Numerical Modelling of Sonicated, Continuous Transesterification and Evaluation of Reaction Kinetics for Optimizing Biodiesel Reactor Design
Bibliographic record
Abstract
Biodiesel is an alternative and sustainable fuel that can reduce the dependence on fossil diesel. This commodity has not only been promoted in the developed world but also in Indonesia, Brazil and several developing counties. In a general procedure it is a product of a transesterification reaction of vegetable oils or waste cooking oils with an alcohol, in the presence of an acidic or basic catalyst. It is a slow reaction which is conventionally carried out in a mechanically stirred batch process. An advanced method to achieve high yield quality in less time is sonication of the reaction in an integrated continuous process. Sonication causes micro-cavitation in the reactant mixture. The cavitation bubbles can have an internal pressure and temperature as high as 1000 atm and 5000K, respectively. Violent collapse of these bubbles causes tremendous increase in mass transfer, thereby enhancing the reaction rates [1]. To optimize the design of the reactor, high fidelity modeling assisted design is pursued. This enables the effective integration of the reactant transport and the sonication effect in a coupled acoustic, reactive multiple-specie flow. In this work, a cylindrical reactor is considered in which reactant mixture will be circulated and sonicated by a sonotrode type ultrasound equipment. To simulate the sonication effect the linear, time independent wave equation is solved for the fluid domain, which provides us with the acoustic pressure variation in the fluid. To account for the attenuation of the wave due to cavitation bubbles the modified wave number is used. To account for the chemical reactions, laminar reacting flow is assumed for the reactant mixture for which the Navier-Stokes equations and transport equation for dilute species is solved for the fluid. A logical reaction rate coupling model, which is dependent on the acoustic pressure and flow velocity, is used to evaluate the kinetics of the reaction, which are to be used as a judging factor for the reactor design [2].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".