Prediction of the hot metal silicon content in blast furnace based on extreme learning machine
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Silicon content in hot metal is an important indicator for the thermal condition inside the blast furnace in the iron-making process. The operators often refer the silicon content and its change trend for the guidance of next production. In this paper, we establish the neural network model for the prediction of silicon content in hot metal based on extreme learning machine (ELM) algorithm. Considering the imbalanced operating data, weighted ELM (W-ELM) algorithm is employed to make prediction for the change trend of silicon content. The outliers hidden in the real production data often tend to undermine the accuracy of prediction model. First, an outlier detection method based on W-ELM model is proposed from a statistical view. Then we modified the ordinary ELM and W-ELM algorithms in order to reduce the interference of outliers, and proposed two enhanced ELM frameworks respectively for regression and classification applications. In the simulation part, the real operating data is employed to verify the better performance of the proposed algorithm.
KeywordsExtreme learning machine Blast furnace Silicon content Outlier detection
This work has been supported by the National Natural Science Foundation of China (NSFC Grant No. 61333002, No. 61673056 and No. 61671054).
- 16.Zheng DL, Liang RX, Zhou Y, Wang Y (2003) A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal, J Univ Sci Technol Beijing 10:68Google Scholar
- 23.Zhou XR (2014) Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism, Mathematical Problems in Engineering, Article ID 938548.Google Scholar
- 25.Fletcher R (1981) Practical methods of optimization: vol. 2 constrained optimization. New YorkGoogle Scholar
- 27.Sohn BY (1994), Weighted least squares regression diagnostics and its application to robust regression, Doctoral Thesis. Dept. of Statistics Korea University, SeoulGoogle Scholar
- 28.Limo K (1996) Robust error measure for supervised neural network learning with outliers. IEEE Trans Neural Netw 7:247–250Google Scholar
- 33.Bache K, Lichman M (2013) UCI Machine Learning Repository, (http://archive.ics.uci. edu/ml), School of Information and Computer Sciences, University of California, Irvine