A hybrid modeling framework for efficient development of Fischer-Tropsch kinetic models

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-SiO2 catalyst with the following operating variables: pressure, temperature, H2/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 H2 as well as the production rates of CO2 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 a changes) with global optimality. Finally, surrogate modeling is performed to obtain the models describing the changes in the optimal a values with respect to the oper-ating conditions. The final model showed the overall relative error of 9.98% for CO, CO2 and H2, and 15.8% for hydrocarbons, which are comparable to the values reported in the literature.(c) 2022 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.

Automated summary

Fischer-Tropsch synthesis (FTS) is a sustainable method for producing various chemicals and fuels, and this study proposes a hybrid modeling framework to efficiently build a kinetic model for FTS. The framework is validated using experimental data, and it shows promising results in predicting the consumption and production rates as well as the optimal operating conditions for FTS.