Co-Combustion of Food Solid Wastes and Pulverized Coal for Blast Furnace Injection: Characteristics, Kinetics, and Superiority
Bibliographic record
Abstract
The combustion characteristics and kinetics of food solid wastes (FSW), pulverized coal (PC), and their mixtures were studied by a non-isothermal thermogravimetric method. In the co-combustion of FSW and PC, with the increase in FSW content in the mixture, the initial decomposition temperature, burnout temperature, and ignition temperature of the mixture decreased, and the flammability index and comprehensive combustion characteristic index gradually increased. The co-combustion of FSW and PC showed an inhibitory effect in the devolatilization stage but exhibited a combustion-promoting effect in the fixed carbon combustion stage. The interaction between FSW and PC while co-combusting them appeared to be dominated by thermal effects. On one hand, FSW combusted first and released heat that was partially absorbed by the PC, which hence suppressed the devolatilization stage of the co-combustion process. On the other hand, the PC absorbed the heat released by the combustion of the FSW, which increased the combustion rate of the PC in the fixed carbon combustion stage of the co-combustion process. The activation energy of the devolatilization stage and the fixed carbon combustion stage of the co-combustion process was calculated to be 34.16–74.52 kJ/mol and 15.04–36.15 kJ/mol, respectively. In general, the combustion performance of FSW is better than that of PC. The mixed injection of FSW and PC can improve the overall combustion efficiency and reduce CO2 emissions in the iron-making process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".