Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.

Blast furnace ironmaking is one of the most complicated industrial processes. As an essential reference index for blast furnace operation, the prediction of silicon content in hot metal is important. Most previous works focus on the dynamics and nonlinearity of the process without comprehensively considering the correlation between the process variables and the silicon content. Besides, the timing mismatch of input and output variables caused by the inertia of the process still cannot be handled effectively. To solve these problems, in this article, the proposed model puts extra attention on input variables to strengthen the information of key variables, and introduces causal convolution-based self-attention to incorporate local context into attention mechanism in the temporal dimension, which realizes local awareness enhancement and variables soft alignment. With a series of theoretical and practical verification, the hybrid model shows significant improvement at hit rate and mean-square error.

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore, early detection of abnormalities in blast furnaces is an important task for ironmaking processes. However, owing to the large fluctuations in the quality of raw materials, dynamic operating conditions, as well as the impact of the hot blast stoves switches, the measurements of blast furnace show severe nonstationary characteristics. All these factors make monitoring the blast furnace a challenging task. In this article, a nonstationary process monitoring method called consistent trend feature analysis (CTFA) is proposed, which can extract the trend-related features and discard perturbations in process data. The directions and amplitudes of the extracted trends are used for abnormality detection, and a local-learning-based method is proposed for determining a time-varying control limit. The detection performance of the proposed method is analyzed, with a sufficient condition and a necessary condition for the detectability given. The effectiveness of the proposed method is validated by the practical data collected from a large-scale blast furnace located in Liuzhou, China.

As a key thermal-state indicator of the iron ore sintering process, the content of ferrous oxide (FeO) in the finished sinter is directly related to product quality. Based on the massive data of sintering process, the data-driven soft sensor model provides a good choice for real-time FeO content detection. However, the complex characteristics of the data, including dynamics, nonlinearity, and multisource heterogeneity, are still the main obstacles to improving the modeling accuracy. To solve this problem, in this article, a multisource information fusion autoformer (MIF-Autoformer) model is introduced. First, feature-level information fusion and data-level information fusion are implemented based on the MIF strategy. Then, the comprehensive information of the sintering process is fed to the downstream autoformer model in a serial manner, which not only improves the information capacity but also provides additional prior information about the FeO content grade. This is helpful for autoformer to capture the complex temporal distributions in the sintering process. Finally, the proposed model is applied to a real sintering plant. Experimental results show that the hybrid image features provided by the MIF strategy have a general optimization effect on different competitive models, and MIF-Autoformer exhibits the lowest prediction error on the test set.

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.