Modelling heterogeneous photocatalytic oxidation using suspended <scp>TiO<sub>2</sub></scp> in a photoreactor working in continuous mode: Application to dynamic irradiation conditions simulating typical days in July and February
Bibliographic record
Abstract
Abstract Compared to more conventional techniques, advanced oxidation processes (AOP) hold significant promise in terms of elimination of organic (especially persistent) compounds and microorganisms (disinfection) in wastewater. If the objective is to power these processes using solar energy, we need to be able to manage the intermittency in the solar resource. This is an essential step for design and to ensure efficient operation of the treatment processes. As solar radiation is inherently variable due to day/night cycles, seasonal cycles, and weather meteorological conditions, solar AOP performances are difficult to establish using conventional measures. To address this gap, we carry out experimental campaigns under controlled conditions and develop modelling tools capable of describing dynamic‐mode photocatalytic degradation. Here we develop a way to capture the responses of a photoreactor subjected to various stresses, including irradiation conditions, via an LED panel. Using a model that considers the influence of UV flux density and pollutant concentration made, it was possible to represent photoreactor responses under different irradiation conditions and feeds (concentration or flow at the input). The ultimate objective is to study the photocatalytic capacity of the photoreactor under irradiation conditions simulating a real day of sunshine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".