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A modified ELM algorithm for the prediction of silicon content in hot metal

  • Extreme Learning Machine and Applications
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Abstract

Silicon content in hot metal in the iron-making process has been used as a major indicator of the internal thermal state in blast furnace for many years. Due to the harsh environment in the blast furnace, how to measure the silicon content in hot metal is quite difficult. Many efforts have been made on the estimation of the silicon content in hot metal. In this paper, a soft-sensing modeling method based on a modified extreme learning machine is proposed to tackle the problem. In this approach, a modified pruning algorithm is utilized to optimize the weights which are generated randomly in the original ELM algorithm. The real data collected from a blast furnace in the factory are applied and tested by the proposed algorithm, and the results show that the proposed prediction model has less error than the other algorithm such as BP algorithm and support vector method.

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Acknowledgments

This work has been supported by the National Natural Science Foundation of China (NSFC Grant No. 61333002) and Beijing Natural Science Foundation (Grant No. 4132065).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Yongliang Yang, Sen Zhang & Yixin Yin

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  1. Yongliang Yang
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  2. Sen Zhang
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  3. Yixin Yin
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Correspondence to Sen Zhang.

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Yang, Y., Zhang, S. & Yin, Y. A modified ELM algorithm for the prediction of silicon content in hot metal. Neural Comput & Applic 27, 241–247 (2016). https://doi.org/10.1007/s00521-014-1775-x

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  • DOI: https://doi.org/10.1007/s00521-014-1775-x

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