Investigation of combustion reactivity and NO emission characteristics of chars obtained from the devolatilization of raw and partially dried lignite
Bibliographic record
Abstract
Abstract In an attempt to achieve the clean and efficient utilization of lignite, drying pre‐treatment was performed in this study before lignite combustion. The combustion reactivity and NO emission characteristics of the partially dried lignite samples in the char combustion stage were investigated by means of TG and isothermal combustion tests, and the reactivity could be summarized as the following order: L1>L0.5>raw>L3>LT>L5 (chars obtained from the devolatilization of the raw and partially dried coals at 378 K for 0.5, 1, 3, 5, and 120 minutes) and the NO conversion ratio of L1 was the lowest. When the moisture content in the coal particles was relatively high (19.68%‐35.84%), the drying treatment could increase the combustion reactivity and inhibit NO emission in the char combustion stage; When the moisture content was within a relatively low range (0.17%‐19.68%), the moisture removal had negative effects on the reactivity and NO emission in the char combustion stage. The surface behaviour and microstructure of the raw coal char and chars derived from the partially dried coals were clarified by temperature programmed desorption/reduction (TPD/TPR) and Raman spectroscopy. The results illustrated that L1 derived from L c 1 (19.68%) was the most reactive sample with the largest amount of C(O) on the particle surface. There were also more reactive aromatic structures in L1 than other samples. Compared with direct combustion or excessive drying treatment, lignite should be dried to a certain degree (19.68%) for optimized lignite combustion.
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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".