Effects of lignite dewatering treatment on the surface behaviour and NO emission characteristics during the combustion process
Bibliographic record
Abstract
Abstract Hailaer lignite (HLE) with a uniform particle size distribution (3–6 mm) was employed to investigate the parameters of the low‐temperature dewatering process influencing the emission of gaseous pollutants (NOx) during their combustion process. The combustion of HLE original/dried samples obtained from a COMBDry drying system were carried out in a fixed bed horizontal furnace under an air atmosphere at the reaction temperature of 1100 °C. The dewatering treatment led to the enhancement of the relative amount of the volatile and fixed carbon content in the upgraded coal sample. The deconvolution of the Raman spectra of each sample showed that the increased degree of drying led to an enhancement of the defects in aromatic structures, indicating an enhancement in the number of active sites or oxygen‐containing complexes on particle surface. The devolatilization and combustion experimental results showed the following: (1) with the increase in the degree of drying, more HCN and NH3 were released during the devolatilization process, and both HCN and NH3 molecules had positive effects on the consumption of NO under high temperature conditions; (2) due to the enhancement of the specific surface area and total amount of surface active sites (Cf) after the drying treatment, the combustion and reduction reactivity of the dried HLE samples increased significantly; and (3) the conversion ratio of fuel‐N to NO during the combustion process decreased significantly with the increase of the degree of dewatering. Therefore, it could be concluded that the removal of moisture content in lignite particles in advance had positive effects on lignite high efficiency combustion with low NO emission.
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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 | 0.000 |
| 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) | 0.001 | 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".