Intelligent Control Method and Application of Sintering Process

Sintering process is one of the most important parts during the whole iron and steel production, the quality of the produced sinter is directly affected by the stability of the sintering process, and high-quality sinter is beneficial to the subsequent iron-making process. As a crucial parameter characterizing the state of the sintering process, burn-through point (BTP) is typically utilized to determine whether current sinter production is stable, therefore the stable control of the BTP is the key to controlling the stability of the entire sintering process. In this paper, an intelligent sintering process control method consisting of two modules is proposed. One module based on the deep neural network (DNN) is designed to achieve accurate prediction of BTP, the other module is designed to stabilize the BTP by adjusting the sintering machine speed, according to the deviation between the predicted BTP from the first module and the target BTP set by the expert. Moreover, the speed adjustment model of sintering machine is established by a convolution neural network (CNN), whose input design is similar to the observation logic of manual adjustment by operators. The experimental results show that the sintering machine speed adjustment recommended by the method is highly consistent with that in the actual sintering production process. The proposed intelligent sintering process control method provides a favorable guidance for the speed adjustment of sintering machine, and implies the foundation for the closed-loop control of the sintering machine speed.