Fouling Indices for Quantification of Natural Organic Matter Fouling and Cleaning in Ceramic and Polymeric Membrane Systems
Bibliographic record
Abstract
Polymeric membranes have emerged as an economically effective treatment option to produce drinking water.More recently, ceramic membranes are raising interest in this field due to their unique physical properties, which may prove to be important in moving towards more robust and sustainable drinking water treatment methods.However, the loss of membrane permeability as a result of natural organic matter (NOM) fouling remains one of the biggest challenges for sustainable polymeric and ceramic membranes operation.A key challenge in membrane system is to understand how operating pressure and water temperature may impact fouling and subsequent cleaning in relationship to NOM.Further there is limited data to ascertain if ultrafiltration (UF) polymeric and ceramic systems will respond in similar or different manners to NOM fouling which then further impacts how respective systems need to be cleaned.Fouling indices have been developed by means of simple, short, empirical filtration tests to assess the fouling potential of membrane feed water.The modified ultrafiltration fouling index (MFI-UF) is a standard test that is used to estimate a fouling index value that gives a general indication about the treatability of feed water or the need for pretreatment prior to a membrane unit.Unlike the MFI-UF, the unified membrane fouling index (UMFI) is used to quantify fouling a membrane is subjected to (i.e.reversible vs. irreversible), which provide different data on fouling.However, different approaches in fouling assessment may suggest that direct comparison lack context which lead to some disconnect between predicted and actual fouling in the field.Thus, the applicability of the MFI-UF to be effectively used in complement with the UMFI to predict NOM fouling under changes in v Acknowledgements I wholeheartedly want to express my gratitude to my supervisor Dr. Onita Basu.Her support and understanding, both academically and personally, have made this long journey less arduous and more rewarding.Her support from start to finish, encouragement and exceptional guidance throughout this research project has been a constant source of motivation.I wish to express my deep and sincere gratitude to Dr. Benoit Barbeau for his guidance and contribution to this research as
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".