Optimal design of bypass location on heat exchanger networks based on structural controllability
Bibliographic record
Abstract
During the operation of heat exchanger networks(HEN)with varying operating conditions,bypass optimal control is considered an effective method to adjust outlet fluid temperature and save energy.However,locations of bypasses are derived without the verification by the control theory.And,it is always very difficult to choose the location of bypasses with the trade-off between controllability and capital investments.Therefore,it is necessary to develop a method to design a HEN control structure based on the control theory.Based on structural controllability,in order to maintain all states controllable and minimize the number of bypasses,a bypass optimal design method with all states controllable(method 1)was proposed.On the other hand,when the number of bypasses was limited,to maximize controllable states an alternative bypass optimal design method with sub-controllable states(method 2)was also presented.The results indicated that with these two methods,the optimal bypass design could be derived for any requirement of controllability and capital investments.A case study of HEN before desalting in an atmospheric/vacuum distillation plant demonstrated the effectiveness of these two methods proposed in this paper.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".