Biomass Gasification and Syngas Combustion for Greenhouse CO2 Enrichment
Bibliographic record
Abstract
Greenhouse carbon dioxide (CO2) enrichment from biomass residues was investigated using exhaust gas from the combustion of syngas produced by gasification. Near complete syngas combustion is essential to achieve CO2 levels which increase plant yields while maintaining a safe environment for workers. Wood pellets were supplied to a downdraft gasifier to produce syngas fed to a steel swirl burner. The preliminary results were encouraging and represented a first step toward a successful development of this technology. The burner required an equivalence ratio (the actual air to fuel ratio relative to the stoichiometric air to fuel requirements) of 2.6 for near complete combustion. Concentrations of sulphur dioxide (SO2) and ethylene (C2H4) emissions were either below critical concentrations or negligible. In 60% of the trials, carbon monoxide (CO) emissions were below ASHRAE standards for indoor air quality. However, the average nitrogen oxide (NOx) emission was 23.6 ppm, and it would need to be reduced below the 0.05 ppm to meet ASHRAE standards. Proposed improvements to the syngas burner design to lower NOx emissions and increase efficiency are: integration of a low swirl design, mesh catalysers, a higher quality refractory material, and a more efficient heat exchanger. Theoretically, combustion or gasification of biomass could provide more CO2 for greenhouse enrichment than propane or natural gas per unit of energy.
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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 | 0.000 |
| 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".