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Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion

Author

Listed:
  • Dhan Lord B. Fortela

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA
    Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA)

  • Matthew Crawford

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Alyssa DeLattre

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Spencer Kowalski

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Mary Lissard

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Ashton Fremin

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Wayne Sharp

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Civil Engineering, University of Louisiana, Lafayette, LA 70504, USA)

  • Emmanuel Revellame

    (Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
    Department of Industrial Technology, University of Louisiana, Lafayette, LA 70504, USA)

  • Rafael Hernandez

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA
    Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA)

  • Mark Zappi

    (Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA
    Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA)

Abstract

This study focused on demonstrating the use of a self-organizing map (SOM) algorithm to elucidate patterns among variables in simulated syngas combustion. The work was implemented in two stages: (1) modelling and simulation of syngas combustion under various feed composition and reactor temperature implemented in AspenPlus TM chemical process simulation software, and (2) pattern recognition among variables using SOM algorithm implemented in MATLAB. The varied levels of feed syngas composition and reactor temperature was randomly sampled from uniform distributions using the Morris screening technique creating four thousand eight hundred simulation conditions implemented in the process simulation which consequently produced a multivariate dataset used in the SOM analysis. Results show that cylindrical SOM topology models the dataset at lower quantization error and topographic error as compared to the rectangular SOM topology indicating suitability of the former for variables pattern elucidation for the simulated combustion. Nonetheless, the variables pattern between component planes from rectangular SOM (9 × 28 grid) and those from cylindrical SOM (9 × 28 grid) are almost similar, indicating that either rectangular or cylindrical architectures may be used for variables pattern analysis. The component planes of process variables from trained SOM are a convenient visualization of the trends across all process variables.

Suggested Citation

  • Dhan Lord B. Fortela & Matthew Crawford & Alyssa DeLattre & Spencer Kowalski & Mary Lissard & Ashton Fremin & Wayne Sharp & Emmanuel Revellame & Rafael Hernandez & Mark Zappi, 2020. "Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion," Clean Technol., MDPI, vol. 2(2), pages 1-14, April.
    • Handle: RePEc:gam:jcltec:v:2:y:2020:i:2:p:11-169:d:351287
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    References listed on IDEAS

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    1. Thiago Gomes Leal Ganhadeiro & Eliane Da Silva Christo & Lidia Angulo Meza & Kelly Alonso Costa & Danilo Pinto Moreira de Souza, 2018. "Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps," Energies, MDPI, vol. 11(10), pages 1-14, October.
    2. Kangas, Jari & Kohonen, Teuvo, 1996. "Developments and applications of the self-organizing map and related algorithms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 41(1), pages 3-12.
    3. Navid Kousheshi & Mortaza Yari & Amin Paykani & Ali Saberi Mehr & German F. de la Fuente, 2020. "Effect of Syngas Composition on the Combustion and Emissions Characteristics of a Syngas/Diesel RCCI Engine," Energies, MDPI, vol. 13(1), pages 1-19, January.
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    Cited by:

    1. Dhan Lord B. Fortela & Ashton C. Fremin & Wayne Sharp & Ashley P. Mikolajczyk & Emmanuel Revellame & William Holmes & Rafael Hernandez & Mark Zappi, 2023. "Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles," Clean Technol., MDPI, vol. 5(1), pages 1-18, March.

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