Minimizing shell-and-tube heat exchanger cost with genetic algorithms and considering maintenance
Bibliographic record
Abstract
This paper presents a procedure for minimizing the cost of a shell-and-tube heat exchanger based on genetic algorithms (GA). The global cost includes the operating cost (pumping power) and the initial cost expressed in terms of annuities. Eleven design variables associated with shell-and-tube heat exchanger geometries are considered: tube pitch, tube layout patterns, number of tube passes, baffle spacing at the centre, baffle spacing at the inlet and outlet, baffle cut, tube-to-baffle diametrical clearance, shell-to-baffle diametrical clearance, tube bundle outer diameter, shell diameter, and tube outer diameter. Evaluations of the heat exchangers performances are based on an adapted version of the Bell–Delaware method. Pressure drops constraints are included in the procedure. Reliability and maintenance due to fouling are taken into account by restraining the coefficient of increase of surface into a given interval. Two case studies are presented. Results show that the procedure can properly and rapidly identify the optimal design for a specified heat transfer process. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".