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Record W2435094879 · doi:10.1002/cjce.22556

Synthesis of large‐scale heat exchanger networks using a T‐Q diagram method

2016· article· en· W2435094879 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHeat exchangerDiagramScale (ratio)Computer scienceThermodynamicsPhysicsDatabase

Abstract

fetched live from OpenAlex

For design of a heat exchanger network (HEN) by pinch technology, it is not uncommon to determine stream splitting and stream matching by empirical experience, in which numerous attempts are usually required to reach the final configuration of the HEN. In this work, a novel T‐Q diagram method is proposed to integrate large‐scale HENs on the basis of partitioning and merging heat recovery intervals. It aims at reducing the difficulties of the stream matching process for large‐scale HENs. The implementation procedure is illustrated via a simple HEN design problem. Furthermore, the HEN of a vacuum distillation unit in a refinery is used to further demonstrate the advantages and versatility of the proposed method. The results indicate that the proposed method can reduce the computational effort required and obtain a cost‐effective design of large‐scale HENs. Therefore, it provides an effective graphical analysis tool for the integration of large‐scale HENs in practice.

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: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.258

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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