TY - GEN
T1 - Design optimization of a methane/steam reforming reactor using an evolutionary algorithm
AU - Pajak, Marcin
AU - Buchaniec, Szymon
AU - Kimijima, Shinji
AU - Szmyd, Janusz S.
AU - Brus, Grzegorz
N1 - Funding Information:
The presented research is a part of the ”Easy-to-Assemble Stack Type (EAST): Development of solid oxide fuel cell stack for the innovation in Polish energy sector” project, carried out within the FIRST TEAM program (project number First TEAM/2016-1/3) of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.
Publisher Copyright:
© ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - The aim of the presented research is the multi-objective optimization of the temperature distribution inside a methane/steam reforming reactor. The optimization is conducted using the macro-patterning concept, which divides the catalyst into separate segments and alters their density. The segments’ composition is chosen as the optimization parameter, as the beneficial influence of macro-pattering was proved in our previous studies. The optimal catalyst distribution is determined using a genetic algorithm, which mimics the rules of natural selection. The multi-objective fitness function is computed based on the amount of the methane converted in the process, as well as the difference between the maximal and the minimum temperature value inside the reactor. The results of computations indicate that the genetic algorithm can be a useful technique to design catalyst distribution in chemical reactors. The obtained results show that temperature difference could be reduced from 53 to 26 degrees, with a 30 % decrease in the methane conversion rate. However, the amount of catalyst used in this case is 60 % lower, compared with a conventional reactor.
AB - The aim of the presented research is the multi-objective optimization of the temperature distribution inside a methane/steam reforming reactor. The optimization is conducted using the macro-patterning concept, which divides the catalyst into separate segments and alters their density. The segments’ composition is chosen as the optimization parameter, as the beneficial influence of macro-pattering was proved in our previous studies. The optimal catalyst distribution is determined using a genetic algorithm, which mimics the rules of natural selection. The multi-objective fitness function is computed based on the amount of the methane converted in the process, as well as the difference between the maximal and the minimum temperature value inside the reactor. The results of computations indicate that the genetic algorithm can be a useful technique to design catalyst distribution in chemical reactors. The obtained results show that temperature difference could be reduced from 53 to 26 degrees, with a 30 % decrease in the methane conversion rate. However, the amount of catalyst used in this case is 60 % lower, compared with a conventional reactor.
KW - Catalyst distribution
KW - Macro-patterning
KW - Methane/steam reforming
KW - Multi-objective optimization
KW - Numerical analysis
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M3 - Conference contribution
AN - SCOPUS:85079664251
T3 - ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
SP - 407
EP - 418
BT - ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
A2 - Stanek, Wojciech
A2 - Gladysz, Pawel
A2 - Werle, Sebastian
A2 - Adamczyk, Wojciech
PB - Institute of Thermal Technology
T2 - 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2019
Y2 - 23 June 2019 through 28 June 2019
ER -