Research And Application Of Furnace Temperature Advanced Control System For Regenerative Furnace

The heating furnace,a large energy consumer,is an important heating device used before billet rolling by steel companies.Its combustion control system features complexity.In addition,its furnace temperature object is characterized by large lag and strong inertia and so on.Therefore,studying advanced strategy for controlling temperature of heating furnace can greatly improve the control quality of the heating furnace and reduce energy consumption of steel companies.This dissertation studies the regenerative walking-beam furnace in Yangzhou City,Jiangsu Province.Based on the existing hardware of the control system at the production site,the advanced control system for heating furnace is designed and and put into use.The operation results show that the control system can reduce oxidation loss of billets and fuel consumption.Based on the analysis of the operation of the advanced control system at the production site,a new strategy for controlling furnace temperature based on wavelet neural network is proposed in this dissertation.The main contents of this dissertation are as follows:Firstly,analyzing the heating furnace temperature object,studying the relationship between furnace temperature,gas flow and air flow,building a furnace temperature-flow ratio control system,and designing a generalized predictive controller of furnace temperature,designing a PID controller loop for smoke temperature,a hardware platform for the advanced control system,and an operation interface for the advanced control system.Secondly,optimizing the advanced control system to solve problems in the process of project implementation,in which the measures include improving commutation process control and applying the multi-model control,thus making the heating furnace have better control quality in each load section and boast the controller's anti-saturation treatment.Through the adjustments of parameters of each control loop,the advanced control system is put into use.The operation results show that this advanced control system not only improves the product quality,but also reduces the oxidation loss of billets,and promotes profits of enterprises.Thirdly,since a heating furnace temperature object features uncertainty and non-linearity,and the neural network enjoys better approximation capability of nonlinear system,wavelet neural network is proposed to establish a temperature prediction model for a heating furnace so as to predict the future output value of the furnace temperature.Beside,a furnace temperature prediction controller is built according to the quadratic performance index,and the parameters of the neural network is corrected through a rolling optimization controller to obtain the future control quantity of the system.The simulation results show that this algorithm features good tracking ability and short adjustment period.