Modeling on Hydrocyclone Separation Performance by Neural Network
A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.
Neural network LZW Hydrocyclone Modeling
Fengqin He Ping Zhou Jiangang Wang
Research Institute of Process Equipment and Pressure Vessels, East China University of Science and T Research Institute of Process Equipment and Pressure Vessels, East China University of Science and T
国际会议
上海
英文
185-188
2011-10-21（万方平台首次上网日期，不代表论文的发表时间）