|
|
|
| Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network |
| Fei HE1,2,Dong-feng HE1,2,An-jun XU1,2,Hong-bing WANG3,Nai-yuan TIAN1,2 |
1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
2. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
3. School of Computer and Communication Engineering,University of Science and Technology Beijing, Beijing 100083, China |
|
|
|
|
Abstract Aiming at the characteristics of the practical steelmaking process, a hybrid model based on ladle heat status and artificial neural network has been proposed to predict molten steel temperature. The hybrid model could overcome the difficulty of accurate prediction using a single mathematical model, and solve the problem of lacking the consideration of the influence of ladle heat status on the steel temperature in an intelligent model. By using the hybrid model method, forward and backward prediction models for molten steel temperature in steelmaking process are established and are used in a steelmaking plant. The forward model, starting from the end-point of BOF, predicts the temperature in argon-blowing station, starting temperature in LF, end temperature in LF and tundish temperature forwards, with the production process evolving. The backward model, starting from the required tundish temperature, calculates target end temperature in LF, target starting temperature in LF, target temperature in argon-blowing station and target BOF end-point temperature backwards. Actual application results show that the models have better prediction accuracy and are satisfying for the process of practical production.
|
|
Received: 30 October 2012
Published: 20 February 2014
|
|
Corresponding Authors:
Fei HE
E-mail: hf2573546@sina.com
|
|
|
|
| [1] |
ZHAO Lu-peng��WU Keng��ZHU Li����CHEN Xiao-min��QIN Xuan-ke. Prediction model of sinter properties based on BP neural network[J]. Chinese Journal of Iron and Steel, 2017, 52(9): 11-15. |
| [2] |
LIANG Xin-teng����SUN Yan-hui��ZENG Jian-hua��CHEN Jun��CHEN Lu. Theoretical?research?and application of less slag steelmaking of semi-steel in BOF of Pansteel[J]. Chinese Journal of Iron and Steel, 2017, 52(7): 47-51. |
| [3] |
CHAI Xiao-jun��LI Hong-bo��ZHANG Jie��ZHOU Yi-zhong�� MA Heng-hao��ZHANG Peng-wu. Transverse distribution law of rolling force and its prediction model in hot rolling[J]. Chinese Journal of Iron and Steel, 2017, 52(6): 52-60. |
| [4] |
GU Mao-qiang,XU An-jun,HE Dong-feng,WANG Hong-bing,FENG Kai. Online management and control model of molten steel temperature based on case-based reasoning[J]. Chinese Journal of Iron and Steel, 2017, 29(6): 468-473. |
| [5] |
BI Zhi-min,WANG Yan. Method of flatness pattern recognition based on improved genetic algorithm optimization Elman neural network[J]. Chinese Journal of Iron and Steel, 2017, 29(4): 305-311. |
| [6] |
TIAN Hui-xin,,LIU Yu-dong,,MENG Bo. Analysis of temperature prediction of molten steel for LF based on AdaBoost.RS[J]. Chinese Journal of Iron and Steel, 2017, 29(2): 98-104. |
|
|
|
|
2014, Vol. 21 
