Biological ion exchange as an alternative to biological activated carbon for natural organic matter removal: Impact of temperature and empty bed contact time (EBCT)
Bibliographic record
Abstract
Biofiltration is a widely used process in drinking water treatment plants to remove natural organic matter (NOM). A novel biofiltration process using ion exchange resins as supporting media (i.e., biological ion exchange or BIEX) has been demonstrated to provide a superior performance compared to conventional biological activated carbon (BAC). In order to optimize the performance of BIEX filters, the impact of temperature and empty bed contact time (EBCT) on NOM removal was systematically studied. In the present study, bench-scale BIEX filters were set up in parallel with BAC filters and operated at different temperatures (i.e., 4 °C, 10 °C and 20 °C) and EBCTs (i.e., 7.5 min, 15 min and 30 min). Higher average dissolved organic carbon (DOC) removal was achieved in BIEX filters (73 ± 6%) than BAC filters (22 ± 9%) at the steady state with an EBCT of 30 min. Higher temperatures improved NOM removal in both BAC and BIEX filters, with the impact being greater at lower EBCTs (i.e., 7.5 min and 15 min). Higher EBCTs could also improve NOM removal, with the impact being greater at lower temperatures (i.e., 4 °C and 10 °C). DOC removal for BIEX and BAC filters can be modeled with a first-order kinetic model (R2 = 0.93–0.99). BAC had a higher temperature activity coefficient than BIEX (1.0675 vs. 1.0429), indicating that temperature has a greater impact on BAC filtration than BIEX filtration. Overall, temperature and EBCT must be considered simultaneously for biofilters to efficiently remove NOM.
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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 | 0.000 |
| 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 | 0.001 |
| 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".