<scp>pH</scp> and <scp>UVA</scp> real‐time data feasibility for monitoring aqueous <scp>PVA</scp> degradation in a continuous pilot‐scale <scp>UV</scp> / <scp> H <sub>2</sub> O <sub>2</sub> </scp> photoreactor: Experimental and statistical analysis
Bibliographic record
Abstract
Abstract The performance of a continuous pilot‐scale UV/H 2 O 2 photoreactor for the degradation of aqueous polyvinyl alcohol (PVA) is studied. The process variables include PVA feed concentration, inlet H 2 O 2 concentration, and hydraulic retention time (HRT), ranging from 20 to 100 mg/L, 2 to 115 mg/L, and 5 to 25 min, respectively. Interactive effects of the input variables on the percentage of total organic carbon (TOC) removal, residual H 2 O 2 concentration, and effluent pH are analyzed using Latin hypercube sampling (LHS), quadratic modelling, and response surface methodology (RSM) in MATLAB and Design Expert. The process analysis determined optimal operating conditions of 22 mg/L PVA feed, 15 mg/L inlet H 2 O 2 concentration, and 24 min HRT, yielding a maximum TOC removal of 51.51% and a residual H 2 O 2 concentration of 5 mg/L. Despite previous studies, online pH and H 2 O 2 sensors are used in the experimental setup. A key novelty of this study is the identification and validation of reliable online indicators for parameters that cannot be measured online, enabling real‐time process monitoring and control. Specifically, UV absorbance of PVA feed solutions at 254 nm (UVA) exhibits a strong dependence on the PVA feed concentration, making it a reliable online indicator for monitoring PVA levels in the feed. Additionally, indicating a linear correlation between effluent pH and %TOC removal, with an R 2 value of 0.9886, suggests that effluent pH could serve as a reliable real‐time indicator of process removal performance. These findings highlight the potential of real‐time monitoring and optimization of UV/H 2 O 2 systems in large‐scale wastewater treatment applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".