Optimization of levulinic acid conversion into methyl levulinate via methyl esterification catalyzed with aluminum sulfate
Bibliographic record
Abstract
The use of low-cost, widely available environmental friendly catalysts may strengthen the sustainability of lignocellulosic biomass conversion into levulinic esters. In this respect, the present study aimed to optimize the methyl esterification reaction between levulinic acid (LVA) and aluminum sulfate [Al 2 (SO 4 ) 3 ] with a view to producing methyl levulinate (MLV). The Box–Behnken design for three factors was applied to investigate the influence of reaction time (60, 120, and 180 min), alcohol:acid molar ratio (4:1, 8:1, and 12:1), and catalyst concentration (0.02, 0.04, and 0.06 mol L −1 ) on LVA conversion into MLV. The reactions occurred in a Parr Instruments stainless steel reactor, at a temperature of 140 ºC and 733 rpm rotation. The results indicated that the interaction between molar ratio and catalyst concentration has a significant influence on LVA conversion. The regression model obtained is significant and predictive, with an R 2 value of 0.86. Analyses of response surfaces showed that a 60 min reaction time produces high conversion, irrespective of the amount of catalyst. Fixing the shortest reaction time and lowest Al 2 (SO 4 ) 3 concentration, the molar ratio indicated for the reaction would be 1:6. The levels selected provide conversions varying from 86.83% to 99.27%, demonstrating the efficiency of a low-cost catalyst at low concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".