Effects of chemical and biological membrane filtration pre-treatment processes on NOM characteristics
Bibliographic record
Abstract
Membrane filtration is commonly applied to reduce dissolved organic carbon (DOC) concentration to control trihalomethanes (THMs) formation; however, the high levels of DOC in the Canadian Prairies water sources can cause severe membrane fouling. Integrated biological and reverse osmosis membrane (IBROM) process is an RO membrane treatment unit utilizing primarily biological filtration pre-treatment. IBROM process claims to remove biodegradable DOC (BDOC), which allegedly should result in reduced fouling of the RO membranes. In this study, several pre-treatment methods, such as coagulation (with alum, polyaluminum chloride (PACl), aluminum chlorohydrate (ACH) and ferric chloride) and oxidation (with KMnO4 and H2O2/UV) were evaluated for removal of DOC, BDOC, and THMs formation potential (THMFP). Moreover, BDOC change was measured in the biofiltration process using filters in the IBROM system. High-organic raw water source supplying the community of Herbert (Saskatchewan, Canada) was used in the experiments (DOC =17.5-22.7 mg/L and BDOC= 5.7-7.5 mg/L). The IBROM filters reduced DOC by 11% and increased the BDOC by 7%. Although the coagulation with PACl achieved the highest DOC and BDOC reduction (up to 57% and 58%, respectively), the coagulated water had the highest THMFP. H2O2/UV oxidation reduced the DOC only slightly by 10%, but the corresponding increase of BDOC and reduction of THMFP was very high (43% and 72%, respectively). Similar observations were made regarding oxidation with KMnO4. Overall, the waters with a higher concentration of BDOC had lower THMFP.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".