Comparative study of interval highly activated drying versus continue and intermittent convection drying processes
Bibliographic record
Abstract
Conventional airflow is the most widely used drying process, although it has several drawbacks that affect the performance and quality of the final product. Investigators have operated various innovative drying techniques to improve the process’s efficiency and the product’s quality. For several years, our research team has defined new intensification drying processes such as swell drying (including Instant Controlled Pressure Drop (DIC)), Multi-Flash Autovaporization (DDS), and other intermittent drying processes. These operations, generally based on interrupting product exposure to heat, can improve energy efficiency and product quality. They have solved some of the problems of conventional continuous drying, such as long time, high-energy consumption, surface hardening, shrinkage, and poor-quality attributes. However, the definition of active and tempering periods in conventional intermittent drying has been empirical due to the lack of fundamental studies. Recently, we defined highly activated interval drying operations as an innovative drying process that intensifies drying performance and improves the quality of finished products. It aims to ideally separate the transfer phenomena during drying, exclusively dedicating the active period (tON) to surface evaporation and the tempering period (tOFF) to the diffusion of water within the product.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".