Optimization of a two-step process for the production of ASTM-standard biodiesel from refurbished oils and fats
Bibliographic record
Abstract
Biodiesel can be produced from low cost feedstock such as refurbished waste fats and oil, which contain significant amounts of free fatty acid (FFA), by using two-step process. The first step converts the FFA by acid catalysis, and the second step converts the triglycerides (TG) by base catalysis. Currently, the major challenge for the industrial production of biodiesel is to optimize biodiesel yield and meet ASTM standards. Experiments have been performed to optimize the reaction conditions. The process variables studied were: FFA content in feedstock, water content, type and amount of acid catalysts, type of base catalysts, salts and their types, and temperature. The experimental parameters studied were: (1) FFA content (0, 10, 15, 20, 30, and 50 mass%), (2) H2SO4 (1.0 and 2.0 wt% of feed) and HCl (0.74 wt% of feed), (3) NaOH (1.0 wt%), KOH (1.4 wt%), and NaOCH3 (1.35 wt% of feed), (4) temperature for the acid step (60°C), and for the base step 23°C and 40°C), (5) water content (water produced from neutralization, and water produced from neutralization as well as from 20% FFA). The optimum conditions found were: (1) FFA content up to 10–15% max., (2) H2SO 4 (1.0 wt%) and HCl (0.74 wt%), (3) NaOH (1.0 wt%), NaOCH 3 (1.35 wt%), and KOH (1.4 wt%), (4) acid step temperature (60°C) and base step temperature (23°C), and Feed/THF/CH3OH volume ratio 1:1:1. These conditions produced biodiesel, which meets ASTM standards.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".