Research And Application Of Strip Tension Control System For Continuous Annealing Furnace

As an important part of the continuous annealing production line,the annealing furnace is mainly responsible for the continuous annealing heat treatment process of the strip steel.In order to ensure the continuous and stable progress of the process and ensure the production quality of the strip steel,the strip steel needs to run smoothly in the annealing furnace.The strip tension control of the annealing furnace is one of the research directions to ensure the stable operation of the strip.The existing research results ensure the production efficiency of the strip to a certain extent.However,the strip tension control process of the annealing furnace involves many factors,and it is difficult to establish an accurate mathematical model to describe the process.At present,the strip deviation phenomenon in the annealing furnace sometimes occurs.Therefore,it is necessary to further build the annealing furnace strip tension control model,and develop the corresponding secondary tracking interface,and finally apply it to the industrial site to improve the annealing furnace strip tension setting accuracy and reduce the annealing furnace strip tension fluctuation.This article takes the annealing furnace in the continuous annealing production line of a steel company as the research object,combined with the actual production process requirements,analyzes the annealing furnace strip tension control system in detail,and uses data analysis,BP neural network,cyclic neural network,and QLearning Algorithms and other technologies build a model to control the strip tension of the annealing furnace.Aiming at the problem of low precision of annealing furnace strip tension setting,this paper designs a annealing furnace strip tension setting plan based on BP neural network,combining BP neural network technology with traditional static meter technology,and applying on-site production data.After training,a model of annealing furnace strip tension setting with good generalization performance was obtained.Field production verification proves that the model can effectively reduce the occurrence of strip deviation.Aiming at the problem of large fluctuations in the annealing furnace strip tension during the production process,this paper designs an annealing furnace strip tension adjustment scheme based on the Q-Learning algorithm,and constructs the annealing furnace tension adjustment value Q table through the Q-Learning algorithm,and applies it The on-site production data was trained,and a good generalization performance of annealing furnace strip tension adjustment model was obtained.The verification of the simulation environment built by the cyclic neural network proves that the model can effectively improve the problem of the fluctuation of the annealing furnace strip tension during the production process.