Upgrading of Oils from Biomass and Waste: Catalytic Hydrodeoxygenation
Bibliographic record
Abstract
The continuous demand for fossil fuels has directed significant attention to developing new fuel sources to replace nonrenewable fossil fuels. Biomass and waste are suitable resources to produce proper alternative fuels instead of nonrenewable fuels. Upgrading bio-oil produced from biomass and waste pyrolysis is essential to be used as an alternative to nonrenewable fuel. The high oxygen content in the biomass and waste pyrolysis oil creates several undesirable properties in the oil, such as low energy density, instability that leads to polymerization, high viscosity, and corrosion on contact surfaces during storage and transportation. Therefore, various upgrading techniques have been developed for bio-oil upgrading, and several are introduced herein, with a focus on the hydrodeoxygenation (HDO) technique. Different oxygenated compounds were collected in this review, and the main issue caused by the high oxygen contents is discussed. Different groups of catalysts that have been applied in the literature for the HDO are presented. The HDO of various lignin-derived oxygenates and carbohydrate-derived oxygenates from the literature is summarized, and their mechanisms are presented. The catalyst’s deactivation and coke formation are discussed, and the techno-economic analysis of HDO is summarized. A promising technique for the HDO process using the microwave heating technique is proposed. A comparison between microwave heating versus conventional heating shows the benefits of applying the microwave heating technique. Finally, how the microwave can work to enhance the HDO process is presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".