Wenze Jiang

Anomaly detection of liquid levels in molds is an important task in continuous casting. Data that consists of a series of liquid levels in mold during a continuous casting process can be viewed as a time series, on which Time Series Anomaly Detection (TSAD) methods can be applied. However, the abnormal and normal data in the liquid data in the mold sequence share similar features. And due to manual control limitations, the anomaly sequence in liquid level in mold data lasts longer. Therefore, using existing TSAD methods based on AutoEncoders (AEs) often results in high false positive rates. In this paper, a novel framework is proposed for anomaly detection of liquid level in mold by using unsupervised deep-learning-based TSAD. The framework decomposes a time series into normal and error sequences. A forecasting network reconstructs the normal sequence to solve the first issue, which allows the proposed method to consider the context. An error extraction network generates errors from the original sequence to solve the second issue. It removes anomalies from the original sequence during training to prevent anomaly pollution and allows the forecasting network’s training to be free from anomaly pollution. A new dynamic threshold method is proposed to identify anomalies. The proposed method is evaluated on the actual casting dataset by comparing it with baseline methods. The experiment results indicate that the proposed framework outperforms some of the best anomaly detection methods in terms of accuracy, precision, and F1 score.

Continuous casting production is an important stage in smelting high-quality steel, and automatic casting control based on artificial intelligence is a key technology to improve the continuous casting process and the product quality. By controlling the opening degree of the stopper rod reasonably, the mold can be filled with liquid steel stably in the specified time window, and automatic casting can be realized. In this paper, an automatic casting control method of continuous casting based on an improved Soft Actor–Critic (SAC) algorithm is proposed. Firstly, a relational model of the stopper rod opening degree and the liquid steel outflow velocity is established according to historical casting data. Then the Markov Decision Process (MDP) model of the automatic casting problem and the reinforcement learning framework based on the SAC algorithm are established. Finally, a Heterogeneous Experience Pool (HEP) is introduced to improve the SAC algorithm. According to the simulation results, the proposed algorithm can predict the stopper rod opening degree sequence under the constraint of the target liquid level curve. Under different billet specifications and interference conditions, an accuracy of 80% of liquid level in the mold and a stopper rod opening degree stability rate of 75% can be achieved, which is 4.29% and 3.17% higher than those for the baseline algorithms, respectively.