Process model extraction method of converter steelmaking based on improved autoencoder

Abstract

The blowing process in converter steelmaking at the blowing stage mainly includes oxygen supply, slag discharge, and bottom blowing. The stability of the blowing process directly affects the quality of the molten steel at the end. The traditional static control method derived from the blowing process model based on material and heat balances ignores the strong coupling relationship between raw materials and process parameters, resulting in its low reliability. Furthermore, data types for raw materials and process parameters are scalar and time series, respectively. Therefore, to extract the features of the abovementioned complex mixed data, this paper proposes a process model extraction method for converter steelmaking based on an improved autoencoder (IAE). The IAE method is based on an autoencoder that includes fully connected modules, long short-term memory network, one-dimensional convolution, and batch K-means module. In the encoder, fully connected modules, long short-term memory networks, and one-dimensional convolutional modules extract nonlinear features of scalar data, long-term dependent features of time series, and local features of time series, respectively. Hence, the hidden vector is obtained by mapping the original high-dimensional data to a low-dimensional feature space using the encoder. To update the cluster center and calculate the clustering loss, the hidden vector is input to the batch K-Means module. Thus, the decoder reconstructs the hidden vector back to the original space to yield reconstructed data, which is then used to calculate the reconstruction loss. The IAE model is trained jointly with clustering and reconstruction losses. Finally, the cluster center of the original data and cluster category of each sample are obtained. The closer the sample is to the cluster center, the better the process parameters are controlled. Additionally, samples within the same cluster category are closer during the process operation. Therefore, the oxygen supply, slagging, and bottom-blowing processes of the closest samples are considered the process models for this type of sample. The effectiveness of the IAE model is evaluated using the endpoint quality index of real data from converter steelmaking. The average hit rate for the endpoint carbon mass fraction within the error range of ±0.02% is 95.06%, the average hit rate for the endpoint temperature within the error range of ±20 ℃ is 91.48%, and the average double hit rate within the error range of ±0.02% carbon mass fraction and ±20 ℃ temperature is 90.80%. Therefore, the results show that the process model extraction method improves the endpoint hit rate.

Keywords

• converter steelmaking
• multivariate time series
• process model extraction
• long short-term memory network
• one-dimensional convolutional network