In situ epoxidation of waste soybean cooking oil for synthesis of biolubricant basestock: A process parameter optimization and comparison with RSM, ANN, and GA
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this work, the use of artificial neural networks (ANNs) as an alternative tool for modelling and predicting the optimum conversion of the unsaturated fatty acid to epoxide in comparison with the response surface methodology (RSM) was developed. In the present investigation, waste soybean cooking oil (WCO) as biolubricant basestock was prepared via structural modification of unsaturated fatty acids (in situ epoxidation). Optimization of the effect of process parameters on maximum oxirane oxygen content (OOC) was studied using RSM. Interaction among the process parameters, such as C=C bonds to H 2 O 2 molar ratio, catalyst loading, and reaction time was examined by ANOVA. The main focus of this study was to establish optimum OOC conditions using sulphuric acid (H 2 SO 4 ) as a homogeneous acid catalyst. Optimum OOC of epoxidized waste soybean cooking oil (EWCO) was found to be 4.69 mass% under the experimental conditions of 60 °C temperature, 6 h reaction time, 1.5 g of catalyst loading, and 1:2 molar ratio of C=C bonds to H 2 O 2 . The resultant epoxide product was confirmed with the help of Fourier transform infrared spectroscopy (FTIR) (at 844.82 cm −1 ) and nuclear magnetic resonance spectroscopy (NMR) (at δ 2.8 to δ 3.1 ppm) analysis. Significant physicochemical properties of the prepared lubricant basestock were evaluated at optimum conditions using standard methods. Further, ANN modelling and genetic algorithm (GA) optimization were carried out by using an identical dataset. The results of the study revealed that the chemically modified WCO derivatives also can act as a potential biolubricant basestock.
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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 | 0.000 |
| 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 it