Laminar Cooling Control System Design And Simulation

Strip laminar cooling control system is an important process in a hot rolling production line. Left to be completed strip finishing mill, after laminar cooling system to achieve its objectives coiling temperature requirements, the implementation of the control model in laminar cooling system, it must reach the speed and finishing speed coiling at the request of the production chain coordination basis. Properly speaking, the water district cooling water of the formula can only think it is an ideal circumstances static mathematical model. In the actual control, a valve opened calculated value is not immediately open the corresponding number of the cooling water. The rolling speed changes, so that the output roll Strip movement is over the speed of process. Therefore, dynamic set, the dynamic tracking, and dynamic control must be resolved,considering the complexity of the actual working conditions under the precise control panel with the point of coiling temperature.In a hot steel strip production line, the coiling temperature control is critical for strip quality. In this paper, the coiling temperature control of a typical steel strip mill is investigated. Through the research of laminar cooling control system for cooling process, in accordance with domestic and international application of the characteristics of some mathematical model, the first-order model of Baosteel 2050 was derived. A simplified dynamic model is introduced, based on which a cooling control scheme with combined feedforward, feedback and adaptive algorithms is developed.It is quite difficult to model accurately the performance of coiling temperature of hot rolled strip using classical modeling techniques that deal with the solution of complex differential equations. Moreover, the mathematical models require a large number of geometrical parameters to define the system, which may not be readily available. As an alternative, model error can be modeled using an artificial neural network approach with greatly improving the coiling temperature accuracy. By using CGBP (conjugate gradient backpropagation ) neural network , the comprehensive model factor of mathematical model in hot rolled strip was predicted , and the results were applied to calculation of mathematical model of coiling temperature with a good prediction of coiling temperature. The method obviously improves the accuracy of coiling temperature with a good practical application. The CGBP neural networks model is trained and tested by MATLAB. A dynamic model and CGBP neural network are used for control of coiling temperature. Combination of analysis for model parameters and neural network for predicting the error of mathematical model was conducted successfully. Off-line simulation results and on-line application in hot strip mill verify the effectiveness of the proposed method.Simulations with a model validated using actual plant data are conducted, and the results have confirmed the effectiveness of the proposed control method.