ANION EXCHANGE RESIN TECHNOLOGY FOR NATURAL ORGANIC MATTER REMOVAL FROM SURFACE WATER
Bibliographic record
Abstract
Natural organic matter (NOM) is present in all surface waters as a result of decaying vegetation, biological activity, and organic soil. Alternative NOM removal processes such as anion exchange resins (AERs) have shown NOM removals typically ranging between 50 to 90%, with up to 99% removal achieved in some cases. \nThe first portion of this study evaluated the performance of two AERs; a conventional Type 1 AER and magnetic ion exchange resin (i.e. MIEX®) for NOM removal from surface water quantified by UV254, dissolved organic carbon (DOC), and specific UV absorbance (SUVA). Samples were also characterized for chloride, sulphate, and chloride-to-sulphate mass ratio (CSMR) to provide additional information on water quality characteristics of AER treated waters. Overall, the results showed that both AERS were effective for removing NOM. However, the MIEX® resin provided greater removal of NOM with shorter contact times compared to the conventional resin investigated. Water treated with MIEX® resin showed significantly higher chloride and lower sulphate concentrations than the conventional AER. Higher CSMR values were found with MIEX® treated water compared to conventional AER system, although both resins showed CSMR much greater than 0.5, which can increase galvanic corrosion effects with lead.\nBench-scale jar tests were conducted to investigate the impact of temperature on the efficacy of three NOM removal treatment technologies; enhanced coagulation with alum, MIEX® and a combined MIEX® treatment followed by coagulation with a low dose of alum. Higher settled water turbidity was observed during cold water operating conditions for all three processes. At cold-water operating conditions, DOC removal was reduced with combined MIEX® -Alum treatment, and UV254 removal was impacted for both MIEX® and MIEX® -Alum processes. The combined MIEX®-Alum process was found to provide the lowest THMFP and HAAFP at both temperatures to concentrations lower than current regulatory maximum acceptable concentration (MAC) guidelines in Canada. \nSurface charge analysis experiments were performed at bench-scale using synthetic water containing humic acid to determine the relationship between NOM and the charge of AER-treated waters. Further bench and pilot-scale studies were performed to investigate the use of surface charge measurements to monitor and optimize NOM removal during treatment with AER systems. Strong correlations were observed between UV254 and respective charge measurements (i.e. ZP, SC) of AER-treated synthetic and raw waters. The results of this research has shown that it is possible to use charge to optimize the MIEX® process for NOM removal. Additionally, it was found that SC measurements could be used as an operational tool for AER processes, where deviations in SC from optimum treatment would indicate the requirement for fresh resin addition or resin regeneration.
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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) | 0.000 | 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) | 0.002 | 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".