Photocatalytic Treatment of An Actual Confectionery Wastewater Using Ag/TiO2/Fe2O3: Optimization of Photocatalytic Reactions Using Surface Response Methodology
Bibliographic record
Abstract
Titanium dioxide (TiO2) photocatalysis is one of the most commonly studied advanced oxidation processes (AOPs) for the mineralization of deleterious and recalcitrant compounds present in wastewater as it is stable, inexpensive, and effective. Out of all, doping with metal and non-metals, and the heterojunction with another semiconductor were proven to be efficient methods in enhancing the degradation of organic pollutants under ultraviolet (UV) and visible light. However, complex degradation processes in the treatment of an actual wastewater are difficult to model and optimize. In the present study, the application of a modified photocatalyst, Ag/TiO2/Fe2O3, for the degradation of an actual confectionery wastewater was investigated. Factorial studies and statistical design of experiments using the Box-Behnken method along with response surface methodology (RSM) were employed to identify the individual and cross-factor effects of independent parameters, including light wavelength (nm), photocatalyst concentration (g/L), initial pH, and initial total organic carbon (TOC) concentration (g/L). The maximum TOC removal at optimum conditions of light wavelength (254 nm), pH (4.68), photocatalyst dosage (480 mg/L), and initial TOC concentration (11,126.5 mg/L) was determined through the numerical optimization method (9.78%) and validated with experimental data (9.42%). Finally, the first-order rate constant with respect to TOC was found to be 0.0005 min−1 with a residual value of 0.998.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".