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• W4309022865 abstract "• A hybrid kinetic modeling framework is proposed for Fischer-Tropsch synthesis. • Anderson-Shulz-Flory (ASF) distribution is modeled by piecewise linear approximation. • Sparse regression is used to identify optimal model for chain growth probability. Fischer-Tropsch synthesis (FTS) receives an extensive attention as it can be used to produce various chemicals and fuels, such as linear alpha olefin, gasoline and jet fuel, in a sustainable way. While a kinetic model can help optimize the operating conditions of FTS reactors for a specific product portfolio, such a model is very challenging to develop due to the large number of species and reactions involved in FTS. To this end, in this work, we propose a hybrid modeling framework to efficiently build a kinetic model for FTS. Specifically, experiments are conducted using a Fe-Cu-K-SiO 2 catalyst with the following operating variables: pressure, temperature, H 2 /CO ratio in syngas, and gas hourly space velocity. Then, using the experimental data, the effectiveness of the proposed framework is illustrated, which consists of three key components. The overall LHHW model is first used to predict the overall consumption rates of CO and H 2 as well as the production rates of CO 2 and overall hydrocarbons. Then, a convex piecewise linear fitting problem is formulated for the ASF distribution model, which can identify the break points (where the value of chain growth probability α changes) with global optimality. Finally, surrogate modeling is performed to obtain the models describing the changes in the optimal α values with respect to the operating conditions. The final model showed the overall relative error of 9.98% for CO, CO 2 and H 2 , and 15.8% for hydrocarbons, which are comparable to the values reported in the literature." @default.
• W4309022865 created "2022-11-21" @default.
• W4309022865 creator A5007226887 @default.
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• W4309022865 date "2023-02-01" @default.
• W4309022865 modified "2023-09-29" @default.
• W4309022865 title "A hybrid modeling framework for efficient development of Fischer-Tropsch kinetic models" @default.
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• W4309022865 doi "https://doi.org/10.1016/j.jiec.2022.11.016" @default.
• W4309022865 hasPublicationYear "2023" @default.
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