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Record W2384196355

Margin optimal design of heat exchanger network with bypasses based on life cycle energy saving

2012· article· en· W2384196355 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.

Bibliographic record

VenueHuagong xuebao · 2012
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHeat exchangerRefineryMargin (machine learning)DistillationEnergy consumptionFoulingProcess engineeringEngineeringEnergy conservationEnergy (signal processing)Computer scienceWaste managementMathematicsMechanical engineeringChemistry
DOInot available

Abstract

fetched live from OpenAlex

For the life cycle of the heat exchanger network(HEN),the heat exchanger performance steps down and energy consumption steps up because of network equipment aging and other factors.And this problem is not resolved effectively by the present margin design for the HEN.A margin optimal design method of the HEN with bypasses based on life cycle energy saving was presented.Through bypass adjustment,effective margin area was released gradually,achieving the purpose of saving energy in the life cycle.The HEN life cycle operation accumulative total cost was considered as the objective function,and at the same time the effect of fouling of heat exchangers and bypass adjustment was included to solve the best margin while satisfying the operation conditions.Lastly sustaining energy conservation was realized.The HEN of a given crude distillation unit in a refinery was treated as the specific research object,illustrating the effectiveness and application prospect of the presented method.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.518

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 head0.015
GPT teacher head0.204
Teacher spread0.190 · 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