Application of Particle Swarm Optimization to Pressure Drop Prediction of Pneumatic Transport Pipe

Pipe Pressure drop is an important parameter of pneumatic transport system design,the traditional solution method is relatively complex.This paper proposes a method which establishes the pipe pressure drop predictive network model by taking gas flow rate,particle concentration and mixture ratio as the inputs of the neural network.In order to further improve the predictive precision of pipe pressure drop,the particle swarm algorithm is used to optimize the network weight and the threshold value by taking prediction error as the fitness value.In addition,an effective pressure drop prediction network is trained by using the sample data.By comparing the prediction data with the measured data of the powder pneumatic transport experimental device,the result demonstrates that the method has high precision and relatively high practical value.