Evaluation of temperature impacts on drinking water treatment efficacy of magnetic ion exchange and enhanced coagulation
Bibliographic record
Abstract
Magnetic ion exchange (MIEX ® ) is an emerging technology for disinfection by-product (DBP) precursor removal in the drinking water industry. Although recent research has demonstrated that this technology is capable of achieving excellent natural organic matter (NOM) removal, there is paucity of published research showing the efficacy of ion exchange (IX) technology in cold water conditions. The overall objective of this research was to evaluate at bench-scale the potential impact of cold water operating conditions on enhanced coagulation with alum, MIEX ® and a combination of MIEX® and low dose alum. All three treatments were evaluated at 1 and 20 °C with settled water quality compared in terms of turbidity, UV254, dissolved organic carbon (DOC), specific UV absorbance (SUVA), trihalomethane formation potential (THMFP) and haloacetic acid formation potential (HAAFP). The results of the study showed that all three technologies evaluated were significantly impacted by cold water operating conditions in terms of turbidity removal, and UV254 removal was significantly reduced in both the MIEX ® and MIEX ® -Alum processes. However, treatment of the surface water with the combined process resulted in the highest removal of DBP precursor material and lowest THMFP and HAAFP concentrations at both temperatures compared to the individual unit operations.
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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 | 0.000 | 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".