Abstract: 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.

Key words: coiling temperature, adaptive method, heat transfer, phase transformation, hot-rolled strip