Scaling-up electrohydrodynamic drying for energy-efficient food drying via physics-based simulations
Bibliographic record
Abstract
Electrohydrodynamic (EHD) drying is a novel non-thermal drying method to dry heat-sensitive foods faster and with lower energy. Upscaling EHD drying to dry large amounts of food is the current challenge of this technology. In this regard, we quantify how successful a newly-proposed electrode configuration for EHD dryers is for drying commercial amounts of fruit for a wide range of operating conditions. To achieve this goal, we simulate an EHD dryer using physics-based modeling. The scalability was evaluated by quantifying the impact of fruit loading density in the dryer, applied voltage, and distance between electrodes on the drying time and energy consumption. Drying fruits in a commercial EHD dryer is more optimal when the dryer is densely loaded, compared to a low loading density. Loading the trays in the dryer to a capacity of 70% increased the drying time by 16%, compared to drying a few fruits widely spaced apart, but the energy consumption was 28% less. We identified the best strategy to dry a particular batch of fruit with EHD drying to achieve the fastest drying with the least energy possible. We found that it is most energy-efficient and quick to load the dryer close to its full capacity, instead of drying smaller batches in many different runs. By loading the trays in the dryer to 70% of their capacity, we could dry 7 times faster and with 11 times less energy in a single drying run instead of drying the same amount of fruit by many different runs. This study presents a key step towards upscaling EHD drying systems for the industry to dry large amounts of fruits in the shortest possible time and more energy-efficiently.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".