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.

In complex industrial processes, it is difficult to measure the key quality variables online. It takes a long time to obtain quality variables through offline testing, which makes it difficult to get timely information to guide the production process. Therefore, this article proposes a novel dynamic time feature expanding and extracting framework for the sinter quality prediction. First, the original data are differentiated, compensated for time delay, expanded, and serialized by using time characteristics, and the input time series is reconstructed. Second, the integrated time features extractor is used to obtain the process information. Then, the recurrent neural network regression is applied to obtain the prediction of key quality variables. Finally, the effectiveness of the proposed method is verified by a numerical example, the actual data of sintering process and various comparative experiments, and the prediction effect of FeO content in sintering process is improved.

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.

In order to guarantee the smooth operation of the blast furnace ironmaking process, it is essential to consider the constraints for the optimization of this process. Due to the complexity of the process, it is challenging to obtain the description of the constraint functions and quantify the feasibility of solutions, making traditional optimization methods helpless. To address this problem, a transfer optimization framework is proposed that consists of a two-stage generation mapping algorithm, and an improved grey wolf optimizer (GWO) algorithm. The method achieves the transformation from constrained optimization to unconstrained optimization by establishing a proper mapping with distribution- and boundary-sensitive two-stage generation algorithm. Meanwhile, the density-amended GWO algorithm with adaptive search steps depended on the solution density distribution is applied to locate the optimal solutions. The intelligent transfer optimization method is validated by both numerical tests and practical data, and the results demonstrate the effectiveness of the proposed algorithm.