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A Hybrid Intelligent Soft-Sensor Model for Dynamic Particle Size Estimation in Grinding Circuits

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

The purpose of this paper is to develop an on-line soft-sensor for dynamic estimation of the particle size distribution of hydrocyclones overflow in consecutive grinding process. The hybrid model based soft-sensor is based on the following model structures: 1. a neural net-based dynamic model of state space description for hydrocyclone with a neural net-based model for classifier and a population balance model for ball mill and sump, 2. an ANFIS-based model mainly for abnormal operating conditions, 3. a fuzzy logic coordinator for the final predictive result according to output values of aforementioned models. The fact that the soft-sensor performs well in particle size estimation demonstrates that the proposed hybrid intelligent soft-sensor model is effective for dynamic estimation of particle size distribution.

This paper is supported by Chinese National Hi-Tech Development Program (2004AA412030).

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Author information

Authors and Affiliations

  1. Research Centre of Automation, Northeastern University, Shenyang, Liaoning, 110004, China

    Ming Tie, Heng Yue & Tianyou Chai

Authors
  1. Ming Tie
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  2. Heng Yue
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  3. Tianyou Chai
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Tie, M., Yue, H., Chai, T. (2005). A Hybrid Intelligent Soft-Sensor Model for Dynamic Particle Size Estimation in Grinding Circuits. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_138

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  • DOI: https://doi.org/10.1007/11427469_138

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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