Biomass processing into ethanol: pretreatment, enzymatic hydrolysis, fermentation, rheology, and mixing
Bibliographic record
Abstract
Abstract Alternate energy resources need to be developed to amend for depleting fossil fuel reserves. Lignocellulosic biomass is a globally available renewable feedstock that contains a rich sugar platform that can be converted into bioethanol through appropriate processing. The key steps of the process, pretreatment, enzymatic hydrolysis, and fermentation, have undergone considerable amount of research and development over the past decades nearing the process to commercialization. In order for the commercialization to be successful, the process needs to be operated at high dry matter content of biomass, especially in the enzymatic hydrolysis stage that influences ethanol concentration in the final fermentation broth. Biomass becomes a thick paste with challenging rheology for mixing to be effective. As the biomass consistency increases, yield stress increases which limits efficiency of mixing with conventional stirred tanks. The purpose of this review is to provide features and perspectives on processing of biomass into ethanol. Emphasis is placed on rheology and mixing of biomass in the enzymatic hydrolysis step as one of the forefront issues in the field.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".