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

Dhan Lord B. Fortela, Matthew Crawford, Alyssa DeLattre, Spencer Kowalski, Mary Lissard, Ashton Fremin, Wayne Sharp, Emmanuel Revellame, Rafael Hernandez and Mark Zappi
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Dhan Lord B. Fortela: Department of Chemical Engineering, 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
Emmanuel Revellame: Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
Rafael Hernandez: Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA
Mark Zappi: Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA

Clean Technol., 2020, vol. 2, issue 2, 1-14

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.

Keywords: chemical process simulation; syngas; machine learning; SOM (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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