Efficient Catalytic Conversion of Waste Peanut Shells into Liquid Biofuel: An Artificial Intelligence Approach
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 Al 2 (SO 4 ) 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, Al 2 (SO 4 ) 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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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".