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Related Experiment Video

Updated: Sep 29, 2025

Fabric Moisture Uniform Control to Study the Influence of Air Impingement Parameters on Fabric Drying Characteristics

Fabric Moisture Uniform Control to Study the Influence of Air Impingement Parameters on Fabric Drying Characteristics

Published on: August 19, 2019

Control Method for Continuous Grain Drying Based on Equivalent Accumulated Temperature Mechanism and Artificial

Zhe Liu¹, Yan Xu¹, Feng Han¹

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.

Keywords:

double drive equivalent accumulated temperature mutual window process control

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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.