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Record W2024645709 · doi:10.1021/es4006256

Water Loss Control Using Pressure Management: Life-cycle Energy and Air Emission Effects

2013· article· en· W2024645709 on OpenAlexaboutno aff
Jennifer Stokes-Draut, Arpad Horvath, Reinhard Sturm

Bibliographic record

VenueEnvironmental Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersArizona State UniversityCalifornia Energy CommissionNational Science Foundation
KeywordsGreenhouse gasEnvironmental scienceEnergy consumptionEnvironmental engineeringEnergy managementLife-cycle assessmentWater flowPressure controlWaste managementEngineeringEnergy (signal processing)Production (economics)Economics

Abstract

fetched live from OpenAlex

Pressure management is one cost-effective and efficient strategy for controlling water distribution losses. This paper evaluates the life-cycle energy use and emissions for pressure management zones in Philadelphia, Pennsylvania, and Halifax, Nova Scotia. It compares water savings using fixed-outlet and flow-modulated pressure control to performance without pressure control, considering the embedded electricity and chemical consumption in the lost water, manufacture of pipe and fittings to repair breaks caused by excess pressure, and pressure management. The resulting energy and emissions savings are significant. The Philadelphia and Halifax utilities both avoid approximately 130 million liters in water losses annually using flow-modulated pressure management. The conserved energy was 780 GJ and 1900 GJ while avoided greenhouse gas emissions were 50 Mg and 170 Mg a year by Philadelphia and Halifax, respectively. The life-cycle financial and environmental performance of pressure management systems compares favorably to the traditional demand management strategy of installing low-flow toilets. The energy savings may also translate to cost-effective greenhouse gas emission reductions depending on the energy mix used, an important advantage in areas where water and energy are constrained and/or expensive and greenhouse gas emissions are regulated as in California, for example.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.002
GPT teacher head0.165
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2013
Admission routes1
Has abstractyes

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