Evaluating Orthophosphate-Silicate Blend as an Alternative to Blended Phosphates for Corrosion Control and Sequestration
Bibliographic record
Abstract
The presence of iron and manganese in drinking water distribution systems can contribute to discoloration, taste and odor issues, scale buildup, deposition corrosion, and the adsorption and transport of lead. Sequestrants can minimize aesthetic concerns and scale buildup, but they are risky due to increased lead/copper solubility. Here, we used a bench-scale batch reactor to evaluate orthophosphate with sodium silicate or polyphosphate for simultaneous lead corrosion control in waters with and without iron/manganese. Consistent with previous work, ortho-trimetaphosphate increased the total lead (23%). Increased dissolved lead was also observed for both ortho-trimetaphosphate (50%) and ortho-silicate (30%) treatments. When iron/manganese was present, orthophosphate-silicate was associated with 5-12% less total lead relative to orthophosphate under the same conditions, and the effect of ortho-trimetaphosphate was pH dependent. The addition of silicate and trimetaphosphate also reduced water discoloration compared to orthophosphate, as measured by apparent color. Here, the orthophosphate-silicate blend did not significantly increase total lead in the absence of high iron and manganese but increases to the highly mobile, dissolved fraction should be noted. Utilities seeking to control lead for compliance purposes, while simultaneously managing iron and manganese for consumer confidence, should explore orthophosphate-silicate as a possible solution in their corrosion control assessments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".