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

ABSTRACT

Regarding the issue of low granulation qualification rates during the granulation coating of red clover seeds, this study theoretically analyzed the force conditions of seeds and powder particles under the action of liquid to obtain the main factors affecting seed coating quality. During the seed granulation coating process, an intermittent powder supply method combined with continuous liquid supply was utilized to control the ratio of powder to liquid. Using the granulation qualification rate as the evaluation index, single-factor experiments were conducted to investigate the effects of coating pan fill ratio, single powder supply amount, powder supply interval, and liquid supply amount on the quality of red clover seed granulation coating. Based on the results of the single-factor experiments, orthogonal experiments were conducted, revealing that the interaction of factors would influence the experimental results. To further optimize the quality of seed granulation coating, the mechanisms of powder and liquid in the adhesion process on granulation coating were explored. Orthogonal experiments were conducted on the process parameters of the granulation coating machine, and the GA-BP model was employed for optimization and solution. The optimal process parameter combination obtained was a coating pan fill ratio of 33.78 %, a single powder supply amount of 5.17 g, a powder supply interval of 7.7 s, and a liquid supply amount of 0.42 mL/s. Under this optimal parameter combination, granulation coating experiments with red clover seeds were performed, and the seed granulation coating quality was relatively high, with a granulation qualification rate of 97.7 %. The research results can provide a reference for optimization experiments on coating irregular seeds.