Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Gravitational Search Algorithm for Estimating the Kinetic Parameters of the Propane Pre-reforming Reaction. / Enikeeva, Leniza; Enikeev, Marat; Gubaydullin, Irek и др.
Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology. Institute of Electrical and Electronics Engineers Inc., 2020. 9253236 (Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Gravitational Search Algorithm for Estimating the Kinetic Parameters of the Propane Pre-reforming Reaction
AU - Enikeeva, Leniza
AU - Enikeev, Marat
AU - Gubaydullin, Irek
AU - Shamshovich, Valentina
N1 - Funding Information: The reported study was funded by RFBR, project number 19-37-60014. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/5/26
Y1 - 2020/5/26
N2 - The article describes a gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming into methane- rich gas over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudo-code are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the inverse problem of chemical kinetics was solved, and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly describes the available experimental data.
AB - The article describes a gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming into methane- rich gas over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudo-code are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the inverse problem of chemical kinetics was solved, and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly describes the available experimental data.
KW - chemical kinetics
KW - gravitational search algorithm
KW - mathematical modeling
UR - http://www.scopus.com/inward/record.url?scp=85097595452&partnerID=8YFLogxK
UR - https://elibrary.ru/item.asp?id=45080408
U2 - 10.1109/ITNT49337.2020.9253236
DO - 10.1109/ITNT49337.2020.9253236
M3 - Conference contribution
AN - SCOPUS:85097595452
T3 - Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology
BT - Proceedings of ITNT 2020 - 6th IEEE International Conference on Information Technology and Nanotechnology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Information Technology and Nanotechnology, ITNT 2020
Y2 - 26 May 2020 through 29 May 2020
ER -
ID: 27351359