Fluorescence‐based fouling prediction and optimization of a membrane filtration process for drinking water treatment
Bibliographic record
Abstract
Abstract A novel fluorescence‐based approach is proposed for modeling, predicting, and optimizing different fouling dynamics in an ultrafiltration (UF) process for drinking water treatment. Principal component analysis (PCA) was used to extract information in terms of principal components (PCs), related to major membrane foulant groups, from fluorescence excitation–emission matrix measurements captured during UF of natural river water. The evolution of PC scores during the filtration process was then related to membrane fouling using dynamic balances of latent variable values (PC scores). This approach was found suitable for forecasting fouling behaviors with good accuracy based solely on fluorescence data collected 15 min from the start of the filtration. The proposed approach was tested experimentally through model‐based optimization of backwashing times with the objective of minimizing the energy consumption per unit amount of water produced during the filtration process. This approach was also useful for identifying fouling groups contributing to reversible and irreversible fouling. © 2011 American Institute of Chemical Engineers AIChE J, 2012
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".