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Fabric Moisture Uniform Control to Study the Influence of Air Impingement Parameters on Fabric Drying Characteristics
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Control Method for Continuous Grain Drying Based on Equivalent Accumulated Temperature Mechanism and Artificial
1College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Foods (Basel, Switzerland)
|March 25, 2022
Summary
A novel artificial intelligence (AI) control method improves continuous grain drying. This AI-control approach enhances prediction, accuracy, and stability for complex heat and mass transfer processes.
Area of Science:
- Agricultural Engineering
- Process Control
- Artificial Intelligence
Background:
- Grain drying involves complex heat and mass transfer with inherent challenges like delay, nonlinearity, and parameter uncertainty.
- Traditional control methods struggle with the intricate dynamics of continuous grain drying processes.
Purpose of the Study:
- To propose and establish an artificial intelligence (AI) control system for continuous grain drying.
- To address the complexities of heat and mass transfer in grain drying using an advanced AI-control method.
Main Methods:
- Developed a mechanism and data dual-drive AI-control method incorporating equivalent accumulated temperature (EAT) and a mutual-window approach.
- Implemented and experimentally verified the proposed AI-control system on a continuous grain drying test platform.
Main Results:
- The AI-control method demonstrated implicit prediction capabilities.
- Achieved high accuracy, strong stability, and self-adaptive abilities in controlling the grain drying process.
- The maximum control deviation for outlet moisture content was within -0.58% to 0.3%.
Conclusions:
- The proposed AI-control method is effective for managing complex grain drying processes.
- The system offers significant improvements in control accuracy, stability, and adaptability.
- This AI-driven approach provides a robust solution for optimizing continuous grain drying operations.
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