Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion
Abstract
:1. Introduction
2. Methodology
2.1. Process Kinetics Simulation
2.2. Variables Pattern Recognition via SOM
2.2.1. Data Pre- and Post-Processing
2.2.2. Lattice Structure, Map Shape, and Size
2.2.3. SOM Initialization and Training
3. Results
3.1. SOM Architecture
3.2. Syngas Combustion Variables Pattern
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
ANN | artificial neural network |
chemical species concentration, mol/m3 | |
rate of generation (+) or consumption (−) of in reaction step , mol/m3/s | |
reaction constant parameter in reaction | |
reaction temperature, degree K | |
gas-phase mole fraction of chemical species at reactor exhaust, % | |
gas-phase mole fraction of chemical species in the syngas feed, % | |
BMU | best-matching unit neuron in the SOM grid |
SOM input dataset of dimension × | |
th feature variable of the SOM input dataset , | |
th observation vector in the SOM input dataset , | |
weight vector of th neuron in the SOM grid | |
PFR | plug-flow reactor |
SOM | self-organizing map |
Te | topographic error of trained SOM |
Qe | quantization error of trained SOM |
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Reaction | Rate Expression | Reaction Constant Parameter | |
---|---|---|---|
1 | |||
2 | |||
3 |
Variables: | (%) | (%) | (%) | (%) | (%) | (%) | (K) |
Minimum: | 4.65 | 12.03 | 0.00 | 0.84 | 0.11 | 0.05 | 1000 |
Maximum: | 59.30 | 67.59 | 15.10 | 38.95 | 54.26 | 43.43 | 1500 |
Median: | 25.55 | 28.02 | 2.93 | 11.24 | 18.10 | 14.62 | 1263 |
Average: | 24.91 | 28.66 | 2.98 | 11.33 | 17.75 | 14.38 | 1256 |
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Fortela, D.L.B.; Crawford, M.; DeLattre, A.; Kowalski, S.; Lissard, M.; Fremin, A.; Sharp, W.; Revellame, E.; Hernandez, R.; Zappi, M. Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion. Clean Technol. 2020, 2, 156-169. https://doi.org/10.3390/cleantechnol2020011
Fortela DLB, Crawford M, DeLattre A, Kowalski S, Lissard M, Fremin A, Sharp W, Revellame E, Hernandez R, Zappi M. Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion. Clean Technologies. 2020; 2(2):156-169. https://doi.org/10.3390/cleantechnol2020011
Chicago/Turabian StyleFortela, Dhan Lord B., Matthew Crawford, Alyssa DeLattre, Spencer Kowalski, Mary Lissard, Ashton Fremin, Wayne Sharp, Emmanuel Revellame, Rafael Hernandez, and Mark Zappi. 2020. "Using Self-Organizing Maps to Elucidate Patterns among Variables in Simulated Syngas Combustion" Clean Technologies 2, no. 2: 156-169. https://doi.org/10.3390/cleantechnol2020011