Convergence of Digital Twins and food drying technology: How to bring the next generation of dryers to life!?
Bibliographic record
Abstract
Digital Twins technology is rapidly growing and has the potential to revolutionize traditional food-processing methods. However, their application in food-drying processes is still in its infancy. This study aimed to explore how Digital Twins can be applied to food drying process. Traditionally, food drying is performed under constant conditions, where air temperature and velocity remain constant. However, the literature review shows that variable drying conditions (trajectories) can improve both energy efficiency and product quality. The challenge is that the trajectories are calculated based on what happened in the process, not what is currently happening. Digital Twins address this shortcoming by enabling decision making based on real-time data. In this conceptual review paper, physiochemical parameters as an element of the physical world of a Digital Twins-based smart food dryer is first presented. Next, potential sensors for building a digital counterpart of the physiochemical parameters are discussed. This is followed by mathematical models, dynamic optimization, and advanced control, which are the core elements of a decision-making and control unit. Finally, future research needs are discussed. This conceptual review paper will guide and give a solid insight to academic researchers, companies, and other potential stakeholders on merging Digital Twins and food drying technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".