Modeling Hydroxyl Radical Distribution and Trialkyl Phosphates Oxidation in UV−H<sub>2</sub>O<sub>2</sub> Photoreactors Using Computational Fluid Dynamics
Bibliographic record
Abstract
Advanced Oxidation Processes (AOPs) promoted by ultraviolet light are innovative and potentially cost-effective solutions for treating persistent pollutants recalcitrant to conventional water and wastewater treatment. While several studies have been performed during the past decade to improve the fundamental understanding of the UV-H(2)O(2) AOP and its kinetic modeling, Computational Fluid Dynamics (CFD) has only recently emerged as a powerful tool that allows a deeper understanding of complex photochemical processes in environmental and reactor engineering applications. In this paper, a comprehensive kinetic model of UV-H(2)O(2) AOP was coupled with the Reynolds averaged Navier-Stokes (RANS) equations using CFD to predict the oxidation of tributyl phosphate (TBP) and tri(2-chloroethtyl) phosphate (TCEP) in two different photoreactors: a parallel- and a cross-flow UV device employing a UV lamp emitting primarily 253.7 nm radiation. CFD simulations, obtained for both turbulent and laminar flow regimes and compared with experimental data over a wide range of UV doses, enabled the spatial visualization of hydrogen peroxide and hydroxyl radical distributions in the photoreactor. The annular photoreactor displayed consistently better oxidation performance than the cross-flow system due to the absence of recirculation zones, as confirmed by the hydroxyl radical dose distributions. Notably, such discrepancy was found to be strongly dependent on and directly correlated with the hydroxyl radical rate constant becoming relevant for conditions approaching diffusion-controlled reaction regimes (k(C,OH) > 10(9) M(-1) s(-1)).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".