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Record W2362377119

Structural controllability analysis for heat exchanger networks with bypass control

2012· article· en· W2362377119 on OpenAlexaff
Xionglin Luo

Bibliographic record

VenueHuagong xuebao · 2012
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControllabilityRedundancy (engineering)Control theory (sociology)Heat exchangerComputer scienceCoupling (piping)Control (management)Topology (electrical circuits)Mathematical optimizationMathematicsControl engineeringEngineeringMechanical engineeringApplied mathematicsReliability engineering
DOInot available

Abstract

fetched live from OpenAlex

During the operation of heat exchanger network(HEN),since operating conditions are always varying,bypass optimal control is considered as an effective method to adjust the temperatures and saving energy.However,locations of bypasses are derived without verification by the control theory.Therefore,it is necessary to analyze the structural characteristics of heat exchanger networks.Firstly,based on the topology theory,the structural states configuration and array were presented by using the structural model of the HEN.Then,the HEN structural controllability,structural control intensity and redundancy degree were analyzed.The results indicated that the bypass on the downstream was less controllable than that on the upstream;structural control intensity was affected by the corresponding bypass location;and the use of coupling channel may affected redundancy degree and controllability significantly.The analysis of case study demonstrated that this method could be used to testify the controllability of the HEN with bypass optimal control,and significantly reduce the complication of complex structural controllability analysis.This method also indicated a novel idea for applying control theory to engineering process.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.579

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 communication0.0000.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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