Catalytic routes for upgrading pyrolysis oil derived from biomass
Bibliographic record
Abstract
The modern energy industry faces the challenge of reducing its carbon footprint, mainly due to fossil fuel use, while satisfying the continuously increasing demand for fuels, electricity, and chemicals. Biomass is one of the six ways to produce renewable energy. It can provide all energy types and become our primary source of chemicals and materials. While energy can also be derived from the other five renewable sources (hydro, solar, wind, ocean, and geothermal), biomass is the only renewable energy source that is further a renewable carbon source. It can potentially complement the production of all C-based raw materials, which are the building blocks of our chemical and biochemical industry. Among the various biomass-conversion technology platforms, ‘pyrolysis’ is one of the most promising to produce bioenergy and biomaterials, particularly bio-oil. The usefulness of bio-oil in transportation is restricted by its high oxygen concentration. This review summarises the recent progress in catalytically upgrading pyrolysis bio-oils to biofuels and chemicals. The first part of this chapter is on the pyrolysis process itself; it focuses on fast pyrolysis and the resulting bio-oil due to the consensus about this technology’s superiority. The second part of this chapter provides an overview of the bio-oil upgrading routes. A comprehensive collection of the results on the type of catalysts used in such processes and their relevant functions are provided. Finally, this chapter closes with a discussion of the challenges and limitations of the bio-oil upgrading processes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".