传热与相变耦合的卷取温度模型自适应方法

为实现卷取温度模型水冷换热学习系数和奥氏体相变速率学习系数的在线实时滚动优化，采用数学方法对带钢段间温度自适应进行研究.首先，构建一个以带钢段初始学习系数为重心的等边三角形，基于各顶点对应的学习系数，分别利用带钢温度模型预报卷取温度，从而获得学习系数对卷取温度的一阶偏导数增益；接着，根据带钢段实测卷取温度与模型预报值的偏差计算学习系数增量部分的瞬时值，并依据学习速率进行学习计算、有效性检查和平滑处理.最后，将学习系数增量值应用于卷取温度动态设定模型，对冷却区内的所有带钢段的冷却规程进行更新.实际应用表明，卷取温度段间自适应方法能够快速响应轧制速度的变化，对卷取温度进行高精度控制.

Adaptive Method for Coiling Temperature Control Model Coupled with Heat Transfer and Phase Transformation

To realize the online real-time scrolling optimization of the heat transfer learning coefficient and phase transformation rate learning coefficient in a coiling temperature control(CTC) model， the coiling temperature adaptation between strip segments was studied. Firstly， an equilateral triangle of learning coefficient was built， where the initial learning coefficent adopted by strip segment was in its center of gravity. Based on the learning coefficient on the triangle’s vertices， the coiling temperatures were predicted by the strip temperature model and then the first-order partial derivative of each learning coefficient to coiling temperature can be also obtained. Secondly， the instantaneous value of incremental learning coefficient can be calculated on the basis of the computed partial derivative value and temperature deviation between the predicted temperature and the measured one. There after， the instantaneous values were learned according to the learning rate， followed by the data validation and smoothing. Finally， the new incremental learning coefficients were delivered to the CTC model to update the cooling schedule of each strip segment located in laminar cooling zone. The results in practice show that the adaptive learning method can respond quickly to the change of rolling speed and the coiling temperature along the strip can be controlled more accurately.