Modelling and simulation of the sugarcane delignification process by alkaline pretreatment using <scp> H <sub>2</sub> O <sub>2</sub> </scp>
Bibliographic record
Abstract
Abstract The present research provides a thorough investigation to enhance the delignification process in a 2G biorefinery by implementing a dynamic process simulator. Developed using the Julia programming language, this simulator enables the study of crucial variables such as liquid–solid ratio (LSR), hydrogen peroxide concentration ( C HP ), and processing time ( t ) during the alkaline hydrolysis stage. Optimal operating conditions were determined through rigorous simulation and meticulous experimental validation, resulting in an optimal configuration of 4% H 2 O 2 concentration, LSR of 15:1 v/w, and 50 h of processing time under standard pressure and temperature conditions. These findings yielded a lignin removal percentage of 93.02%. Furthermore, the experimental validation process facilitated the recalibration of kinetic parameters. Overall, this research underscores the potential of the dynamic simulator in optimizing critical variables across various raw materials.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".