Online control of UV and UV/H2O2 processes targeted for the removal of contaminants of emerging concern (CEC) by a fluorescence sensor
Bibliographic record
Abstract
This study assessed the online and real-time monitoring of contaminants of emerging concern (CEC) using a microbial/tryptophan-like fluorescence sensor in a quaternary AOP (advanced oxidation process) pilot plant installed downstream of a tertiary municipal wastewater treatment plant (WWTP). Real-time fluorescence measurements were validated with lab-scale tryptophan-like fluorescence. Changes in water quality induced by different UV or UV/H 2 O 2 doses were detected by the fluorescence sensor allowing real-time control of processes. The removal of CEC was discussed considering their photo-susceptibility and reactivity with •OH and then classified into three groups based on their reactivity and removal efficiency (RE). Linear models of CEC removal developed using real-time fluorescence removal as a surrogate parameter resulted very accurate (overall R 2 ≥ 0.90 ) for most of CEC. Furthermore, real-time fluorescence data were successfully used to predict i) pseudo-observed first-order degradation rate constants of CEC ( R 2 = 0.99 ), and ii) UV doses during both UV and UV/H 2 O 2 processes ( R 2 > 0.90 ). The findings of this study demonstrated that fluorescence sensors can be employed in operational relevant environment to monitor a broad range of CEC and control UV doses during UV-AOPs. Therefore, the implementation of fluorescence sensors is expected for optimizing costs, energy consumption and efficiency of quaternary wastewater treatments. • Fluorescence sensor can detect fast changes in water quality during AOPs • CEC and fluorescence sensor removal during UV/H2O2 process are strongly correlated • Real-time monitoring of fluorescence allows UV dose control in UV-based AOPs • CEC degradation rate constants can be predicted by real-time fluorescence data
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 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".