Effect of Process-Condition-Dependent Chain Growth Probability and Methane Formation on Modeling of the Fischer-Tropsch Process
Author(s)
Matsuka, Maki
Braddock, Roger D
Hanaoka, Toshiaki
Shimura, Katsuya
Miyazawa, Tomohisa
Hirata, Satoshi
Griffith University Author(s)
Year published
2016
Metadata
Show full item recordAbstract
The Fischer–Tropsch (FT) process can be described by the Anderson–Schulz–Flory model, although it does not handle the methane kinetics accurately. The value of chain growth probability (α) in the model is largely dependent upon the process conditions. The purpose of the research is to combine a CH4 kinetic model and process-condition-dependent chain growth probability α model and to calibrate the model parameters against experimental data from the literature. The combined model clearly improved the model predictions when compared to experimental data. Sensitivity analysis of the combined model showed the importance of ...
View more >The Fischer–Tropsch (FT) process can be described by the Anderson–Schulz–Flory model, although it does not handle the methane kinetics accurately. The value of chain growth probability (α) in the model is largely dependent upon the process conditions. The purpose of the research is to combine a CH4 kinetic model and process-condition-dependent chain growth probability α model and to calibrate the model parameters against experimental data from the literature. The combined model clearly improved the model predictions when compared to experimental data. Sensitivity analysis of the combined model showed the importance of adsorption coefficients to the outputs from the combined model. Testing the reactor temperature and feedstock composition shows that the outputs can be optimized, depending upon the length of carbon chains required in the output, and also suggested the importance of incorporating the effects of process conditions in the modeling of the FT product distribution.
View less >
View more >The Fischer–Tropsch (FT) process can be described by the Anderson–Schulz–Flory model, although it does not handle the methane kinetics accurately. The value of chain growth probability (α) in the model is largely dependent upon the process conditions. The purpose of the research is to combine a CH4 kinetic model and process-condition-dependent chain growth probability α model and to calibrate the model parameters against experimental data from the literature. The combined model clearly improved the model predictions when compared to experimental data. Sensitivity analysis of the combined model showed the importance of adsorption coefficients to the outputs from the combined model. Testing the reactor temperature and feedstock composition shows that the outputs can be optimized, depending upon the length of carbon chains required in the output, and also suggested the importance of incorporating the effects of process conditions in the modeling of the FT product distribution.
View less >
Journal Title
Energy and Fuels
Volume
30
Subject
Physical chemistry
Chemical engineering
Chemical and thermal processes in energy and combustion
Resources engineering and extractive metallurgy