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Multi-model Switching Method Based on Sphere-Based SVM Classifier Selector and Its Application to Hydrogen Purity Multi-model Soft Sensor Modeling in Continuous Catalytic Reforming

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Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems (AsiaSim 2016, SCS AutumnSim 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 646))

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Abstract

The process of continuous catalytic reforming is complex and changeable. Usually, a single model soft sensor is hardly to grantee the accuracy of the prediction result, so it is necessary to adopt the multi-model strategy to improve the model performance. The process of sub model combination of the multi-model soft senor could be considered as a multi-class classification issue. The main idea of the proposed method in this paper aims to solve this issue with Support Vector Machine (SVM). The proposed approach is to build a sphere structure to cover the same-class samples as much as possible, and these sphere-based structure can be considered as a selector of those SVM classifiers. Experimental results show that the proposed method is suitable for particular use in SVM multi-class classification, and the switched-based multi-model soft sensor for hydrogen purity in continuous catalytic reforming based on the proposed method has a higher prediction accuracy.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61573144, 61174040), Shanghai Commission of Science and Technology (Grant no. 12JC1403400), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China

    Yi-Fan Shuang & Xing-Sheng Gu

Authors
  1. Yi-Fan Shuang
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  2. Xing-Sheng Gu
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Correspondence to Xing-Sheng Gu .

Editor information

Editors and Affiliations

  1. Beihang University , Beijing, China

    Lin Zhang

  2. Beihang University , Beijing, China

    Xiao Song

  3. Beihang University , Beijing, China

    Yunjie Wu

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Shuang, YF., Gu, XS. (2016). Multi-model Switching Method Based on Sphere-Based SVM Classifier Selector and Its Application to Hydrogen Purity Multi-model Soft Sensor Modeling in Continuous Catalytic Reforming. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 646. Springer, Singapore. https://doi.org/10.1007/978-981-10-2672-0_7

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  • DOI: https://doi.org/10.1007/978-981-10-2672-0_7

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  • Print ISBN: 978-981-10-2671-3

  • Online ISBN: 978-981-10-2672-0

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