The higher removal efficiency obtained by S 2 O 8 −2 /ClO − was achieved in a neutral aquatic medium using DSA®‐Cl 2 at a lower current density (CD), temperature, and electrolysis time (pH: 6, CD: 0.1 kA/m 2 , and time: 60 min) compared to those obtained by Ag (II) ions (pH: ≤3, CD: 1.69 kA/m 2 , and time: 130 min). The former process occurred under the charge transfer control mechanism at low CDs, whereas at an elevated CD (about 1.0 kA/m 2 ), mass transfer was predominant due to the parasitic oxygen evolution. On the other hand, the latter process was found charge transfer‐controlled at low BA concentrations (≤200 ppm), whereas it turned out mass transfer‐controlled at higher BA concentrations. Comparing the anode type, energy consumption for BA mineralization by S 2 O 8 −2 /ClO − ions using DSA®‐Cl 2 at the optimum conditions was 0.009 kW · h/dm 3 , which was about three times lower than that of the DSA®‐O 2 anode.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".