Biomass Valorization for Fuel and Chemicals Production -- A Review
Bibliographic record
Abstract
The transformation of biomass into fuel and chemicals is becoming increasingly popular as a way to mitigate global warming and diversify energy sources. Biomass is a renewable, carbon-neutral resource, and fuels derived from biomass usually burn more cleanly than fossil fuels. It has been estimated that biomass could provide about 25% of global energy requirements. In addition, biomass can also be a source of valuable chemicals, pharmaceuticals and food additives. Several kinds of biomass can be converted to fuel and chemicals. Examples are wood and wood waste, agricultural crops, agricultural waste, litter from animal feedlots, waste from food processing operations and sludge from water treatment plants.Various processes can be used to convert biomass to energy. The biomass can be burned, transformed into a fuel gas through partial combustion, into a biogas through fermentation, into bioalcohol through biochemical processes, into biodiesel, into a bio-oil or into a syngas from which chemicals and fuels can be synthesized. Wood combustion, bioethanol production from either sugarcane or corn, and biodiesel production from oilseeds are currently the most economically significant processes but still need significant improvements. A detailed review of the many processes that can convert biomass into fuels and chemicals shows that no individual process is without drawbacks. As a result, it is recommended that a biorefinery is the best solution to combine and integrate various processes to maximize economic and environmental benefits, while minimizing waste and pollution.
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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 | 0.004 |
| 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) | 0.006 | 0.003 |
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".