Thermal performance of heat and water recovery systems: Role of condensing heat exchanger material
Bibliographic record
Abstract
A significant amount of sensible and latent heat can be recovered at low temperatures from the flue gas of process heating equipment. However, the condensation of acids and water vapor makes the flue gas a highly-corrosive environment, which is a challenge for condensing heat exchangers. Corrosion-resistant materials are usually expensive and/or have relatively low thermal conductivities. Hence, it is essential to characterize the role of thermal conductivity of heat exchanger material in the performance of condensing heat exchangers. In the present study, a new analytical model is proposed and validated against available experimental data to predict the thermal performance of a tube-bank heat and water recovery unit. Our study indicates a threshold for tube thermal conductivity (~0.75 W m−1K−1), which is a point where further increase does not significantly improve the condensation efficiency. This relatively low value of thermal conductivity, compared to commonly-used materials, e.g., ~10–15 W m−1K−1 for stainless steel, unlocks the potential of using materials such as natural graphite, plastics, polymers, and ceramics (with thermally conductive additives) for applications in condensing heat exchangers and heat/water recovery in industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".