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Record W4406108206 · doi:10.1111/itor.13607

An optimization model for the energy management of the network of tanks in a drinking water distribution system

2025· article· en· W4406108206 on OpenAlexafffund
Franklin Djeumou Fomeni, M. Montaz Ali, David Herrero‐Fernández, Alba Cabrera‐Codony, Hèctor Monclús

Bibliographic record

VenueInternational Transactions in Operational Research · 2025
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsGroup for Research in Decision AnalysisUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia y TecnologíaMinisterio de Ciencia e Innovación
KeywordsInflowOutflowPopulationEnvironmental scienceWater supplyWater qualityWater resourcesWater storageComputer scienceWater resource managementEnvironmental engineeringEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract “L'eau c'est la vie” is a well‐known French expression for “water is life,” which reflects the fact that water is undoubtedly the most vital resource in the world. The main mission for water utility companies is to convey and distribute water that is of acceptable quality to satisfy the demand of the population at any time of the day. In the recent years, achieving this mission has become very challenging for these companies. Indeed, on the one hand, the rapid growth of population and the expansion of urbanization have significantly increased the demand for water, while on the other hand, natural phenomenon, such as drought, as well as the impact of climate changes are making it almost impossible for water distribution companies to convey the right amount of water where and when it is needed. The presence of water storage tanks in a water distribution network is aimed at alleviating this pressure by storing water and distributing it later in response of the variability of the demand across the network. The management of the tanks of the network is done based on the assignment of three‐level set points, which allows to meet the outflow demand of water from each tank, while maintaining the adequate flow rate through the network. The set points define the level at which the valves that enable inflow and outflow of water to the tank have to be switched on or off. However, operating the valves during different periods of the day to meet the water demand may yield extremely high operational cost because opening some of the valves will induce the running of pumps to maintain an adequate flow rate of water in the network. We present a network optimization model for managing the network of storage tanks in a drinking water distribution system while minimizing the total cost of electricity involved. Computational experiments have been conducted on up to three sets of distribution networks. The results show that our proposed optimization model can be used to reduce the operational cost of managing the network of storage tanks for a water distribution system by up to 38%, while still being able to maintain the right amount of water in the tanks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.303
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2025
Admission routes2
Has abstractyes

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