Development of hot-dip galvanized air knife automatic control system based on recurrent neural network
The service life of cold-rolled galvanized sheet is longer than that of cold-rolled raw sheet,it is widely used in industries such as automotive and home appliance manufacturing,building materials,etc.In the hot-dip galvanizing process,the control of the zinc layer thickness is a very important production process.At present,in most domestic galvanizing production lines,the control of zinc layer thickness still relies on manual control,which consumes a lot of energy and brings quality risks of zinc layer thickness fluctuations.This article establishes a control system of air knife recurrent neural network,based on the equipment and process characteristics of a galvanized line in a steel plan.It takes such as the difference between the actual zinc layer and the target zinc layer,the distance from the air knife to the strip surface,the height of the air knife,the stability roll and the strip speed as the input of the recurrent neural network,and the air knife pressure is used as the output of the network.The temporal characteristics of the recurrent neural network are used to realize the closed-loop control of zinc layer thickness.Actual production data shows that this system can realize automatic zinc layer thickness control while reducing zinc consumption.This algorithm can be widely applied to the design process of air knife control models with different equipment characteristics,and has great application prospects.
hot-dip galvanizingcold rollingair knivesrecurrent neural networksautomatic control
万方数据
维普
