Application of Adaptive Mutation Particle Swarm Optimization Algorithm in Roller Optimization

To solve the problems of excessive volume and weight of the double roll granulator, a volume function model was established with the key parameters of the double roll structure, which is the main component of the granulation mechanism, as variables. Through the Matlab software platform, combined with five constraint conditions including tooth surface contact fatigue, gear root cutting restriction, tooth root bending fatigue, straw particle cross-sectional size and surface area, the adaptive mutation particle swarm algorithm is used for iterative constraint optimization design. Compared with the original double roll structure model, the optimized design achieves more compact volume parameters while satisfying the constraints, effectively reducing the problems of excessive volume and weight of the double roll granulator.