Using Life-Cycle Assessment to Evaluate the Environmental Impacts of Water Network Design and Optimization
Bibliographic record
Abstract
The paper presents a multi-objective optimization approach that accounts for supply-chain environmental outputs linked to the production and manufacturing of water network components and to the generation of electricity for pumping water. The optimization approach combines the non-dominated sorting genetic algorithm (NSGA-II) with economic input-output life-cycle analysis (EIO-LCA) to minimize capital cost, energy use, and environmental impact objectives. A previously developed environmental impact (EI) index is used to evaluate environmental impacts in the multi-objective optimization framework. The EIO-LCA-based NSGA-II is applied to the expansion of the `Anytown' water distribution network (Walski et al. 1987). The results indicated that capital cost and the EI index were inversely related and that annual pumping energy use and the EI index were linearly related (or nearly so). Additional research is needed to explore how the EI index can be used as an objective function in multi-objective optimization to make water network retrofit and expansion decisions.
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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 | 0.000 |
| 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".