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Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach

Received: 23 July 2017     Accepted: 21 August 2017     Published: 5 November 2017
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Abstract

An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a Takagi-Sugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.

Published in American Journal of Chemical Engineering (Volume 5, Issue 6)
DOI 10.11648/j.ajche.20170506.11
Page(s) 122-129
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Inferential Sensor, Distillation Column, Takagi-Sugeno-Kang Fuzzy System, Nearest Neighborhood Clustering, Particle Swarm Optimization, Wavelet, Principal Component Analysis

References
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Cite This Article
  • APA Style

    Babak Ghanaati, Mehdi Shahbazian. (2017). Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach. American Journal of Chemical Engineering, 5(6), 122-129. https://doi.org/10.11648/j.ajche.20170506.11

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    ACS Style

    Babak Ghanaati; Mehdi Shahbazian. Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach. Am. J. Chem. Eng. 2017, 5(6), 122-129. doi: 10.11648/j.ajche.20170506.11

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    AMA Style

    Babak Ghanaati, Mehdi Shahbazian. Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach. Am J Chem Eng. 2017;5(6):122-129. doi: 10.11648/j.ajche.20170506.11

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  • @article{10.11648/j.ajche.20170506.11,
      author = {Babak Ghanaati and Mehdi Shahbazian},
      title = {Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach},
      journal = {American Journal of Chemical Engineering},
      volume = {5},
      number = {6},
      pages = {122-129},
      doi = {10.11648/j.ajche.20170506.11},
      url = {https://doi.org/10.11648/j.ajche.20170506.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.20170506.11},
      abstract = {An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a Takagi-Sugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.},
     year = {2017}
    }
    

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    T1  - Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach
    AU  - Babak Ghanaati
    AU  - Mehdi Shahbazian
    Y1  - 2017/11/05
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajche.20170506.11
    DO  - 10.11648/j.ajche.20170506.11
    T2  - American Journal of Chemical Engineering
    JF  - American Journal of Chemical Engineering
    JO  - American Journal of Chemical Engineering
    SP  - 122
    EP  - 129
    PB  - Science Publishing Group
    SN  - 2330-8613
    UR  - https://doi.org/10.11648/j.ajche.20170506.11
    AB  - An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a Takagi-Sugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.
    VL  - 5
    IS  - 6
    ER  - 

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Author Information
  • Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

  • Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

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