In situ epoxidation of waste soybean cooking oil for synthesis of biolubricant basestock: A process parameter optimization and comparison with RSM, ANN, and GA
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 H2O2 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 (H2SO4) 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 H2O2. 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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".