A dynamic optimization control approach of life cycle energy saving for heat exchanger network with bypasses
Bibliographic record
Abstract
In order to achieve sustainable energy saving of heat exchanger network in the life cycle,bypasses were set in the network to increase its control degree of freedom,while a certain margin was designed to provide operating space of optimal control.To better use bypass adjustment and margin of operating space,an optimal control approach method based on dynamic model of heat exchanger network was presented and combined with existing conventional control loop,not only expanding the feasible region of optimal control,but also meeting the accuracy requirements of the original conventional control loop.The cumulative cost minimization of heat exchanger network within a certain period was considered as the objective function,while considering the impact of disturbances on heat exchanger network.To meet the process conditions,the best bypass opening in the life cycle was solved in order to achieve sustained energy saving of heat exchanger network.Closed-loop correction,iterative and rolling implementation were adopted,and optimization was based on actual conditions.A global sub-optimal solution could only be arrived at every turn.However,the actual control result could achieve the optimum.Finally,a refinery's crude oil heat exchanger network was treated as the specific study object,illustrating the effectiveness and application prospect of the presented method.
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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 | 0.000 |
| 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".