Enzyme-catalyzed synthesis and kinetics of ultrasonic-assisted biodiesel production from waste tallow
Bibliographic record
Abstract
The use of ultrasonic processing was evaluated for its ability to achieve adequate mixing while providing sufficient activation energy for the enzymatic transesterification of waste tallow. The effects of ultrasonic parameters (amplitude, cycle and pulse) and major reaction factors (molar ratio and enzyme concentration) on the reaction kinetics of biodiesel generation from waste tallow bio-catalyzed by immobilized lipase [Candida antarctica lipase B (CALB)] were investigated. Three sets of experiments namely A, B, and C were conducted. In experiment set A, two factors (ultrasonic amplitude and cycle) were investigated at three levels; in experiment set B, two factors (molar ratio and enzyme concentration) were examined at three levels; and in experiment set C, two factors (ultrasonic amplitude and reaction time) were investigated at five levels. A Ping Pong Bi Bi kinetic model approach was employed to study the effect of ultrasonic amplitude on the enzymatic transesterification. Kinetic constants of transesterification reaction were determined at different ultrasonic amplitudes (30%, 35%, 40%, 45%, and 50%) and enzyme concentrations (4, 6, and 8 wt.% of fat) at constant molar ratio (fat:methanol); 1:6, and ultrasonic cycle; 5 Hz. Optimal conditions for ultrasound-assisted biodiesel production from waste tallow were fat:methanol molar ratio, 1:4; catalyst level 6% (w/w of fat); reaction time, 20 min (30 times less than conventional batch processes); ultrasonic amplitude 40% at 5 Hz. The kinetic model results revealed interesting features of ultrasound assisted enzyme-catalyzed transesterification (as compared to conventional system): at ultrasonic amplitude 40%, the reaction activities within the system seemed to be steady after 20 min which means the reaction could proceed with or without ultrasonic mixing. Reversed phase high performance liquid chromatography indicated the biodiesel yield to be 85.6±0.08%.
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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) | 0.001 | 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 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".