Dynamic Sinter Quality Prediction Based on Time-Shifted State Space Reconstruction

The prediction of sintering quality indicators is essential for the automation of the ironmaking process. However, various challenges, including variable time delays and relatively weak correlations, present difficulties in achieving precise modeling of the sintering process. To address these challenges, we introduce a novel method based on the nonuniform time delay embedding technique for predicting sintering quality indicators. First, we construct input variables with different compensated time delays. Second, we develop a low-dimensional approximation of the joint mutual information criterion to search the optimal variables and time delays. This approach effectively leverages valuable information from process variables while minimizing irrelevant information. Next, we implement a hybrid modeling technique that combines extreme gradient boosting and support vector regression to predict sintering quality indicators, allowing for adaptation to various operating conditions. Finally, we validate the effectiveness of our proposed method using real production data, specifically focusing on FeO content.