Prediction of FeO content in sintered ore based on ICEEMDAN and CNN-BiLSTM-AM

FeO content of sintered ore is an important reference index for measuring the performance of sintered ore. It significantly impacts the ironmaking process, iron quality, and energy consumption. Aiming at the current problem of delayed and poor accuracy of sintered ore FeO content detection results, this article proposes a hybrid network model that incorporates improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) for FeO content prediction. First, the FeO content time series were decomposed using ICEEMDAN to obtain sub-layers with different frequencies. Then, the features with higher correlation with FeO content were selected by feature selection as model inputs, followed by predicting the decomposition sequences of FeO content using CNN-BiLSTM-AM for the feature-selected variables, respectively. Finally, all predicted sublayer predictions were reconstructed into the final prediction by summation. The proposed model effectively captures the essence of the sequence through the ICEEMDAN algorithm, extracts deep features from FeO content data using CNN, captures contextual information through BiLSTM, and enhances feature extraction capability with AM. The experimental results show that the collaboration of CNN, BiLSTM, and AM effectively enhances the modelling capability of the model and significantly improves the prediction accuracy. Additionally, the ICEEMDAN decomposition algorithm is employed to enhance the prediction performance further, offering advantages over other decomposition techniques. The MAE, MAPE, RMSE, RRMSE, and R² of the new ICEEMDAN-CNN-BiLSTM-AM (ICBA) model are 0.0751, 0.846%, 0.0937, 1.0500%, and 0.9646, respectively, demonstrating a significant improvement in prediction accuracy and outperforming the relevant comparison models.