Maximizing the production of total reducing sugars from sugarcane bagasse using ultrasound‐assisted acid hydrolysis based on response surface methodology approach
Bibliographic record
Abstract
Abstract The depletion of fossil fuels and the associated environmental impact necessitate the development of sustainable energy sources as well as feedstocks for value added chemicals. Lignocellulosic biomass, particularly sugarcane bagasse (SCB), is a promising feedstock for various industrial processes and products with the total reducing sugars (TRS) as one of the valuable intermediates. The current study focuses on maximizing TRS production from sugarcane bagasse using ultrasound‐assisted acid hydrolysis. The Box–Behnken design of response surface methodology was employed to determine the best conditions for maximizing TRS concentration, with the study involving five independent variables as time, ultrasonic power, duty cycle, temperature, and acid loading. The statistical analysis predicted the best operating parameters as time of 82.77 min, ultrasonic power of 124.03 W, 60.95% duty cycle, temperature of 61°C, and acid concentration of 3.44%, resulting in the highest TRS concentration of 3.49 mg/mL. Experimental data and statistical analysis validated the quadratic model's predictive capability, demonstrating its practical applicability in enhancing TRS production efficiency. Overall, the work has demonstrated an effective method of using delignified biomass for maximizing the yield of reducing sugars based on detailed study of the effect of operating parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".