Bibliographic record
Abstract
Bioenergy production using woody biomass is a fast developing application since this fuel source is considered to be carbon neutral. The harnessing of bioenergy from these sources produces residue in the form of ash. As the demand for bioenergy production increases, ash and residue volumes will increase. Major concerns arising from the management of this byproduct include: storage availability, usage, product disposal and the implications of the presence of unburned carbon. This research studies various ash types, identifying specific fractions of technological, environmental and economic viability. Fractions of energy importance, inorganic distribution and catalytic properties of specific ash types are investigated. Ash from three systems were investigated, an industrial boiler, a fixed bed updraft gasifier and a wood pellet burner. Analyses of the boiler ash included particle fractionation, proximate and ultimate analysis, Brunauer-Emmett-Teller (BET) surface area, thermogravimetric analysis (TGA) and bulk density. Samples were separated into various fractions based on particle sizes. The fixed carbon in the as-received boiler ash samples was 30 and 50 % and the higher heating value (HHV) ranged from 5 - 25 MJ/kg of the different fractions. 68 % or more of the energy could be recovered in fractions ≥ 425 μm. High carbon ash was successfully gasified in a fluidized bed reactor at low temperatures and atmospheric pressure. The pH of the as received samples for the gasifier, boiler and pellet burner were 10.36, 12.49 and 13.46, respectively. Ni with a concentration of 229 mg/kg in the pellet burner ash, exceeded the maximum limit for soil amendments (in British Columbia, Canada) within the particle size fraction ≥ 850 μm but <2000. All samples were significantly enriched in both Ca (50-61 %) and K (10-26 %). Wood ash derived catalyst obtained from a gasifier and a wood pellet burner influenced gasification reactivity. The pellet burner ash was a more effective catalyst than the gasifier a
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".