Utilization of green seed canola oil for biodiesel production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Increasing percentage of green canola seed every year is a serious problem for canola growers. Chlorophyll content of this oil is very high, which makes it more susceptible to photo‐oxidation and ultimately the oxidation stability of the oil is very reduced. Hence green seed canola oil is underutilized for edible purposes. The present work is an attempt to produce high‐quality biodiesel from green seed canola oil and methanol, ethanol and various mixtures of methanol and ethanol using KOH as a catalyst. A mixture of alcohols improved the rate of reaction. After transesterification of green seed canola oil using KOH, the chlorophyll content of the oil was decreased substantially (from 22.1 ppm to 10.3 ppm). Characteristics of the esters prepared from green seed canola oil were well within the limits of ASTM standards. Lubricity of the green seed oil esters was excellent (20% decrease in wear scar area) when added at 1 vol% to the base fuel. Oxidation stability is crucial for long‐term storage of the fuel. Oxidation stability index (OSI) of green seed esters was 4.9 h at 110 °C, which is much less than the European Standard (6 h at 100 °C). The low oxidation stability of green seed esters is attributed to its higher chlorophyll (10.3 ppm) content. An attempt was also made to reduce the chlorophyll content of the oil before transesterification using activated carbon treatment, and it was observed that chlorophyll content was reduced from 22.1 to 2.2 ppm. Copyright © 2006 Society of Chemical Industry
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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