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A Soft Sensing Scheme of Gas Utilization Ratio Prediction for Blast Furnace Via Improved Extreme Learning Machine

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Abstract

Gas utilization ratio (GUR) is an important indicator reflecting the operating state and energy consumption of blast furnace (BF). Skilled operators usually refer to changing trends of GUR to guide the next step of production. For these reasons, this paper establishes a soft sensing scheme based on an improved extreme learning machine (ELM) to predict GUR. In order to enhance the modeling capability of ELM for industrial data, an improved ELM, named GR-ELM, is proposed based on grey relational analysis (GRA) and residual modification mechanism. In GR-ELM, considering the different effective information contained in each input attribute for modeling, the input attribute optimization is proposed combining with GRA and entropy weight method. Then, because the modeling capability of ELM is limited and the data collected from industrial process are usually contaminated, the residual modification mechanism is implemented to improve the reliability of the model. In addition, considering the influence of time delay in BF ironmaking process, generalized correlation coefficient method based on mutual information is used for time delay analysis to eliminate the influence. The real data collected from a BF are applied and validated the performance and effectiveness of the proposed soft sensing scheme. The experimental results show that the proposed soft sensing scheme is available and can achieve better performance than some state-of-the-art algorithms. The soft sensing scheme can provide effective decision support and guidance for further optimization operation.

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Acknowledgements

This work has been supported by the National Natural Science Foundation of China (NSFC Grants Nos. 61333002 and 61673056), Beijing Natural Science Foundation (No. 4182039) and Beijing Key Subject Construction Projects (No. XK100080573).

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Authors and Affiliations

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

    Yanjiao Li, Sen Zhang, Yixin Yin, Jie Zhang & Wendong Xiao

  2. Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing, 100083, China

    Yanjiao Li, Sen Zhang, Yixin Yin, Jie Zhang & Wendong Xiao

Authors
  1. Yanjiao Li
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  2. Sen Zhang
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  3. Yixin Yin
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  4. Jie Zhang
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  5. Wendong Xiao
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Correspondence to Sen Zhang.

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Li, Y., Zhang, S., Yin, Y. et al. A Soft Sensing Scheme of Gas Utilization Ratio Prediction for Blast Furnace Via Improved Extreme Learning Machine. Neural Process Lett 50, 1191–1213 (2019). https://doi.org/10.1007/s11063-018-9888-3

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