Design and Testing of Composite Heat Exchanger Applied to Industrial Self-Recuperative Gas Burners
Bibliographic record
Abstract
In the thermal treatment of steel materials and the heating and smelting processes of lightweight alloys, the dissipation of waste heat often leads to energy loss.In line with the global trends in combustion heating system development, preheating combustion systems are currently the most suitable for application in industrial furnaces operating in the range of 823K to 1223K, offering 15% to 20% energy savings and carbon reduction.However, existing heat exchangers for preheating burners are limited to individual fin or bundle designs.These designs cannot simultaneously meet the dual requirements of high heat exchange efficiency and low-pressure loss during heat exchange operations.This study proposed an innovative composite heat exchange technology for preheating combustion to address this issue.The approach involved a composite heat exchanger incorporating features of bundles and fins along with a fluid flow path-switching control module.The flexible use of the switching module modified the fluid path within the composite heat exchanger depending on the thermal treatment temperature and power requirements.This achieved maximum heat exchange efficiency and minimum pressure loss during the heating and socking processes of the heat treatment.Furthermore, the system ensured stable combustion.In the simulated analysis of the composite heat exchanger, the fin-type heat exchange efficiency reached 65%, whereas the bundle-type heat exchange efficiency reached 75%.The experimental data showed a fin-type heat exchange efficiency of 69% and a bundle-type heat exchange efficiency of 78%.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".