MODEL AND METHODS FOR ENHANCING THE EFFICIENCY OF MECHATRONIC SYSTEM MODULES USED IN THE MOISTENING PROCESS WITHIN WHEAT PROCESSING SYSTEMS
DOI:
https://doi.org/10.5281/zenodo.17447655Keywords:
wheat tempering, mechatronic module, digital twin, genetic algorithm, artifcial neural network, optimization, moisture sensorsAbstract
This study examines techniques to enhance the efciency of mechatronic modules in wheat tempering through
virtual prototyping (digital twin), advanced moisture sensors, and multi-objective optimization. The proposed framework
leverages genetic algorithms and artifcial neural networks to improve moisture uniformity by ≥ 20% and reduce water
energy consumption by ≥ 10%. Pilot-scale experiments validated the model with a determination coefcient of R² ≥ 0.95,
demonstrating robust real-time control performance.
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