摘要
The coiling temperature control of a typical steel strip mill was investigated. Due to the high speed of a strip and complex circumstance, it is very hard to set up a cooling model with high accuracy. A simplified dynamic model was proposed, based on which a cooling control scheme with combined feedforward, feedback and adaptive algorithms was developed. Meanwhile, the genetic algorithms were used for the optimization of model parameters. Simulations with a model validated using actual plant data were conducted, and the results have confirmed the effectiveness of the proposed control methods. At last, a simulation system for coiling temperature control was developed. It can be used for new product trials and newcomer training.
The coiling temperature control of a typical steel strip mill was investigated. Due to the high speed of a strip and complex circumstance, it is very hard to set up a cooling model with high accuracy. A simplified dynamic model was proposed, based on which a cooling control scheme with combined feedforward, feedback and adaptive algorithms was developed. Meanwhile, the genetic algorithms were used for the optimization of model parameters. Simulations with a model validated using actual plant data were conducted, and the results have confirmed the effectiveness of the proposed control methods. At last, a simulation system for coiling temperature control was developed. It can be used for new product trials and newcomer training.
