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Kinetic Parameters Estimation via Dragonfly Algorithm (DA) and Comparison of Cylindrical and Spherical Reactors Performance for CO2 Hydrogenation to Hydrocarbons

Abstract

Climate change and global warming, as well as growing global demand for hydrocarbons in industrial sectors, make great incentives to investigate the utilization of CO2 for hydrocarbons production. Therefore, finding an in-depth understanding of the CO2 hydrogenation reactors along with simulating reactor responses to different operating conditions are of paramount importance. However, the reaction mechanisms for CO2 hydrogenation and their corresponding kinetic parameters have been disputable yet. In this regard, considering the previously proposed Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism, which considered CO2 hydrogenation as a combination of reverse water gas shift (RWGS) and Fischer-Tropsch (FT) reactions, and using a one-dimensional pseudo-homogeneous non-isothermal model, kinetic parameters of the rate expressions are estimated via fitting experimental and modelling data through a novel swarm intelligence optimization technique called dragonfly algorithm (DA). The predicted reactants conversion using DA algorithm are closer to the experimental data (with about 4% error) comparing to those obtained by the artificial bee colony (ABC) algorithm, and are in significant agreement with available literature data. The proposed model is used to assess the effect of reactor configuration on the performance and temperature fluctuations. Results show that axial flow spherical reactor (AFSR) and radial flow spherical reactor (RFSR) exhibiting the same surface area with that of the cylindrical reactor (CR), i.e., AFSR-2 and RFSR-2-i are the most efficient exhibiting hydrocarbons selectivity of 40.330% and 40.286% at CO2 conversion of 53.763% and 53.891%. In addition, it is revealed that the location of the jacket has an essential role in controlling the reactor temperature.

Funding source: BME has been supported by the NRDI Fund (TKP2020 NC, Grant No. BME-NCS)
Related subjects: Production & Supply Chain
Countries: Germany ; Hungary ; Portugal
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/content/journal2269
2020-10-25
2022-05-26
http://instance.metastore.ingenta.com/content/journal2269
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