基于数据特征的加热炉钢温预报模型.

Considering that the industrial process of reheating furnace is with the characteristics of complexity， non-linearity and time delay， and the prediction of billet temperature is difficult to achieve， an improved principal component regression(PCR)and prediction method based on data features is proposed in this paper. The time-delay among different variables is solved at first by synchronization of the original data. Then statistic and entropy features are extracted from each billet of reheating furnace and these data features consist of a data feature vector orderly. Lastly， the prediction model between billet outlet temperature and data features of process variable is established by PCR. The proposed method is applied in the reheating furnace of a real steel factory， and the model parameter is reckoned based on actual operational data. The experiment results and error analysis indicate that this model is able to predict the billet steel outlet temperature， and the prediction error can satisfy the demands of industrial application. [ABSTRACT FROM AUTHOR]