Research on Power Consumption Model of Permanent Magnet Direct Drive Belt Conveyor System Based on Fruit Fly Optimization Algorithm-Generalized Regression Neural Network-Particle Swarm Optimization

Abstract

The permanent magnet direct drive electric drum is used to replace the driving device of the traditional belt conveyor, which simplifies the structure. However, the permanent magnet direct drive electric drum still operates in a high speed and stable state after starting, and cannot transport materials reasonably, belt speed cannot match the coal quantity reasonably; the problem of energy waste is still severe. Thus, based on fruit fly optimization algorithm-generalized regression neural network-particle swarm optimization algorithm, the power consumption network model of the permanent magnet direct drive belt conveyor system is established. The relationship among belt velocity, coal transport quantity and power is obtained, and the optimal belt velocity is found with the power consumption network model. Thus, the minimum power is obtained. The algorithm selects the optimal smoothing factor by fruit fly optimization algorithm, inputs the optimal smoothing factor into generalized regression neural network and establishes the optimal power consumption model. Then establishes the matching relationship of coal quantity, optimal power, and optimal belt speed in the power consumption model by the adaptive weight particle swarm optimization. The model is compared with the network model with smoothing factors of 0.7 and 0.4. The comparisons show that the optimized model performs better, which can be better applied to establish the energy consumption model of the system.