Gaseous Ozone Distribution Within a Forced Air Ozone Reactor for Bulk Produce Decontamination
Bibliographic record
Abstract
Recalls of fresh produce due to possible bacterial contamination are increasing. Commonly used disinfection methods such as washing in chlorinated water can impact food quality and often have limited effectiveness. Recently, an Ontario food-processing company has designed and built a fresh produce decontamination treatment system that fumigates bulk produce within a forced air ozone reactor with low concentrations of gaseous ozone. Prior trials demonstrated that a 2-log reduction of a Listeria surrogate could be achieved throughout a 500 kg batch of apples using the reactor although the distribution of ozone through the produce bed is unknown. The current study was undertaken to gain a better understanding of the ozone transport and flow dynamics within the reactor. Measurements of ozone concentrations, air flow rates, pressure, temperatures, and humidity were collected and recorded during system operation to characterize the system pressure losses, thermal characteristics and operating treatment conditions. It is concluded that a balance between superficial air velocity and initial ozone concentration is required to minimize pressure drops (energy cost) while maintaining well distributed airflow throughout the bed and to achieve desired microbial reduction. The data generated will enable the reactor design to be further optimized and applied to decontaminating a diverse range of produce type.
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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) | 0.000 | 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) | 0.001 | 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".