In the blast furnace iron-making process (BFIP), there still has been a significant push to maintain a stable process and ensure maximum efficiency. Although some control systems can compensate for multiple types of disturbances, some significant process faults always require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnosis system to identify these faults quickly. This article focuses on a novel approach called deep stationary kernel learning support vector machine (DSKL-SVM) for BFIP fault diagnosis. To eliminate the impact of nonstationary property on modeling, analytic stationary subspace analysis (ASSA) is adopted to estimate consistent underlying features. Then, design a multilayer stacked deep kernel network to explore deep nonlinear information further. A DSKL-SVM classifier and the corresponding two-tier model optimization algorithm are constructed to isolate different data types. At last, a series of case studies based on actual BFIP present the effectiveness of DSKL-SVM. The results demonstrate that the proposed method has an outstanding effect on fault diagnosis, and it is verified that the performances of stationary projection (SP), deep stationary nonlinear (DSN) construction, and online computation times are superior to other methods.

The blast furnace ironmaking process is a crucial step in the steel industry. Effectively modelling the blast furnaces is significant in ensuring smooth operation and accelerating the digitalisation transformation of blast furnaces. The authors focus on the mechanism modelling of the blast furnace operation process, using a digital twin model development platform to simulate the main reaction processes inside the blast furnaces. Under on‐site production conditions, Si, Mn and Ti contents of molten iron and the corresponding indicators for model accuracy are calculated. For Si, Mn, and Ti content, the Root Mean Square Error values are 0.0678, 0.0108 and 0.0093 for dataset 1, while 0.0933, 0.0120 and 0.0111 for dataset 2, respectively, indicating that the model has a small simulation error and high accuracy. By comparing the simulation results with accurate laboratory results, the model is validated to have satisfactory simulation reliability and compensates for the existing shortcomings in the blast furnace mechanism modelling field.

The permeability index (PI) is a key comprehensive indicator that reflects the smoothness of internal gas flow in pig iron production via blast furnace. An accurate prediction for it is essential for forecasting abnormal furnace conditions and preventing potential faults. However, developing an early prediction model for PI has been neglected in existing research, and it faces massive challenges due to the strong nonlinearity, undesirable nonstationarity, and significant multiscale time delays inherent in the blast furnace data. To bridge this gap, a new modeling paradigm for PI is proposed to explore the inherent time delay characteristics among multiple variables. First, the data are progressively decomposed into multiple components using wavelet decomposition and spike separation. Then, a novel delay extraction method based on wavelet coherence analysis is developed to obtain accurate multiscale time delay knowledge. Furthermore, the integration of Orthonormal Subspace Analysis (OSA) and wavelet neural network (WNN) achieves comprehensive modeling across time and frequency domains, incorporating global and local features. A Gauss–Markov-based fusion framework is also utilized to reduce the output error variance, ultimately enabling the early prediction of PI. Mechanism analysis and a practical case study on blast furnace production verify the effectiveness of the proposed target-oriented prediction framework.