A New Optimization Framework That Includes Water Conservation Strategies to Reduce Demand in Water Distribution Networks
Bibliographic record
Abstract
This paper presents a new multi-objective framework that includes a demand-side management objective to reduce water demand in water distribution network design. Demand-side management refers to any practice that reduces the amount of potable water being drawn from the network at a given time be reducing end-user demand (through attitude changes, low-flow fixtures/appliances, rain-water harvesting, greywater reuse, etc.), leakage rates, or shifting use to off-peak periods. The optimization seeks to simultaneously minimize upgrade and operational costs and network demand. The demand model is based on end-user consumption and includes the sum of daily water used for household fixtures and appliances. The decision variables in the model, taken from the perspective of the utility, are the diameters of the new water mains, the price of water, and the decision to offer rebates for various low-flow fixtures or appliances. Two scenarios are demonstrated on a five-node network in a case study that highlights the impact of installing new, low-flow household fixtures on both water demand and upgrade and operational cost. Results show a trade-off between decreasing demand and upgrade and operational cost.
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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 | 0.000 | 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) | 0.000 | 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".