Utilization of a High-Alkali Lignite Coal Ash for SO2 Capture in Power Generation
Bibliographic record
Abstract
This work explored the use of ashes of a low-sulfur, high-alkali lignite coal for partially capturing the sulfur dioxide emissions from combustion of a high-sulfur bituminous coal. The bituminous coal was mixed with the lignite ashes and then burned in a laboratory drop-tube furnace (DTF) externally heated to 1,400 K. The gas-phase emissions in the combustion effluents of the neat bituminous coal were monitored and compared with those of the bituminous coal mixed either with the lignite ashes or with other additive compounds, such as a specially prepared sorbent from the ash of the lignite coal or with calcium oxide (CaO). All experiments were executed at a molar Ca:S=0.3 in air, under fuel-lean conditions. Coal particles were in the size range of 75–90 μm. Results showed that the addition of lignite ashes caused substantial reductions, by up to 21% in the SO2 emissions of the bituminous coal. Such reduction was akin to that caused by burning the coal mixed with the CaO sorbent. Significant reduction in NOx emission was also attained. This observation, in conjunction with ash analysis, showed that the alkali-rich ashes of the lignite coal acted as sulfur sorbents for the abundant SO2 emissions of the bituminous coal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".