Optimizing Nitrate Dosing Strategies for Sulfide Control Using Kinetic Modeling, Variance‐Based Sensitivity Analysis, and Laboratory‐Scale Sewer Reactors
Bibliographic record
Abstract
Sulfide and methane production in sewer systems poses significant operational and environmental challenges, including odor, corrosion, and greenhouse gas emissions. This study investigates the optimization of nitrate dosing strategies to mitigate sulfide generation using a laboratory-scale sewer reactor system combined with mathematical modeling. An extended kinetic model was developed, based on the Wastewater Aerobic/Anaerobic Transformations in Sewers (WATS) model, to simulate sulfide and methane dynamics, incorporating key microbial processes and nitrate-based oxidation pathways. The model was calibrated and validated using experimental data with and without nitrate dosing. A variance-based global sensitivity analysis was performed to identify influential parameters affecting model predictions. Results show that dosing location and rate substantially influence sulfide removal efficiency and residual nitrate levels. Among the tested strategies, nitrate dosing in the third reactor (out of four) at 14.5 mgNO₃-N/L offered optimal trade-offs, achieving sulfide concentrations below 0.5 mgS/L while maintaining effluent nitrate levels at 0.9 mgNO₃-N/L, representing a 42% reduction in dosing costs compared to upstream dosing. These findings provide a quantitative foundation for improving nitrate dosing strategies in sewer networks.
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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 | 0.001 | 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 | 0.000 |
| 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 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".