Reduction of Sulfur Dioxide Emissions by Burning Coal Blends
Bibliographic record
Abstract
Given that sulfur contents of coals vary widely, this work investigated whether cofiring of high-sulfur coals with low-sulfur coals of different ranks has any distinct advantages on lowering the sulfur dioxide emissions of the former coals, beyond those predicted based on their blending proportions. Such cofiring intends to take advantage of documented evidence in previous investigations at the author's laboratory, which demonstrated that lignite coals of low-sulfur, high-calcium, and high-sodium content undergo massive bulk fragmentation during their devolatilization. This particular behavior generates a large number of small-sized char particles which, upon effective dispersion in the gas, can heterogeneously absorb the emitted sulfur dioxide gases, i.e., act as defacto sorbents, and then retain them in the ash. This study included two high- and medium-sulfur bituminous coals, two low-sulfur lignite coals, and a sub-bituminous coal. Results showed that bituminous coals burning under substoichiometric (fuel-lean) conditions release most of their sulfur content in the form of SO2 gases, whereas low-ranked coals only partly release their sulfur as SO2. Furthermore, the SO2 emission from coal blends is nonlinear with blend proportions, hence, beneficial synergisms that result in substantial overall reductions of SO2 can be attained. Finally, NOx emissions from coal blends did not show consistent beneficial synergisms under the implemented fuel-lean combustion conditions.
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) | 0.001 | 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".