Photodegradation of aqueous pharmaceuticals in a continuous UV/H2O2 system: Photoreactor modeling
Bibliographic record
Abstract
The removal of pharmaceuticals from wastewater is vital due to their adverse effects on aquatic ecosystems and human health, making the UV/H₂O₂ process a promising solution for addressing this challenge. This study investigates the photodegradation of pharmaceuticals from wastewater using a UV/H₂O₂ process, focusing on total organic carbon (TOC) removal under varying H₂O₂/TOC mass ratios (0.5, 4, and 8 mgH 2 O 2 /mgC) and hydraulic retention times (HRTs: 7, 30, and 60 min). The UV lamps delivered an intensity of 1.55 × 10 4 µW/cm² at the quartz sleeve surface and operated at a wavelength of 254 nm. Utilizing Design Expert software, a model was developed based on experimental data and response surface methodology (RSM), identifying the optimal operating conditions at a H₂O₂/TOC mass ratio of 4 and an HRT of 60 min, achieving a TOC removal efficiency of 60.5 %. Furthermore, a mechanistic model was employed to determine the apparent reaction rate constant of the pharmaceuticals with hydroxyl radicals , enabling predictions of TOC removal along the photoreactor. The findings demonstrated that higher HRT significantly increases TOC removal, while the H₂O₂/TOC mass ratio must be optimized to prevent scavenging effects that can inhibit pharmaceutical degradation. The estimated electrical energy per order (EEO), calculated at approximately 37.1 kWh.m -3 /order, provided a comprehensive evaluation of the process efficiency and economic viability. The experimental validation of both the RSM and mechanistic photoreactor models confirmed their accuracy and reliability in predicting TOC removal, showing their applicability in process design and scale-up.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".