A Comparison of Combustion Properties in Biomass–Coal Blends Using Characteristic and Kinetic Analyses
Bibliographic record
Abstract
This paper presents comparative research on the combustion of coal, wheat, corn straw (CS), beet residues after extracting sugar (BR), and their blends, coal–corn straw blends (CCSBs), coal–wheat blends (CWBs), and coal–beet residue blends (CBRBs), using thermogravimetric (TG) analysis under 10, 20, 30, 40 and 50 °C/min. The test results indicate that CS and wheat show better combustion properties than BR, which are recommended to be used in biomass combustion. Under the heating rate of 20 °C/min, the coal has the longest thermal reaction time when compared with 10 and 30 °C/min. Adding coal to the biomass can improve the burnout level of biomass materials (BM), reduce the burning speed, and make the reaction more thorough. The authors employed the Flynn–Wall–Ozawa (FWO) method and the Kissinger–Akahira–Sunose (KAS) method to calculate kinetics parameters. It was proven that overall, the FWO method is better than the KAS method for coal, BM, and coal–biomass blends (CBBs), as it provides higher correlations in this study. It is shown that adding coal to wheat and BR decreases the activation energy and makes conversion more stable under particular α. The authors selected a wider range of biomass raw materials, made more kinds of CBB, and conducted more studies on different heating rates. This research can provide useful insights into how to choose agricultural residuals and how to use them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".