Research on FeO prediction of sintered ore in machine tail section for intelligent sintering
Aiming at the prediction accuracy of FeO in sintering process,a prediction model based on machine learning was proposed.By collecting and processing the temperature data of the tail section,a data set containing multiple features was established.The feature selection method based on MIV(Mean Impact Value)was used to screen out the features that account for a higher weight of the prediction model.The Bi-LSTM(bidirectional long-short-term memory neural network)algorithm was used to train and test the production process data to obtain a high-precision prediction model.The prediction effect of the model was verified by experiments,and compared with other neural network model methods,the comparison results show that the model has high prediction accuracy and practicability.Within the allowable range of enterprise error,the accuracy rate reaches 90.2％,so it can provide an important reference for the application of intelligent sintering technology and the control and optimization of sintering quality.
intelligent sinteringprediction modelFeO content of sinterbig dataneural network
NETL
NSTL
万方数据
