Hybrid Model of Molten Steel Temperature Prediction Based on Ladle Heat Status and Artificial Neural Network

2014 ◽  
Vol 21 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Fei He ◽  
Dong-feng He ◽  
An-jun Xu ◽  
Hong-bing Wang ◽  
Ni-yuan Tian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Molten Steel ◽  
Temperature Prediction ◽  
Steel Temperature ◽  
Molten Steel Temperature ◽  
Artificial Neural

Temperature Prediction Model for Molten Steel in RH Refining Process

2013 ◽  
Vol 712-715 ◽  
pp. 22-25 ◽  
Author(s):  
Tia Xia ◽  
Zhu He
Keyword(s):  
Mathematical Model ◽  
Prediction Model ◽  
Molten Steel ◽  
Initial Temperature ◽  
Refining Process ◽  
Temperature Prediction ◽  
Steel Temperature ◽  
Molten Steel Temperature

A mathematical model for the RH refining process was developed and validated by the measured molten steel temperature in situ. It is showed that the model predicted temperature matched the measured value well and the average errors within ±5°C were 86.9%. The model results also showed that for every increase of 100°C of the initial temperature of the chamber inwall , the average molten steel temperature increased by about 8°C. For every blowing extra 50m3 oxygen, the steel temperature increased by about 7°C.

MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

2013 ◽  
Vol 02 (01) ◽  
pp. 1350004 ◽  
Author(s):  
S. RATH ◽  
P. P. SENGUPTA ◽  
A. P. SINGH ◽  
A. K. MARIK ◽  
P. TALUKDAR
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Data Driven ◽  
Roll Force ◽  
Hot Strip ◽  
Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

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