Optimization and predictive modelling of biodiesel production from waste cooking oil catalyzed by blast furnace slag geopolymer using <scp>RSM</scp> and machine learning
Bibliographic record
Abstract
Abstract Biodiesel production from waste cooking oil (WCO) has emerged owing to growing interest in sustainable energy sources. Geopolymers synthesized from industrial wastes, such as blast furnace slag (BFS), are promising catalysts because of their environmental benefits and catalytic properties. However, a knowledge gap exists in the application of machine learning (ML) for the transesterification of WCO catalyzed by geopolymer. This study aimed to optimize and predict biodiesel yield using a numerical approach, response surface methodology (RSM), and two ML algorithms: artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS). Four input process parameters were investigated: methanol‐to‐oil ratio (20–50 wt.%), catalyst ratio (5–15 wt.%), reaction time (4–8 h), and reaction temperature (30–70°C), with biodiesel yield as the response. Central composite design (CCD) was used to evaluate the effects of process parameters, and models were evaluated using R 2 , root mean squared error (RMSE), mean absolute error (MAE), mean average percent error (MAPE), and average relative error (ARE). The optimum yield of 98.635% was achieved at 11.103 wt.% catalyst, 44.068 wt.% methanol to oil, 6.704 h reaction time and 57.493°C reaction temperature. ANFIS displayed the best predictive performance ( R 2 : 0.996, RMSE: 1.429, MAE: 0.684, and MAPE: 1.548). Analysis of variance (ANOVA) results indicated the methanol‐to‐oil ratio had the most significant impact ( F ‐value: 91.77), followed by the catalyst ratio ( F ‐value: 51.58). The produced biodiesel met ASTM D6571 and EN 14214 standards. Future research should focus on catalyst reusability and catalyst synthesis optimization for industrial applications. This study contributes to global efforts toward sustainable biodiesel production by addressing waste disposal and green fuel development.
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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".