Prediction Model of the Sinter Comprehensive Performance Based on Neural Network

Wei Chen; Hui Juan Zhang; Bao Xiang Wang; Ying Chen; Xing Li

doi:10.4028/www.scientific.net/AMR.753-755.62

Paper Titles

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Prediction Model of the Sinter Comprehensive...

Prediction Model of the Sinter Comprehensive Performance Based on Neural Network

Abstract:

The sinter quality and the stability of composition could directly affect the yield, quality and energy consumption of ironmaking production. It is important for iron and steel industry to steadily control sinter chemical composition and analyze sintering energy consumption. The MATLAB m file editor was used to write code directly in this paper. A predictive system for two important sinter chemical composition (TFe and FeO), sinter output and sintering solid fuel consumption of was established based on BP neural network, which was trained by actual production data.) The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

62-65

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.62

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Cite this paper

Online since:

August 2013

Authors:

Wei Chen, Hui Juan Zhang, Bao Xiang Wang, Ying Chen, Xing Li

Keywords:

Neural Network (NN), Prediction, Sinter Chemical Composition, Sinter Yield, Solid Fuel Consumptions

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