Heat recovery from vacuum brazing furnaces
Bibliographic record
Abstract
By partly replacing the use of primary energy sources with waste heat recovery, climate and environmental goals for the future will be closer at hand. This thesis investigates the waste heat potential of Alfa Laval's vacuum brazing furnaces in Ronneby and alternative ways of integrating the furnace's waste heat into the building's HVAC system. The main challenge was the low-temperature qualities associated with the cooling water, which constituted an obstacle to recovering waste heat without any additional equipment, such as a heat pump. Tests and analyses performed in this thesis are, therefore, mainly aimed at raising the temperature quality of the cooling water. A test was conducted on the cooling system to calculate the energy losses with regards to the cooling water. In one 11-hour cycle, 1546 kWh of electricity was used to heat the furnace. Out of that, 1360 kWh was cooled off to the atmosphere. Additionally, a test on the furnace's clean-up cycle was performed. The maximum cooling water temperature reached during this test was 44°C. This shows excellent potential in the possibility of recovering the waste heat without any additional equipment. Further, this thesis aims to broaden the knowledge around areas concerning increased cooling water temperatures, which, during the writing, seemed to have a gap in documented sources. The results of this thesis indicate that a temperature quality increase of the furnaces' cooling water is possible. Cooling system changes have also been suggested, which is necessary for an efficient and safe heat recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".