Enhancement of low‐temperature lignite pyrolysis by recycled carbocoal for high‐quality tar in fixed‐bed reactor
Bibliographic record
Abstract
Abstract Lignite could be converted to high value‐added char and tar through a pyrolysis process. To improve the tar yield as well as the quality of tar/char during a low temperature (550 °C) lignite pyrolysis (LT‐LP) process, the influence of mixing inexpensive and accessible recycled carbocoal with three kinds of lignite was investigated in a fixed‐bed reactor. The LT‐LP processes with and without recycled carbocoal were investigated using a TGA and were compared in detail. The results indicated that the addition of the recycled carbocoal into the three lignite optimized the light tar yield from 4.63, 3.33, and 2.09 g/g to 10.50, 6.10, and 5.01 g/g, and improved the gasification reactivity of the chars up to 2.88, 2.58, and 2.88 h−1, respectively. To identify the role of recycled carbocoal during the LT‐LP process, the obtained products, such as tar and char, were characterized by chromatography, thermal gravimetric analyzer, chromatograph‐mass spectrometer, elemental analyzer, Fourier transform infrared spectroscopy, and scanning electron microscope. It was demonstrated that the existence of the recycled carbocoal could impede the self‐polymerization of small molecule hydrogen‐containing (SM‐HC) free radicals and cut down the reactions between SM‐HC and hydroxyl radicals at the weak‐bonds breaking zone (300–450 °C), thereby supplying sufficient free radical stabilizers for the tar intermediates at the molecular skeleton region (450–550 °C). The high‐valued tar and char were obtained. In summary, the employment of recycled carbocoal during the LT‐LP process could not only promote the economic benefit of LT‐LP products but also reduce the cost of lignite value‐added utilization.
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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 | 0.001 |
| 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 | 0.001 |
| 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".