Water and Energy Efficiency: Transmission Operations Optimizer (TOO) City of Toronto Water Supply
Bibliographic record
Abstract
This paper describes the implementation of an automatic control system based on operational optimization, for city of Toronto and York Region Water Transmission System. The new automation system has been in operation since November 2015. The water supply system, serving a population of 3.4 million, is the largest in Canada and one of the largest in North America. The system consists of treated water pumping at four filtration plants, 18 pumping stations, 15 reservoirs/tanks, 126 pumps (up to 1,865 kW (2,500 hp)), and approximately 500 km (310 miles) of large transmission mains. While the city of Toronto and the region of York provide the water delivery/service requirements in a cost effective and uninterrupted manner, the complexity of the water system and the volatility and complex structure of the energy rates, present opportunities for benefitting from automation and further optimizing operations. As part of the new automation process, the transmission operations optimizer (TOO) minimizes energy used and cost of energy, while ensuring fundamental service delivery standards including pressure, flow, and storage are met. Pre-set minimum (critical) storage levels are not violated. ‘Optimal’ automatic control strategies are achieved for different seasonal, weekday/weekend demand patterns, as well as when abnormal events occur such as a pumping station or filtration plant being taken out-of-service. TOO involves water consumption/demand prediction, energy rate prediction, hydraulic modeling, mathematical optimization, analytical algorithms, data integration and on-line monitoring of system performance, and energy spot-market rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".