Characterization of Permanent Fouling on the Surfaces of UV Lamps Used for Wastewater Disinfection
Bibliographic record
Abstract
A significant problem with UV disinfection of wastewater is the accumulation of fouling materials at sleeve-water interfaces. It has been shown that automated chemical and/or mechanical cleaning can remove most fouling materials satisfactorily. However, permanent foulants, which cannot be removed completely by typical cleaning operations, always remain on the quartz sleeves. These permanent foulants reduce UV transmittance, in turn reducing disinfecting effectiveness. This research investigated the origins, formation, and composition of permanent fouling. Four instrumental analyses were used: atomic force microscope (AFM), X-ray fluorescence (XRF), scanning electron microscope combined with energy dispersive X-ray microanalysis (SEM/EDX), and sleeve UV transmittance (SUVT). It appeared that permanent foulants were trapped initially by microscale holes and peaks on fresh sleeve surfaces. Based on the topography and roughness measurements from the AFM, after long-term regular chemical and mechanical cleaning, the wipers of cleaning systems damage sleeve surfaces, creating scratches or holes with larger surface areas. Foulants are trapped more easily, and are attached tightly to the surfaces of these scratches or to each other. The results from XRF and SEM/EDX showed that the main fouling components were aluminum, iron, magnesium, calcium, and phosphorus. These could not be related to wastewater quality. For the specific cases studied, chemical plus mechanical cleaning is more effective in removing foulants and avoiding the accumulation of permanent foulants than mechanical cleaning alone; in most cases, SUVT was higher than 90% after chemical plus mechanical cleaning, whereas mechanical cleaning alone achieved only approximately 30% SUVT after long-term usage.
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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) | 0.000 | 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) | 0.001 | 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 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".