Application of Genetic Algorithm Combined with BP Neural Network in Soft Sensor of Molten Steel Temperature

2006 ◽  
Author(s):  
Huixin Tian ◽  
Zhizhong Mao ◽  
Shu Wang ◽  
Kun Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Molten Steel ◽  
Bp Neural Network ◽  
Soft Sensor ◽  
Steel Temperature ◽  
Molten Steel Temperature

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.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

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