A review on coal pyrolysis and gasification: understanding the chemistries and influence of operating conditions
Bibliographic record
Abstract
Abstract Pyrolysis and gasification are two popular technologies that have been applied for coal utilization for centuries. In recent times, their applications for sustainable coal utilizations have attracted significant interest in hydrocarbon-based chemical synthesis, synthetic natural gas production, and carbon material synthesis. However, despite their long history in the coal industry, the underlying chemistries governing the pyrolysis and gasification processes are not well understood. This has contributed to why these technologies have yet to witness widespread commercialization in the coal industry. Therefore, the chemistries of coal pyrolysis and gasification were discussed in this review. It was found that the extent of primary pyrolysis reactions depends on hydrogen because hydrogen stabilizes the free radicals, and its absence slows down or stops the reactions. Operating conditions such as temperature and reactive atmospheres directly affect the pyrolytic reactions and the characteristics of the products, while pressure and particle size affect the mass transfer of volatile species and influence the extent of secondary pyrolysis reactions. In gasification, the composition of the syngas is principally determined by the water-gas-shift and methane-reforming reactions, while operating conditions such as temperature, catalysts, and choice of gasifying agents are used to control the characteristics of the syngas, dictated by the intended application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".