Self-learning factor prediction of the heat transfer coefficient based on a dynamic fuzzy neural network for ultra-fast cooling

¹ Electronic Information and Engineering College, Taiyuan University of Science and Technology, Shanxi, Taiyuan, P.R. China
e-mail: xiaoxiaoshine@126.com
² State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, Liaoning, P.R. China

Received: 8 June 2014
Accepted: 3 February 2015

Abstract

The aim of this paper is to improve the plate mechanical properties by enhancing the control accuracy of the finish cooling temperature (FCT) during the ultra-fast cooling (UFC) process. In the mathematical model of ultra-fast cooling, the self-learning factor of the heat transfer coefficient is one of the important technological parameters for the finish cooling temperature. Some parameters can influence the self-learning factor, such as plate thickness, water flow and water temperature. In order to predict the self-learning factor of the heat transfer coefficient through these parameters, a dynamic fuzzy neural network (D-FNN) is introduced and combined with a traditional mathematical model. After training and prediction, it is shown that the D-FNN model has high prediction accuracy and can achieve predictive control of the mathematical model. Testing the BP neural network with the same data, the prediction accuracy of the D-FNN is higher than the BP neural network. In industrial production, FCT errors demonstrate that satisfactory performance can be achieved by the D-FNN.