Gasification of Bio-oil and Torrefied Biomass: An Overview
Bibliographic record
Abstract
Current energy policies seek to decrease the dependence on fossil resources by supporting the production of fuels and chemicals, with a lower carbon footprint, from alternative feedstocks. Conversion of biomass to synthetic fuels and chemicals, using gasification followed by Fischer–Tropsch synthesis and refining, is of interest. Entrained flow gasification of coal and heavy oil is commercially practiced and can be used for the conversion of biomass feedstocks. Moreover, intermediates such as bio-oil and torrefied biomass can be used in entrained flow gasifiers with little modification. Bio-oils are produced from raw biomass via pyrolysis or hydrothermal liquefaction, while torrefied biomass is obtained via torrefaction. The use of these more homogeneous and energy-dense feedstocks can reduce biomass transport costs and allows decoupling of biomass availability from end-use application scale and location. This chapter discusses feedstocks, production processes and bio-oils and torrefied biomass properties, as well as their conversion to syngas via entrained flow gasification. Technical challenges and scale-up activities are presented. Concepts for decentralized bio-oil and torrefied biomass production, followed by centralized gasification, are compared to centralized raw biomass gasification. Required technological developments toward the implementation of syngas production from biomass feedstocks and for high-capacity Fischer–Tropsch processes are highlighted.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".