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A weighted SVM ensemble predictor based on AdaBoost for blast furnace Ironmaking process

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Abstract

As one of the most complex industrial reactors, there remain some urgent issues for blast furnace (BF), such as BF automation, prediction of the inner thermal state, etc. In this work, the prediction of BF inner thermal state, which is represented by the silicon content in BF hot metal, is taken as an imbalanced binary classification problem and a Weighted Support Vector Machine (W-SVM) ensemble predictor based on AdaBoost is presented for the prediction task. Compared with the traditional W-SVM algorithm, the proposed predictor dynamically adjusts the weight distribution of training samples according to the performance of weak classifier, in this way to mine information lurked in the samples. The prediction can act as a guide to aid the operators for judging the thermal state of BF in time. Experiments results on five benchmark datasets and two real-world BFs datasets demonstrate the efficiency of the proposed W-SVM ensemble predictor.

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Acknowledgements

This research is partially supported by Natural Science Foundation of China under Grant Nos. 61973145 and 61873279, Foundation of the Education of Jiangxi Province under Grant No. GJJ180247, National Key Research and Development Program of Shandong Province under Grant No. 2018GSF120020 and Fundamental Research Funds for the Central Universities under Grant No. 19CX05027B.

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Authors and Affiliations

  1. School of Statistics, Jiangxi University of Finance & Economics, Nanchang, 330013, China

    Shihua Luo, Zian Dai & Tianxin Chen

  2. College of Science, China University of Petroleum, Qingdao, 266580, China

    Hongyi Chen

  3. School of Economics and Management & Institute for Energy Economics and Policy, China University of Petroleum, Qingdao, 266580, China

    Ling Jian

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  1. Shihua Luo
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  2. Zian Dai
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  4. Hongyi Chen
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  5. Ling Jian
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Correspondence to Ling Jian.

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Luo, S., Dai, Z., Chen, T. et al. A weighted SVM ensemble predictor based on AdaBoost for blast furnace Ironmaking process. Appl Intell 50, 1997–2008 (2020). https://doi.org/10.1007/s10489-020-01662-y

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