OPTIMIZATION OF RADIOFREQUENCY HEATING OF IN-SHELL EGGS THROUGH FINITE ELEMENT MODELING AND EXPERIMENTAL TRIALS
Bibliographic record
Abstract
Abstract—Considering Radio Frequency (RF) heating as a viable alternative for the in-shell heating of eggs, Finite Element Modeling and simulation of RF heating of in-shell eggs at 27.12 MHz were carried out to assess the feasibility and heating uniformity of the process. According to the recommendations of USDA-FSIS for the pasteurization of eggs, egg white must be heated up to 57.5◦C, and the egg yolk has to be heated up to 61.1◦C for 2 min. The objective of the simulation was to determine the location of hot and cold spots generated due to non-uniform heating. A parallel plate setup for Radio Frequency heating was simulated for different electric field strength levels and orientations of the egg (long axis parallel and long axis perpendicular to the plates). The simulation results were experimentally verified and the simulation procedure was validated using a laboratory parallel plate RF setup. A coaxial cavity design was simulated with a similar approach. Results indicated that both the parallel and coaxial cavity designs were suitable for in-shell pasteurization of eggs provided that the eggs were rotated to maintain the uniformity in heating. After the simulation of RF heating process, the process optimization was carried out to determine the most effective procedure for the process. The varying parameters obtained by using different modeling techniques for radiofrequency heating of in-shell eggs, were optimized using MATLAB. Laboratory scale experimental trials were conducted to test the validity and effectiveness of the optimized parameters. The optimal parameters set forth were found to be more efficient in terms of heating time and uniformity.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".