Unlocking the Potential of Lignocellulosic Biomass: Microwave and Hydrothermal Pretreatment to Improve the Production of High Value-Added Biorefinery Compounds
Bibliographic record
Abstract
This study is focused on the performance of a hydrothermal reactor (HTR) and microwave-assisted (MW) pretreatments of sugar beet pulp (SBP), orange peel (OP), brewer spent grain (BSG), and rice husk (RH) to evaluate the extraction of high-value biorefinery compounds. The influence of temperature, duration of treatment, and energy consumption on hydrolysis efficiency was evaluated by quantifying total reducing sugars (TRS), proteins (PR), polyphenols (TP), and volatile fatty acids (VFA). MW pretreatment at 180 °C for 30 min yielded 18% TRS and 24% PR from OP, respectively. In contrast, HTR at 200 °C, for 60 min, achieved higher yields of 32% TRS and 22% PR for OP. BSG showed higher responsiveness under HTR, reaching 25% TRS and 20% PR at 220 °C after 120 min. The highest VFA production was 16 g H-Ac/L (BSG, HTR) and 3.2 g H-Ac/L (SBP, MW) after 120 and 5 min at 220 °C, respectively. From the point of view of energy consumption, MW pretreatment consumed significantly less energy (40.1 kJ/g) than HTR (70.85 kJ/g) under equivalent conditions (120 min at 220 °C). In addition, the MW pretreatment proved to be more energy-efficient for simpler substrates (SBP, OP), whereas HTR was optimal for complex biomasses (BSG, RH). Therefore, tailored pretreatment strategies based on substrate type are crucial to optimize energy consumption and maximize bioproduct recovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".