Continuous Organic Characterization for Biological and Membrane Filter Performance Monitoring
Bibliographic record
Abstract
Continuous organic characterization at a full‐scale drinking water treatment plant was achieved using fluorescence spectroscopy. The feasibility of this method was demonstrated through monitoring the performance of biological activated carbon contactors (BACCs), which serve as pretreatment for fouling control of ultrafiltration (UF) membranes. Fluorescence monitoring was applied successfully to identify the preferential removal of select fluorescence components and addition of another microbial humic‐like component by the biological filters. Spikes in BACC influent organic matter and fouling development on the downstream UF membranes highlighted the importance of preozonation. To demonstrate possible use of the short‐term continuous fluorescence data, neural networks were used to predict fouling development on downstream UF on the basis of BACC effluent water quality. Short‐term fluctuations in fouling development were well predicted by incorporation of continuous fluorescence characterization data. Continuous organic characterization shows promise for the application of fluorescence spectroscopy for real‐time process optimization and control in drinking water treatment systems.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".