Intensifying biofuel production using a novel bionic flow-induced peristaltic reactor: biodiesel production as a case study
Bibliographic record
Abstract
Intensification of biofuel production processes could play a critical role in boosting the economic and environmental features of the whole process. A novel bionic flow-induced peristaltic reactor with a high conversion rate is constructed to realize efficient biofuel production from high-concentration high-viscosity fluids. It is experimentally verified through biodiesel production from soybean oil. Experimental results show that the conversion efficiency is up to 89.9% at 10 s in the peristaltic reactor, which is 38.4% higher than that in the rigid tube reactor. Furthermore, a three-dimensional peristaltic model is conducted to understand the mechanism of heat and mass transfer enhancement. The simulation results show that an increase in peristaltic amplitude strengthens the mixing of the bionic peristaltic reactor by 92.5-100.8%. The temperature distribution in the bionic peristaltic reactor is more uniform than in the traditional rigid tube reactor. The results demonstrate that the conversion rate of soybean oil in the bionic flow-induced peristaltic reactor is 528.82% min-1, which is 17-60 times higher than other intensified reactors operating in either continuous or batch modes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".