ADVANCED OXIDATION PROCESSES: ASSESSMENT OF NATURAL ORGANIC MATTER REMOVAL AND INTEGRATION WITH MEMBRANE PROCESSES
Bibliographic record
Abstract
Stringent water quality regulations and general aesthetic issues have urged drinking water industry to apply advanced water treatment technologies that can meet multiple treatment objectives. Removal of significant amount of natural organic matter (NOM), including colour causing organics, to meet stringent disinfection by product (DBP) regulations from source water with low alkalinity and low turbidity is very challenging with conventional water treatment processes. Membrane filtration processes are effective in removing significant amount of NOM thus minimizing the formation of carcinogenic DBPs. However, fouling of membrane is a major problem affecting system performance. Improved pretreatment of feed water helps reduce or eliminate membrane fouling.\n\nThis study characterized source water, examined fouling in nanofiltration (NF) membranes and explored various pretreatment options to reduce NF fouling. Resin fractionation was performed to characterize NOM and to identify the major fractions responsible for DBP formation in natural source water of the Tatamagouche water treatment plant (WTP) in Nova Scotia. The source water primarily comprised of hydrophilic neutrals (HIN) and hydrophobic acid (HOA) compounds, with the latter being a major contributor to the DBP formation. Fouling behaviour of the NF membranes was examined at bench- and full-scale levels to understand the impact of source water quality on membrane fouling in the Tatamagouche and Collins Park WTPs. Bench- and full-scale results revealed higher fouling in the Collins Park WTP which together supported ongoing membrane cleaning practices in the plant. Surface enhanced Raman spectroscopy (SERS), demonstrated here as a novel technique, suggested that carbohydrates and proteins are the main foulants in the source water. Bench-scale experiments conducted to evaluate the performance of ozone (O3), ultraviolet (UV), hydrogen peroxide plus ozone (H2O2/O3), H2O2 plus UV (H2O2/UV) and O3 plus UV (O3/UV) for reducing NOM and DBP precursors suggested that the O3/UV AOP offers the optimum reduction of NOM. Integrating AOP pretreatments with NF membrane resulted in an improved permeate flux but not permeate quality of the NF membrane.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".