Life cycle assessment of biodiesel production using a nonionic surfactant and CaO from eggshell as a catalyst
Bibliographic record
Abstract
Life cycle assessment (LCA) is a tool used to evaluate the environmental impacts and resources used to manufacture a product. The present study proposes an innovative and unprecedented based on LCA of biodiesel production from the methyl transesterification of soybean oil catalyzed by eggshell-derived CaO and using the nonionic surfactant nonylphenol ethoxylate (NP6EO). Biodiesel was produced under mild conditions with a 1:4 methanol-to-oil ratio, 2 wt% CaO, 1 wt% NP6EO, and reaction times of 2 hours with surfactant and 3 hours without, with a yield of 95.18% and 97.75%, respectively. The life cycle impact assessment (LCIA) was performed using the SimaPro software and the Ecoinvent 3.6 database by implementing the CML-IA baseline method. Results indicated that the catalyst preparation process had the lowest environmental impact, while soybean oil used contributed the most across all impact categories. The surfactant-based process was 77% more eco-efficient than the non-surfactant process. Results also showed that surfactant concentration has more influence on impacts than additional electricity consumption due to a longer reaction time. Compared to traditional KOH-catalyzed biodiesel production, the use of CaO from eggshells and NP6EO demonstrated a lower environmental impact, suggesting this method is a promising alternative to conventional processes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".