Temperature measuring point analysis and control system design of heat exchanger networks based on structural observability
Bibliographic record
Abstract
For the large scale heat exchanger networks(HEN),there are a mass of coupled temperatures.If these states cannot be controlled and observed,it will go against saving energy and safety in production.To implement control system,placing the temperature measuring points in the HEN properly is the basis.Generally,too many thermocouples are set by experience to ensure all states of the HEN are observable.But it is not cost-efficient and lack of verification by the control theory.Therefore,it is necessary to develop a method to design temperature measuring points of HEN based on the control theory.An optimal design method of temperature measuring points with all states observable was proposed by using structural observability and analysis of the impact of bypasses.The design could maintain all of states observable and minimize the number of temperature measuring points with the trade-off between observability and capital investment.With measuring points determined,the regulatory control system was proposed based on the relative gain array analysis.Consequently,a case study with HEN in a crude distillation unit demonstrated the effectiveness of the method proposed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".