LAUSR

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.