Simultaneous synthesis of structural‐constrained heat exchanger networks with and without stream splits
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Bibliographic record
Abstract
Abstract This paper presents a comprehensive simultaneous synthesis approach based on stage‐wise superstructure to design cost‐optimal heat exchanger network (HEN). It is well known that the simultaneous synthesis model has very complicated mixed integer nonlinear programming formulations, which are non‐convex, non‐continuous and have many local optima. Up till now, it cannot be expected that an algorithm can find, in polynomial time, the global solution to the simultaneous synthesis problem of HEN. In order to reduce computational complexity, some simplified assumptions for structures, such as no stream splits, stream splits with isothermal mixing, no stream split flowing through more than one exchanger, etc, are adopted to prune the search space at the expense of neglecting certain important alternatives in the network configuration. In this work, a flexible stage‐wise superstructure is proposed to control the solution performance and search space efficiently. At each stage of the superstructure, with or without stream splits is determined at random or by the experience of designers. In this way, various candidate series and split network designs featuring the lowest annual cost can be found. Moreover, an efficient two‐level optimisation algorithm is employed for solving the presented model utilising genetic algorithm and particle swarm optimisation algorithm. Three case studies are presented to show the applicability of the proposed methodology. In addition, the results show that the new approach is able to find more economical networks than those generated by other methods. © 2012 Canadian Society for Chemical Engineering
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it