Optimizing delignification and saccharification process for sawdust processing using a central composite design
Bibliographic record
Abstract
Abstract Lignocellulosic mass consists of cellulose, hemicelluloses, and lignin. Although biomass promises to be efficiently used for biofuel production and many other value‐added products, lignin present in lignocellulosic biomass affects the hydrolysis of cellulose and hemicelluloses, making it necessary to develop techniques that provide better lignin removal efficiency and high cellulose hydrolysability. The current work aims to maximize lignin removal in sawdust and develop an understanding of the hydrolysis of pretreated biomass for sugar production. Different parameters such as solvent to solid ratio, temperature, and reaction time have been considered based on the design of experiment to understand the effect on the delignification and saccharification processes. After treating sawdust for 1.5 h, it was observed that a maximum of 85% lignin was removed at a temperature of 131°C and solid loading of 16 g. Subsequent hydrolysis of delignified sawdust at 131°C temperature, solvent to solid ratio 15, and 0.5 h resulted in a maximum reducing sugar production of 26.82 mg/mL. The study elucidated the optimum conditions for the effective processing of sawdust in terms of delignification and saccharification, leading to maximum benefits in lignin removal and sugar production.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".