This study examines techniques to enhance the efciency of mechatronic modules in wheat tempering throughvirtual prototyping (digital twin), advanced moisture sensors, and multi-objective optimization. The proposed frameworkleverages genetic algorithms and artifcial neural networks to improve moisture uniformity by ≥ 20% and reduce waterenergy consumption by ≥ 10%. Pilot-scale experiments validated the model with a determination coefcient of R² ≥ 0.95,demonstrating robust real-time control performance.
| Mualliflar | Qamariddinov Shohruh Akmal o‘g‘li |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2025-08-01 |
| Jild | 1 |
| Son | 8 |
| Betlar | 15-20 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.17447655 |
DOI: 10.5281/zenodo.17447655 · Maqolaning asl sahifasi
wheat tempering, mechatronic module, digital twin, genetic algorithm, artifcial neural network, optimization, moisture sensors
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