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Application of spiking neural networks for modeling the process of high-temperature hydrogen production in systems with gas-cooled reactors

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dc.contributor.authorStarkov, S. O.
dc.contributor.authorLavrenkov, Y. N.
dc.contributor.authorСтарков, Сергей Олегович
dc.date.accessioned2024-11-21T09:51:35Z
dc.date.available2024-11-21T09:51:35Z
dc.date.issued2019
dc.description.abstract© 2019 Obninsk Institute for Nuclear Power Engineering, National Research Nuclear University 'MEPhI'. All rights reserved.The article considers a simulated possible scenario for the joint production of hydrogen and electrical energy using a high-temperature gas-cooled reactor. The considered model is based on a neural network system, which is used as a technological tool for generating control signals. The multi-layer direct-acting neural network is composed of spiking neural elements, the architecture of which is based on interacting reverberation loops. The electro-optical commuting system considered in this article is the base for building a switching communication system between neurons. The use of optical communication and liquid crystal modulators simplifies the mass distribution of a signal to many neurons from different populations and the change of its parameters. This property is necessary to ensure the neural controller high performance. The approximating properties of a neural network are used to control a group of dual electrolytic cells. Each electrolyzer has a set of variables controlling the temperature, chemical composition and current density through the cell. The spiking network, exerting a control action on pairs of electrolytic cells, completely controls the process of low-temperature electrolysis in a copper sulfate solution. The amounts of hydrogen produced at the cathodes of the grouped electrolyzers will be proportional to the amount of gas produced by the high-temperature electrolysis systems, in which nuclear reactors are the sources of thermal and electrical energy. Information coding is carried out by sequences of spiking pulses from groups of 4 neurons. This method of representing the control sequence elements minimizes a false change in the parameters of low-temperature electrolysis. Learning of the neural network system is carried out by a scattered search algorithm. The evaluation of the simulation efficiency has shown the feasibility of constructing hybrid models with a neural network control system, which do not require the use of expensive materials.
dc.format.extentС. 143-154
dc.identifier.citationStarkov, S. O. Application of spiking neural networks for modeling the process of high-temperature hydrogen production in systems with gas-cooled reactors / Starkov, S.O., Lavrenkov, Y.N. // Izvestiya Wysshikh Uchebnykh Zawedeniy, Yadernaya Energetika. - 2019. - 2019. - № 1. - P. 143-154. - 10.26583/npe.2019.1.13
dc.identifier.doi10.26583/npe.2019.1.13
dc.identifier.urihttps://www.doi.org/10.26583/npe.2019.1.13
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85067809101&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/18310
dc.relation.ispartofIzvestiya Wysshikh Uchebnykh Zawedeniy, Yadernaya Energetika
dc.titleApplication of spiking neural networks for modeling the process of high-temperature hydrogen production in systems with gas-cooled reactors
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume2019
relation.isAuthorOfPublication71403bfb-bab9-47cf-bc16-87ea26ef4973
relation.isAuthorOfPublication.latestForDiscovery71403bfb-bab9-47cf-bc16-87ea26ef4973
relation.isOrgUnitOfPublication4b108cd7-08a1-4350-ab3f-20450dec091d
relation.isOrgUnitOfPublication.latestForDiscovery4b108cd7-08a1-4350-ab3f-20450dec091d
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