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Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

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Abstract

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. 61273260), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20121333120010), Natural Scientific Research Foundation of the Higher Education Institutions of Hebei Province (No. 2010165), the Major Program of the National Natural Science Foundation of China (No. 61290322) and Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China (No. SCIP2012008).

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Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Xia Xu, Changchun Hua, Yinggan Tang & Xinping Guan

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  1. Xia Xu
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  2. Changchun Hua
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  3. Yinggan Tang
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  4. Xinping Guan
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Correspondence to Xia Xu.

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Xu, X., Hua, C., Tang, Y. et al. Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer. Neural Comput & Applic 27, 1451–1461 (2016). https://doi.org/10.1007/s00521-015-1951-7

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  • DOI: https://doi.org/10.1007/s00521-015-1951-7

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