Biologically active ion exchange (BIEX) for NOM removal and membrane fouling prevention
Bibliographic record
Abstract
The natural organic matter (NOM) removal efficiency and regeneration behavior of ion-exchange filters with promoted biological activity (BIEX) was compared to operation where biological activity was suppressed (i.e. abiotic conditions). The impact of BIEX pre-treatment on fouling in subsequent ultrafiltration was also investigated. Biological operation enhanced NOM removal by approximately 50% due to an additional degradation of smaller humic substances, building blocks and low molecular weight acids. Promotion of biological activity significantly increased the time to breakthrough of the filters and, therefore, is expected to lower the regeneration frequency as well as the amount of regenerate of which to dispose. Pre-treatment using BIEX filters resulted in a significant decrease in total and irreversible fouling during subsequent ultrafiltration. The decrease was attributed to the effective removal of medium and low molecular weight NOM fractions. The results indicate that BIEX filtration is a robust, affordable and easy-to-operate pre-treatment approach to minimize fouling in ultrafiltration systems and enhance the quality of the produced permeate.
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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 | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".