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Record W2017031830 · doi:10.1177/0143624406071305

Hybrid fuzzy logic control strategies for hot water district heating systems

2007· article· en· W2017031830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBuilding Services Engineering Research and Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTemperature controlHeating systemControl systemVolumetric flow rateBoiler (water heating)Control theory (sociology)Fuzzy control systemWater flowFuzzy logicControl logicEnvironmental scienceEngineeringControl (management)Computer scienceControl engineeringEnvironmental engineeringMechanical engineeringWaste managementMechanics

Abstract

fetched live from OpenAlex

Hybrid fuzzy logic control (FLC) strategies for hot water district heating (HWDH) systems are designed. A non-linear dynamic model of a HWDH system (heated floor area 90 000 m 2 ) with 21 dynamic equations is developed. The dynamic model consists of a boiler, pipe network, heaters and buildings. Six different hybrid control strategies involving FL and PI control were designed to regulate fuel firing rate, water flow rate or water temperature in the system. Simulation results show that the control strategies, based on the use of return water temperature and indoor air temperature predictor (IATP), give good zone temperature control. Pumping costs were reduced by modulating water flow rate in the system. Results also showed that the proposed hybrid control strategies could save up to 17% energy in buildings.

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.

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 score0.645
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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