Optimisation of trimethylolpropane ester synthesis from waste cooking oil methyl ester by response surface methodology, and its physicochemical properties and tribological characteristics
Bibliographic record
Abstract
This study is focused on optimising the process variables of trimethylolpropane (TMP) ester synthesis from waste cooking oil methyl ester (WCOME) using response surface methodology with Box–Behnken experimental design in order to maximise the TMP ester (TWCOE biolubricant) yield. The following process variables were optimised: (1) reaction time, (2) TMP-to-WCOME ratio, and (3) sodium methoxide catalyst concentration. The predicted TWCOE biolubricant yield was 97.06 %, which conformed well with the experimental TWCOE biolubricant yield of 96.12 %. The quadratic response surface model demonstrated a robust fit with the experimental data ( R ² = 0.9888).The physicochemical properties and tribological characteristics of the TWCOE biolubricant were assessed and compared with those of commercial lubricants. The TWCOE biolubricant had a kinematic viscosity of 41.55 mm 2 /s at 40 °C and 6.93 mm 2 /s at 100 °C. The TWCOE biolubricant had an acid value of 0.4 mg KOH/g, flash point of 222.2 °C, and viscosity index of 125.30. The coefficient of friction of the TWCOE biolubricant (0.045) was lower than those of the SAE15W40, SAE0W30, and ATF9 lubricants (0.062, 0.088, and 0.089, respectively). However, the average wear scar diameter for the TWCOE lubricant (0.632 mm) was higher than those of commercial lubricants. The favourable lubricating characteristics suggest that the TWCOE biolubricant has the potential for use as an effective lubricant or additive in industrial machinery.
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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".