Recent Development of Biomass Fast Pyrolysis Technology and Bio-Oil Upgrading: An Overview
Bibliographic record
Abstract
The increasing demand of energy has led to the development of renewable energy in order to mitigate the dependency of fossil fuels. Fast pyrolysis of biomass is one of the most anticipated renewable energy technologies since it has a huge potential to become the efficient, environmentally sustainable, and cost effective technology for energy. Fast pyrolysis process produces liquid bio-oil as a main product, along with solid char and combustible gas. Bio-oil can be utilized for heat and power generation as well as it can be used as a feedstock for pure chemicals production. Over the last decades, numerous researches have been conducted in order to develop the process in terms of reactor design and process optimization in order to achieve the high yield of liquid with high organics and less water content. The aim of this review is to provide the state of the art on fast pyrolysis of biomass with some suggestions presented on upgrading the bio-oil. Based on the recent reactor configurations, current status of biomass fast pyrolysis in commercial scale around the world, the fuel and chemical characteristic of bio-oil compared to the conventional fossil fuels, and the potential application of bio-oil in the future, some recommendations are proposed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".