Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

ABSTRACT

Controlling the molten iron temperature plays an important role in the iron and steelmaking industry. The change of silicon content is adopted to reflect the temperature, however , the prediction of silicon content has been one of the hot and difficult problems. In this paper, a new model based on gray relational analysis (GRA) and extreme learning machine (ELM) is developed. Firstly, the GRA is used to get the high correlation indexes with the silicon content. Then the relevant indicators are taken as input and the silicon content is taken as output. The ELM model is constructed and the model is trained. Based on this, the silicon content is predicted. The results show that the hit rate reaches 87%(the error is less than 0.10). Compared with the traditional backpropagation or radial basis function neural network , this model has higher hit rate and faster running speed. 

KEYWORDS:

• Prediction of furnace temperature
• silicon content of molten iron
• blast furnace
• GRA
• ELM

Additional information

Funding

This work was financially supported by the Natural Science Foundation of China [grant number 51574103] and the Natural Science Foundation of Hebei province [grant number E2012209025].