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Record W2084033100 · doi:10.1002/apj.5500140317

Heat Exchanger Network Dynamic Analysis

2006· article· en· W2084033100 on OpenAlexaff
Brent R. Young, Denis L. Westphalen, William Y. Svrcek

Bibliographic record

VenueDevelopments in Chemical Engineering and Mineral Processing · 2006
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of CalgaryJacobs (Canada)
Fundersnot available
KeywordsHeat exchangerFlexibility (engineering)Process integrationProcess (computing)HeuristicCapital costDynamic simulationProcess controlProcess engineeringComputer scienceEngineeringMechanical engineeringSimulation

Abstract

fetched live from OpenAlex

Abstract The continued high cost of energy has mandated that the Chemical Process Industries reduce operational and capital costs through process heat integration. However, the heat integration of process streams can lead to process structures that are difficult to operate and control. This paper addresses the control of heat exchanger networks and it shows the importance of dynamic simulations in the synthesis of workable control structures. Steady‐state simulations were used to delineate the trade‐off between flexibility and capital costs of networks. Dynamic simulations were used to assess the placement of the by‐pass on the process‐to‐process heat exchangers. Steady‐state and dynamic simulations showed that the use of stream splitting should be avoided as a control scheme. The analysis of several simple case studies allowed the proposal of heuristic rules to identify the best control strategy for a heat exchanger network.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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
Published2006
Admission routes1
Has abstractyes

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