Prediction Model of Silicon Content in Blast Furnace Hot Metal Based on Artificial Neural Network and Genetic Algorithm

Abstract:

In this paper, a predicting model of silicon content in blast furnace hot metal is built based on artificial neural network and genetic algorithm, which optimizes the initialization weight values of neural network with genetic algorithm. This model can effectively improve the prediction accuracy and reduce the calculating time. Online application shows that the predicting model can effectively predict the silicon content in blast furnace hot metal and play an important role in production. when required absolute error was within ±0.03, the accuracy of model can reach 81.4%, and when absolute error was within ±0.04, the accuracy can reach 91.4%.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3505-3508
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3505
• Online since: July 2014
• Authors: Yong Hua Cheng*, Cai Wen Niu, Yu Xin Zhang
• Keywords: Artificial Neural Network (ANN), Blast Furnace, Genetic Algorithm (GA), Hot Metal, Silicon Content

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