Performance Modeling and Simulation for Wastewater Collection Networks
Bibliographic record
Abstract
This study develops a framework for asset management strategy of wastewater collection networks comprised of three interconnected decision-making layers: (1) Visions & Values, (2) Function, and (3) Performance, which are set according to the established concepts of strategic targets, policy levers, sustainability and life cycle. The asset management strategy framework is implemented and validated through demonstration of functionality and value by using the wastewater collection networks of three utilities in Ontario, Canada, to drive management simulations. A borrowing management strategy is used to benchmark the utilities against each other in terms of infrastructure, sociopolitical, and financial performance over a 100-year benchmarking period. It is found that a borrowing management strategy can enable the utility to accelerate their capital works, reduce the volume of inflow and infiltration and their associated expenses and sustainably meet their strategic targets over the life cycle of the assets. Using contour plots, the impact of maximum debt capacity on two infrastructure and financial benchmarking performance indicators is also investigated to explore the “optimal” combination of allowable fee hikes and preferred rehabilitation rates. Furthermore, using a borrowing management strategy, a business case for asset management of wastewater collection networks is developed to explore the “optimal” combination of allowable fee-hike and rehabilitation rates, using a developed inflow and infiltration expenditures (I&IEx) saving ratio contour plots. The results indicate that a borrowing management strategy competes as long as the combinations of allowable fee-hike and preferred rehabilitation rates lead to a positive value of I&IEx saving ratio.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".