Comparative study of physicochemical and rheological property of waste cooking oil, castor oil, rubber seed oil, their methyl esters and blends with mineral diesel fuel
Bibliographic record
Abstract
In this work, physicochemical properties and rheological behaviour of waste cooking oil (WCO), castor oil (CO), rubber seed oil (RSO) and their methyl esters (ME), as well as ME blends (5, 10 and 15 vol%) with diesel fuel were investigated. Rheological properties of samples were measured in the range of 25–80 °C temperature and 5–300 s−1 shear rate. Similarly, rheological behaviour of WCO, CO and RSO based methyl esters (WCOME, COME, ROSME) and its blends (5, 10, and 15 vol%) with diesel fuel were also studied. Power law model was used to study the flow behaviour of all the samples. The viscosity behaviour of oils (WCO, CO and RSO), methyl esters (WCOME, COME and RSOME) and their blends with diesel fuel showed Newtonian nature in the temperature range of 25–80 °C. The viscosity values of the chemically modified oil samples (via transesterification) were found to be lower than the original oil samples. However, WCO, CO and their methyl esters showed a slight deviation from Newtonian behaviour between shear rate intervals of 5–100 s−1. The dynamic viscosity of RSO (25.58 mPa.s) was less than that of WCO (49.91 mPa.s) and CO (338.08 mPa.s). At 40 °C, the kinematic viscosity values of RSOME (3.81 mm2/s) and WCOME (3.36 mm2/s) were lower than the value of COME (10.59 mm2/s). The dynamic viscosities of the samples were found to be dependent on fatty acids chain length, unsaturation and temperature. According to fatty acid composition and physicochemical properties of the oils samples, WCO, CO and RSO are suitable for substituting edible feedstock to make biodiesel production sustainable. The fuel properties of the methyl esters and their blends with diesel were estimated as per ASTM D6751 biodiesel standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".