Rapid Optimization of Typha Grass Organosolv Pretreatments Using Parallel Microwave Reactors for Ethanol Production
Bibliographic record
Abstract
A comparative study of organosolv process was performed at laboratory scale using traditional stainless steel batch reactor with parallel microwave (MW) reactors. Ethanol (with sulfuric or soda catalyst), formic acid, and performic acid organosolv processes were first optimized for the pretreatment of Typha capensis using MW reactors. The best conditions based on mass balance and Klason lignin content were reassessed using a traditional pressure steel reactor. The enzymatic hydrolysability of soda process revealed better results (reducing sugar yields = 77–87%) as compared to the sulfuric acid process (reducing sugar yields = 57–66%). Substantially higher delignification and better enzyme hydrolysability were observed for the formic acid process with hydrogen peroxide catalyst. This process produced a pulp with very low residual lignin (<3%) and a high cellulose-to-glucose conversions (>85%). It can be concluded from this study that parallel microwave technology could be used for rapid optimization of biomass pretreatment to narrow down the range of process parameters studied before a final optimization using a classical pressure reactor.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".