Critical review of the role of ash content and composition in biomass pyrolysis
Bibliographic record
Abstract
In the face of environmental challenges (e.g., dramatically increasing greenhouse gas emissions and climate change), it is utmost of importance to sustainable energy systems. Biomass consisting of agricultural and forest waste, municipal solid waste, and aquatics, has been identified as alternative and promising fuel sources. Thermochemical conversion approaches like pyrolysis can turn various types of biomass into three valuable product streams, namely, bio-oil, biochar, and syngas. To date, past review articles have considered the major operating parameters of kinetics, chemistry, and the application of pyrolysis products. However, ash content is one of the key biomass components that lacks investigation on its influence during biomass pyrolysis with respect to products yield and properties. This review article examines: i) the ash content and composition in different types of biomass; ii) effects of ash content on catalytic pathway and biomass thermal degradation; iii) ash related problems in the thermal degradation of biomass; and iv) available deashing techniques for biomass. The review aims to provide new understandings and insights regarding the effects of ash content and composition on biomass pyrolysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".