Life-Cycle Energy Analysis of a Water Distribution System
Bibliographic record
Abstract
The paper presents a life-cycle energy analysis (LCEA) to quantify energy expenditures in the fabrication, use, and end-of-life stages of the pipes of a water distribution system. The methodology incorporates the capabilities of environmental input-output life-cycle analysis to quantify the energy required to fabricate pipes. The EPANET2 hydraulic model is applied in conjunction with a pipe-aging model to calculate the theoretical energy recovery in the use stage. An exponential pipe-break model is applied to quantify the energy required to repair pipe breaks during the use stage of a system. Simple formulations are developed to estimate the energy required to dispose of and recycle pipes once their service period has expired. The LCEA methodology is then applied to the New York City (NYC) water supply tunnels example to quantify energy expenditures in four planning scenarios with 10-, 20-, 50-, and 100-year pipe replacement frequencies. The results of the NYC example highlight the tension between the energy costs incurred in the fabrication and end-of-life stages of a system and those incurred in the use stage. A pipe-replacement period roughly equal to 50 years yielded the lowest overall energy expenditure in the three life stages. A sensitivity analysis was carried out to assess the influence of uncertain system parameters on energy expenditure estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".