Predictive Control On The Temperature Of Multi-purpose Heat Treatment Furnace Based On BP Neural Network

In recent years, with the improvement of industrial control technology, the rapid development of aerospace technology, wind power, transportation, machinery and other industries, the work piece precision heat treatment has been increasingly demanding. Heat treatment to a certain extent is a direct reflection of China’s industrial level. Temperature, carbon potential, nitrogen potential are important indicators of the impact of heat treatment quality product. Advanced Control Systems of heat treatment furnaces, controlled atmosphere heat treatment, Vacuum heat treatment has been widespread concern and have rapid development. In the heat treatment process, there are various parameters to achieve precise control, that the work piece is no oxidation, no decarburization, better surface quality, less distortion. Heat treatment furnace is a complex controlled object, there is a nonlinear, time-varying, time delay factor random interference factors and uncertainties, thus severely limiting control method based on precise mathematical model, the traditional PID control is difficult to meet the temperature control accuracy and temperature real-time requirements.Under such background, This article study the optimization of the heat treatment temperature control methods. And take the controlled atmosphere multi-purpose heat treatment furnace for the object, for its multi-variable, nonlinear, large inertia and time delay characteristics, combined with neural networks and predictive control algorithm. Give a predictive control algorithm based on BP neural network. To establish a multi-purpose Furnace prediction model and on the basis of the model of BP algorithm to optimize the use of field data for model training, in order to pre dict the furnace output. Combined with predictive control algorithm, using the feedback correction model to overcome the interference caused by other system error, and then scroll through the optimization of the control amount to obtain optimal control sequence.By MATLAB software simulation, verification forecast model output, control error, verify the effectiveness of the model. Adjust the main parameters of predictive control algorithm, select the optimal control parameters, has made fast response, basically there is no overshoot. The application of this model to a company on the 1st multi-purpose furnace temperature control system, and has achieved good practical effect.