Optimization of process parameters for Trifolium pratense L. seed granulation coating using GA-BP neural network.

Area of Science:

• Agricultural Engineering
• Materials Science

Background:

• Low granulation qualification rates in red clover seed coating pose a challenge.
• Understanding the forces affecting seed and powder particles is crucial for improving coating quality.

Purpose of the Study:

• To analyze factors influencing red clover seed granulation coating quality.
• To optimize the granulation coating process for irregular seeds.

Main Methods:

• Theoretical analysis of forces during coating.
• Single-factor and orthogonal experiments to evaluate process parameters.
• Application of a GA-BP model for parameter optimization.

Main Results:

• Identified coating pan fill ratio, powder supply, powder interval, and liquid supply as key factors.
• Determined optimal parameters: 33.78% fill ratio, 5.17g powder, 7.7s interval, 0.42mL/s liquid.
• Achieved a 97.7% granulation qualification rate under optimal conditions.

Conclusions:

• The study provides an optimized process for red clover seed granulation coating.
• The findings offer valuable insights for coating irregular seeds effectively.