Bench-scale evaluation of ferrous iron oxidation kinetics in drinking water: Effect of corrosion control and dissolved organic matter
Bibliographic record
Abstract
Corrosion control strategies are important for many utilities in maintaining water quality from the water treatment plant to the customers' tap. In drinking water with low alkalinity, water quality can become significantly degraded in iron-based pipes if water utilities are not diligent in maintaining proper corrosion control. This article reports on experiments conducted in bicarbonate buffered (5 mg-C/L) synthetic water to determine the effects of corrosion control (pH and phosphate) and dissolved organic matter (DOM) on the rate constants of the Fe(II) oxidation process. A factorial design approach elucidated that pH (P = 0.007, contribution: 42.5%) and phosphate (P = 0.025, contribution: 22.7%) were the statistically significant factors in the Fe(II) oxidation process at a 95% confidence level. The comprehensive study revealed a significant dependency relationship between the Fe(II) oxidation rate constants (k) and phosphate-to- Fe(II) mole ratio. At pH 6.5, the optimum mole ratio was found to be 0.3 to reduce the k values. Conversely, the k values were observed to increase for the phosphate-to- Fe(II) mole ratio > 1. The factorial design approach revealed that chlorine and DOM for the designated dosages did not cause a statistically significant (α = 0.05, P > 0.05)change in rate constants. However, an increment of the chlorine to ferrous iron mole ratio by a factor of ∼ 2.5 resulted in an increase k values by a factor of ∼ 10. This study conclusively demonstrated that the lowest Fe(II) oxidation rate constant was obtained under low pH conditions (pH ≤ 6.5), with chlorine doses less than 2.2 mg/L and with a phosphate-to-Fe(II) mole ratio ≈ 0.3 in the iron water systems.
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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 | 0.002 | 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) | 0.001 | 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 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".