Enhanced Coagulation for Removal of Natural Organic Matter and Disinfection Byproducts: Multivariate Optimization
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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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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 | 0.001 |
| 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 it