Systems modeling and intelligent control of meat drying process
Bibliographic record
Abstract
The objective of this research is to develop a system model and an intelligent controller for the meat drying process, which will lead to better control accuracy than the current PID control system used in meat manufacturing. In meat drying rooms, temperature and relative humidity are coupled by nonlinear thermodynamic laws. The coupling results in nonlinear fluctuations in relative humidity. The classical PID control method is not able to effectively control the relative humidity. The models of the systems of temperature and relative humidity for a meat drying room in this paper include the model of the plant, the model of the coupling, the controller, and the complete control system of the meat drying room. The proposed intelligent control uses the methods of a fuzzy PID controller to limit the fluctuations in the meat drying system. Results show that the performance of the proposed fuzzy PID control systems is superior in terms of relative humidity control to the classical PID control method used in the current control systems for the meat drying rooms.
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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) | 0.001 | 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) | 0.001 | 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".