Efficient Catalytic Conversion of Waste Peanut Shells into Liquid Biofuel: An Artificial Intelligence Approach
Why this work is in the frame
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Bibliographic record
Abstract
Artificial intelligence approach can be used to solve complicated process problems. A hybrid methodology comprising artificial neural network (ANN) and genetic algorithms (GA) was utilized to model and optimize the methanolysis process of waste peanut shells. Acid catalytic methanolysis of waste peanut shells into liquid biofuel-methyl levulinate (ML) was investigated. The combination of sulfuric acid with extremely low concentration and Al2(SO4)3 was identified as the efficient mixed acid catalytic system. The ML yield under optimal conditions optimized by response surface methodology (RSM) was 16.49%, while the ML yield optimized by ANN–GA was 17.61%. The results showed that ANN–GA had a higher optimizing ability than the RSM model. Meanwhile, the methanolysis kinetics provided insights into the reaction routes for ML production. Moreover, Al2(SO4)3 can be recycled and reused five times without much decrease of the ML yield. This study suggested that waste peanut shells can be used as potential raw materials for ML production and ANN–GA can serve as a powerful tool for biofuel processing technology.
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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 | 0.001 |
| 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 it