Control of Heat Transfer in Continuous Casting Process Using Neural Networks
利用神经网络控制连续浇铸过程中的热传导

In continuous casting,the cooling-solidification pro- cess must be based on the adaptation of heat transfer,which is directly connected to casting conditions such as casting speed, casting temperature,and cooling parameters.Most control schemes are based on the static relation between casting speed and water flow rate in each cooling zone;this constitutes an open loop that does not consider the dynamic surface tempera- ture,which is an important parameter for the final slab quality. In steelmaking,the casting-speed changes affect the global heat transfer.An optimal operation requires an adjustment of the process control variables,i.e.,global heat transfer.A learning neural network(NN)allows the identification and the control of a nonlinear heat transfer model in the continuous casting pro- cess.A heat transfer model was developed using the dynamic heat balance.A comparison between the experimental open loop results and those of the model simulation is considered.Follow- ing adaptation,the model is used for controlling the slab sur- face temperature in closed loop,using NN technology and PID controllers.The NN identification and control strategy gives a stable temperature closed loop control comparatively to the conventional PID.