Pre-Treatment of Waste Frying Oils for Biodiesel Production
Bibliographic record
Abstract
Synthesis of biodiesel is a strategic step in overcoming energy scarcity and the environmental degradationcaused by the continuous use of the petroleum based energy. Biodiesel as an alternative fuel for diesel engine isproduced from renewable resources such as vegetable oils and animal fats. The main obstacle in the biodieselproduction is the high price of the raw materials, resulting in the price of biodiesel is not competitive comparedto the petroleum diesel. Therefore, the use of waste frying oils (WFO) is one way to reduce the cost of biodieselproduction, because of its availability and low price. In the present work, WFO from California Fried chicken(CFC) restaurants in Surabaya were used as feed stock for the biodiesel production. The experiments wereconducted using three steps of processes: pre-treatment of WFO, preparation of alumina based compositecatalyst CaO/KI/γ-Al2O3 and transesterification of treated WFO. WFO was treated by several types and variousamounts of activated adsobents. The treated WFO was transesterified in three neck glass batch reactor withrefluxed methanol using CaO/KI/γ-Al2O3. The results reveal that the best method for treating WFO is using 7.5%(wt. % to WFO) of coconut coir. Alumina based composite catalyst CaO/KI/γ-Al2O3 was very promising fortransesterification of WFO into biodiesel. The yield of biodiesel was 83% and obtained at 65ºC, 5 h of reactiontime, 1:18 of molar ratio WFO to methanol and 6% amount of catalyst.
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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 | 0.000 | 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".