Production and separation of acetic acid from pyrolysis oil of lignocellulosic biomass: a review
Bibliographic record
Abstract
Abstract Bio‐oil from lignocellulosic biomass pyrolysis is a promising feedstock as a precursor for the production of transportation fuels and value‐added chemicals. The presence of significant concentrations of oxygen, water, and acids makes it difficult to use bio‐oil directly as a transportation fuel without costly upgrading. The acidity of pyrolysis liquids is mainly derived from volatile acids, such as acetic acid, causing chemical instability and corrosion. The extraction of acids from bio‐oil can therefore offer strategies for improved applications and economic value. Moreover, acetic acid is a valuable reagent and the building block for several commercially important chemicals. This review presents the results of important research related to the production of bio‐oil‐derived acetic acid. The discussion is intended to summarize the effect of biomass type and pretreatment method, pyrolysis processing conditions, and separation techniques on acetic acid production via pyrolysis. On this basis, acetic acid characterization techniques are also presented along with an overview of acetic acid applications and economic considerations. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".