A Novel Prediction Method for Blast Furnace Gas Utilization Rate Based on Dynamic Weighted Stacked Output-Relevant Autoencoder

In the blast furnace (BF) ironmaking process, the gas utilization rate (GUR) is a crucial indicator for reflecting the energy consumption and operating status of BF. However, due to the complex and harsh environment in the BF top, accurately obtaining GUR online is not an effortless task. Although many studies have been carried out to predict GUR through data-driven methods, some challenges still exist: 1) limited feature extraction capability for complex data patterns; 2) prediction accuracy is sensitive to the fluctuation of BF working conditions. Therefore, a novel deep learning method is proposed based on dynamic weighted stacked output-relevant autoencoder (DW-SOAE) for GUR online prediction. First, the input layer variables for each AE are weighted according to their importance, which will reduce output-unrelated features. Then, the output variable is also reconstructed at the output layer of each AE, which ensures extracted features can largely predict GUR. Next, considering that the fluctuation of BF working conditions may affect prediction accuracy, the density peak clustering algorithm is used to cluster the process variables, and several DW-SOAE-based submodels are built for GUR prediction. Finally, the effectiveness and superiority of the proposed GUR prediction method are verified in industrial experiments.