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Record W765562879 · doi:10.26636/jtit.2012.3.1283

Optimal Pump Scheduling for Large Scale Water Transmission System by Linear Programming

2012· article· en· W765562879 on OpenAlexaboutno aff
Jacek Błaszczyk, Andrzej Karbowski, Kamil Krawczyk, Krzysztof Malinowski, A.Y. Allidina

Bibliographic record

VenueJournal of Telecommunications and Information Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceTime horizonNonlinear systemScheduling (production processes)Transmission systemLinear programmingTransmission (telecommunications)Mathematical optimizationWater supplyScale (ratio)Environmental scienceEnvironmental engineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

Large scale potable water transmission system considered in this paper is the Toronto Water System, one of the largest potable water supply networks in North America. The main objective of the ongoing Transmission Operations Optimizer project consists in developing an advanced tool for providing such pumping schedules for 153 pumps, that all quantitative requirements with respect to the system operation are met, while the energy costs are minimized. We describe here a linear, so-called SimplifiedModel (SM), based on mass-balance equations, which is solved on week horizon and delivers boundary conditions for so-called Full Model (FM), which is nonlinear and takes into account hydraulic phenomena and water quality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.240

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 studies0.0000.000
Scholarly communication0.0000.002
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 head0.005
GPT teacher head0.212
Teacher spread0.206 · 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
GenreMethods

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

Citations11
Published2012
Admission routes1
Has abstractyes

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