Aiming at the problem that unlabeled samples cannot be utilized by machine learning due to the low detection frequency of sinter chemical indexes,a prediction model for sinter chemical indexes that makes full use of the useful information in the samples is proposed.Firstly,the unlabeled samples are transformed into labeled samples by combining Gaussian mixture model(GMM)and K-nearest neighbor(KNN)algorithm,and then combined with long short-term memory(LSTM)unit for predicting three chemical indexes,namely,total Fe mass fraction,FeO mass fraction,and alkalinity of sinter.By comparing with the three models of back propagation neural network(BPNN),recurrent neural network(RNN),and LSTM,the results show that the proposed model has a low prediction error.The prediction hit rates of total Fe mass fraction and FeO mass fraction reach 98.73%and 95.33%,respectively within the allowable error of±0.5%,and the prediction hit rate of alkalinity is 98.13%within the allowable error of±0.05,demonstrating high prediction accuracy.
chemical indexes of sinterprediction modelunlabeled samples processing algorithmLSTM(long short-term memory)data preprocessing
NETL
NSTL
维普
万方数据
