Enhancing Furnace Thermal Efficiency by Adjusting Fuel Temperature
Bibliographic record
Abstract
Increasing fuel temperature leads to lower fuel density so that speed of the fuel leaving the nozzle increases causing the fuel to reach the burning point sooner. Hence, a higher temperature can be maintained in the furnace to reduce heat loss. Increasing the fuel temperature also causes higher temperature of the hot gas surrounding the burner nozzle, faster flowing velocity for the hot gas flow in the furnace, more even thermal distribution in the furnace, as well as faster thermal flow rising velocity and higher temperature in the furnace radiation zone. Hence, the fuel consumption and carbon dioxide emission are significantly reduced. In this study, results obtained with a full-scale furnace show that when the fuel temperature is raised from 95 oC to 130 oC, the hot gas temperature in the convective zone of the furnace rises by 21 oC; the furnace pressure increases by 2.5 mm H2O so that oxygen concentration in the excess air drops by 1.2 vol %. Under these conditions, 7.5 x 105 m3 of fuel cost can be saved, and 858 ton per year of CO2 emission can be reduced.
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 | 0.000 |
| 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) | 0.002 | 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".