Production and valorization of acetic acid from lignocellulosic biomass pyrolysis: Influence of operational conditions and membrane separation processes
Bibliographic record
Abstract
Pyrolysis of lignocellulosic biomass is a thermochemical route for transforming forest and agricultural residues into valuable products. Among these, acetic acid is particularly important given its broad industrial applications in vinyl polymers, agrochemicals, and food additives. However, effectively recovering acetic acid from the aqueous fraction (fast pyrolysis) or wood vinegar (slow pyrolysis) of pyrolytic oils remains a challenge. This review summarizes the principal factors affecting acetic acid yield during fast pyrolysis, including feedstock composition (cellulose and hemicellulose), moisture content, temperature, particle size, reactor type, and residence time. Approaches such as mild pretreatments and optimized catalytic conditions can further enhance the release of acetyl groups from hemicellulose, thus raising acetic acid production. Recent advances in separation methods emphasize membrane technologies like nanofiltration (NF) and reverse osmosis (RO). These processes provide high selectivity, energy efficiency, and a reduced environmental footprint compared to traditional techniques such as liquid-liquid extraction and vacuum evaporation. Operational parameters—such as transmembrane pressure, pH, and feed composition—influence both membrane flux and retention of acetic acid. Interactions among solutes, membrane materials, and process conditions can either facilitate or hamper selective acetic acid recovery. This review highlights the potential to integrate optimized pyrolysis parameters with robust membrane systems to achieve sustainable acetic acid production. Ongoing research focuses on improving the acid resistance of membrane materials and elucidating mass transport mechanisms for scale-up. Successful implementation of these technologies will help establish a circular bioeconomy by converting lignocellulosic residues into high-value chemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".