A Review of Thermochemical Conversion of Waste Biomass to Biofuels
Bibliographic record
Abstract
Biofuels are sustainable alternatives to fossil fuels because of their renewable and low-cost raw materials, environmentally friendly conversion technologies and low emissions upon combustion. In addition, biofuels can also be upgraded to enhance their fuel properties for wide applicability in power infrastructures. Biofuels can be produced from a wide variety of biomasses through thermochemical and biological conversion processes. This article provides insights into the fundamental and applied concepts of thermochemical conversion methods such as torrefaction, pyrolysis, liquefaction, gasification and transesterification. It is important to understand the physicochemical attributes of biomass resources to ascertain their potential for biofuel production. Hence, the composition and properties of different biomass resources such as lignocellulosic feedstocks, oilseed crops, municipal solid waste, food waste and animal manure have been discussed. The properties of different biofuels such as biochar, bio-oil, bio-crude oil, syngas and biodiesel have been described. The article concludes with an analysis of the strength, weaknesses, opportunities and threats of the thermochemical conversion technologies to understand their scale-up applications and commercialization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".