Off-Gassing of VOCs and Permanent Gases during Storage of Torrefied and Steam Exploded Wood
Bibliographic record
Abstract
Thermal treatment for upgrading of low-value feedstocks to improve fuel properties has gained large industrial interest in recent years. From a storage and transport perspective, hazardous off-gassing could be expected to decrease through the degradation of reactive biomass components. However, thermal treatment could also shift chemical compositions of volatile organic components, VOCs. While technologies are approaching commercialization, off-gassing behavior of the products, especially in terms of VOCs, is still unknown. In the present study, we measured off-gassing of VOCs together with CO, CO 2, CH 4, and O 2 depletion from torrefied and steam exploded softwood during closed storage. The storage temperature, head space gas (air and N 2 ), and storage time were varied. VOCs were monitored with a newly developed protocol based on active sampling with Tenax TA absorbent analyzed by thermal desorption-GC/MS. High VOC levels were found for both untreated and steam exploded softwood, but with a complete shift in composition from terpenes dominating the storage gas for untreated wood samples to an abundance of furfural in the headspace of steam exploded wood. Torrefied material emitted low levels of VOCs. By using multivariate statistics, it was shown that for both treatment methods and within the ranges tested, VOC off-gassing was affected first by the storage temperature and second by increasing treatment severity. Both steam exploded and torrefied biomass formed lower levels of CO than the reference biomass, but steam explosion caused a more severe O 2 depletion.
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 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 | 0.000 | 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) | 0.001 | 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".