Enhanced Coagulation for Removal of Natural Organic Matter and Disinfection Byproducts: Multivariate Optimization
Bibliographic record
Abstract
Enhanced coagulation can remove dissolved organic carbon (DOC) that acts as a precursor to disinfection byproducts (DBPs). However, previous studies have not elucidated the effect of certain coagulation and settling parameters, such as the fast-mixing rate and settling time, on haloacetic acids (HAAs) and trihalomethanes (THMs). Furthermore, coagulant dosage, fast mixing power, and settling time play essential roles in the cost-efficiency of operation and construction of a water treatment plant. This study aims to present a novel investigation of the effects associated with the operational factors of coagulation-flocculation and sedimentation to achieve feasible solutions for management of DBPs. The simultaneous effects of pH (4.5–8.5), coagulant type (Fe3+/Fe3+ + Al3+ ratio), coagulant dosage, fast-mixing rate, and settling time were examined using a response surface methodology design. Accordingly, predictive models were generated by conducting 2 sets of experiments, which comprised 50 runs of jar tests that were performed in triplicate of 2 blocks for 2 natural drinking water sources in Newfoundland, Canada. The results were validated on four natural waters and two synthetic water samples. The multivariate optimization on THM4 and HAA5 resulted in a significant reduction in the fast-mixing energy by 59.9%, and a reduction of 23.4–41.1% in coagulant dosage. The results of natural organic matter fractionation on water samples revealed that the optimized coagulant dosage of 3.83–5.95 mg/mg DOC could remove up to 91.00%, 72.64%, and 70.79% of THM4, HAA5, and DOC, respectively, in natural water samples with a very hydrophobic acid (VHA) fraction of 0.67–0.81 VHA/DOC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".