Utilization of green seed canola oil for biodiesel production
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".