Sieving and Acid Washing as a Pretreatment to Fast Pyrolysis of a High Ash Hog Fuel
Bibliographic record
Abstract
Mineral matter can negatively influence liquid yield and product properties from the fast pyrolysis of woody biomass residuals. Biomass pretreatment approaches to reduce the ash content represent a potential pathway to expand the feedstock flexibility of fast pyrolysis. In this work, ash reduction in biomass pyrolysis oil via sieving the fines portion of a hog fuel as well as washing the fractionated hog fuel with nitric acid was investigated prior to the conversion in CanmetENERGY-Ottawa’s 5–10 kg/h fast pyrolysis system. It was found that sieving the material was much less influential on the liquid yield and product properties relative to nitric acid washing. Product analysis showed up to 40% increase in organic liquid yield and up to 30% decrease in biochar yield on a dry, ash-free basis by nitric acid washing the fractionated hog fuel. Pyrolysis reaction water was minimized when nitric acid washing the feedstock, which has important implications for the phase separation of the pyrolysis liquids. Through nitric acid washing, the ash content in the liquid was reduced up to 87% relative to the liquid produced from the pyrolysis of the untreated material. The chemical quantification of the produced pyrolysis liquids demonstrated that the chemical composition was significantly altered after nitric acid washing the hog fuel, indicating that the removal of the ash species impacted the pyrolysis reaction chemistry.
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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".