Study on Emission Control for Precursors Causing Acid Rain (VI) : Suitability of Aquatic Plant Biomass as a Co-combustion Material with Coal
Bibliographic record
Abstract
In China, energy and environmental problems are becoming serious owing to rapid economic development. Coal is the most problematic energy source because it causes indoor and outdoor air pollution, acid rain, and global warming. One type of clean coal technology that has been developed is the coal-biomass briquette (or bio-briquette, BB) technique. BBs, which are produced from pulverized coal, biomass (typically, agricultural waste), and a sulfur fixation agent (slaked lime, <TEX>$Ca(OH)_2$</TEX>) under high pressure without any binder, have a high sulfur-fixation effect. In addition, BB combustion ash, that is, the waste material, can be used as a neutralization agent for acidic soil because of its high alkalinity, which originates from the added slaked lime. In this study, we evaluated the suitability of alternative biomass sources, namely, aquatic plants, as a BB constituent from the perspective of their use as a source of energy. We selected three types of aquatic plants for use in BB preparation and compared the fuel, handling, and environmental characteristics of the new BBs with those of conventional BBs. Our results showed that air-dried aquatic plants had a higher calorific value, which was in proportion to their carbon content, than agricultural waste biomass; the compressive strength of the new BBs, which depends on the lignin content of the biomass, was high enough to bear long-range intracontinental transport in China; and the new BBs had the same emission control capacity as the conventional BBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".