A study on the ultrasonic oil extraction and in situ transestrification of microalgae biodiesel
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
Ottawa-Gatineu,Predicted that in 2020 Oil and LNG production both types of fossil fuels is declining.These conditions encourage countries in the world to perform efficiency and explore the potential of new energy source and perform diversification of fuel oil.One of the alternative energy sources that can be proposed is biofuel Biofuel to have become the focus of many researchers in many countries There are many of the biodiesel sources which are derived from the seeds of the plants, but at the same time people need these plants for food supply(Palm oil,coconut oil,Soybean oil, corn oil, etc).The use of microalgae to replace fossil fuel has become one focus of attention as the use of this plant is beneficial since it has lesser or nearly no effect to the world’s food supply The extraction and transestrification of microalgae oil are interesting topics (besides culturing and microalgae strain)in the development process of biodiesel microalgae.Some methods of biodiesel production have been well known.In this research,the ultrasonic method was used to identify the dominant factors in the extraction and in situ transesterification processes and to determine the optimum combination of the dominant factors.This is an experimental laboratory study that was run using ultrasonic homogenizer Omni Ruptor 4000,examining the effect of type of solvent, solvent concentration,alga-solvent ratio, ultrasonic power,ultrasonic time,ultrasonic pulse and mixing toward yield.Based on Box-Behnken design,a quadratic model is developed to correlate the parameter to surface area to analyze certain factors and combination of dominant factors. The result shows that power,time and pulse as the most dominant factors that influence the yield.In the extraction, the combinations of pulse-time give better result than power-pulse combination.While in the in situ transesterification,the power-time combination give better result that power-pulse combination.Even though the optimum point has not been reached yet,in general the combination of power-time is categorized as the most influential combination to increase the yield.The experimental values versus predicted values use the model equation developed by STATISTICA Software version 6.0.A line of unit slope,the line of perfect fit with points corresponding to zero error between experimental and predicted values is also shown that the coefficient of correlation(R2)is 0.97977 (for extraction) and 0.98743 (for in-situ).Saponication number is 114, 269 KOH/1 g oil.The percentage of FFA is 19.67% consisting of monounsaturated and polyunsaturated Octadecenoic acid (C18:1) 43.49%, Dedecanoic acid(C12) 16.30%,Hexadecanic acid(C16:0)12.51%,Tetradecanoic acid (C14)11.43%,Octadecadinoic acid(C18:2)5.85% dan Octadecanoic acid (C18:0) 5.62%.
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 | 0.000 | 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) | 0.000 | 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