The Influence of Inorganic Components in the Shengli Lignite on the Bond Breaking and Functional Group Evolution Patterns During Pyrolysis
Bibliographic record
Abstract
Abstract Inorganic components, as an important part of coal, have a significant impact on the pyrolysis behaviour of coal. In this paper, the acid washing method is used to remove inorganic components from coal, and the ion exchange method is utilized to load various metals detected by inductively coupled plasma atomic emission spectrometer (ICP) back into the coal in their original content. The effects of inorganic component removal on the physicochemical structure of SL lignite were systematically investigated, with particular focus on bond cleavage mechanisms, evolutionary patterns of functional groups, and pyrolysis characteristics of the coal samples. The results show that the removal of inorganic components can disrupt the cross‐linked structure in SL lignite, promoting the release of aliphatic, aromatic, and oxygen‐containing functional groups at low temperatures. Nevertheless, it is detrimental to the cleavage of C ar O and C al C ar at high temperatures, hinders the further depolymerization of the coal structure, and leads to a decrease in the content of light components in the pyrolysis tar. The addition of K, Fe, and Ca can effectively promote the breaking of various CC bonds and CO bonds during the pyrolysis process, increase the release rate of various functional groups during the pyrolysis process, and increase the content of light components in the pyrolysis tar. Among them, K, due to its strong cracking ability, promotes the extensive breaking of CC bonds. Fe, on the other hand, promotes the generation of H 2 and stabilizes the oxygen‐containing components, resulting in the highest tar yield.
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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".