Synthesis of large‐scale heat exchanger networks using a T‐Q diagram method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".