Prediction of Hydraulic Gradient of Fly Ash Paste Pipeline Transportation Based on MMAS-BP Neural Network

Hydraulic gradient is an important parameter in the design of coal ash paste underground treatment system, which determines the energy consumption and operation cost. In order to master the accurate results of hydraulic gradient, MAX-MIN ant system （ＭＭＡＳ） and BP neural network are applied to the prediction of hydraulic gradient. The prediction model of hydraulic gradient is established. The practical application shows that the model has fast convergence and global prediction of MAX-MIN ant system, and the strong mapping effect of BP neural network. The prediction results completely meet the needs of practical applications.