Understanding the effect of reaction parameters on the production of levulinic acid from glucose
Bibliographic record
Abstract
Abstract A significant and sustainable feedstock for many value added products is levulinic acid, which is basically a short‐chain fatty acid. The current study aims to comprehend how multiple factors affect the hydrothermal reactions that convert glucose to levulinic acid. Glucose can be readily obtained from lignocellulosic biomass and hence it is selected in the work as representative sustainable source. The effect of various operating parameters, including time (0–180 min), temperature (140–180°C), nitrogen pressure (0–25 bar), glucose concentration (3%–10%), agitation speed (100–300 RPM), and acid concentration (2%–6%); use of different salts (NaCl, AlCl3 6H2O, FeCl3); and different acids (HCl, H3PO4, H2SO4) on the reaction progress has been studied in a batch autoclave reactor. It was elucidated that pressure (only nitrogen purge was essential for reaction progress) or salt content changes did not affect sugar conversion significantly. The process was seriously influenced by the presence of acids, mostly in the form of homogeneous catalysts, and the most significant results were obtained for H2SO4. The highest levulinic acid yield (39.7 g/g) at 90 min, with nearly complete sugar conversion, was obtained under the ideal conditions of 160°C, 5% sugar loading, and 5% H2SO4 concentration. The current study indicates that the two primary operating parameters in this conversion process are temperature and time, with higher temperature and lower sugar concentration showing a rising tendency in sugar conversion. Overall, the study establishes a sustainable process for levulinic acid synthesis.
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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) | 0.001 | 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".