Cutting-down the energy consumption of electrohydrodynamic drying by optimizing mesh collector electrode
Bibliographic record
Abstract
Drying is one of the most energy-intensive processes in the multiple industries, due to the high latent heat required to evaporate the water, which is often done by employing hot-air drying. Electrohydrodynamic (EHD) drying is an alternative, innovative drying technology with large potential for industrial application and lower energy consumption. EHD drying is non-thermal, which makes this technology particularly suitable for drying of heat-sensitive biomaterials. A key bottleneck for EHD drying is the process scalability in order to uniformly dry large amounts of product, which is limited by the geometrical design of the collector electrode. To overcome this challenge, a recently introduced electrode configuration – a mesh collector – is further optimized in order to significantly reduce the energy consumption of the process. Exergy analysis was used to identify the energy conversion losses in ion production, ionic flow generation, and convective dehydration stages of fruit. As a result, a much more energy-efficient mesh configuration was designed. This improved design resulted in a similar drying rate as a normal mesh collector but showed a seven times smaller energy consumption. This upscalable, cleaner, and also much more energy-efficient EHD dryer design paves the way for industrial prototypes and pilot plants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".