Supervised machine learning with regression for the IRT-T reactor cooling system; ITM Web of Conferences; Vol. 59 : II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)

Supervised machine learning with regression for the IRT-T reactor cooling system; ITM Web of Conferences; Vol. 59 : II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)

Detalles Bibliográficos
Parent link:ITM Web of Conferences.— .— Les Ulis: EDP Sciences
Vol. 59 : II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023).— 2024.— Article number 03007, 10 p.
Autor principal: Kublinsky M. K. Maksym Konstantinovich
Autor Corporativo: National Research Tomsk Polytechnic University
Otros Autores: Smolnikov N. V. Nikita Viktorovich, Naymushin A. G. Artem Georgievich
Sumario:Title screen
The purpose of this study is to create a machine learning model for the IRT-T reactor cooling system, which can estimate and predict the temperature difference in the secondary circuit. To do this, data was downloaded from the SCADA system, then an application was developed for converting and preprocessing this data. Then regression and classification models were constructed that evaluated the efficiency of the cooling system and its ability to predict changes in the temperature drop on heat exchangers. The main technical characteristics of the IRT-T reactor include a thermal power of 6 MW, the use of UO2 nuclear fuel in an aluminum matrix with an enrichment of 90.1%, a coolant in the form of desalinated water, tubular square-section fuel rods with external cooling, the fuel element shell material is SAV-1 alloy, and the use of five 5 IRT-1000 type heat exchangers with a total area of heat exchange of 1000 m2
Текстовый файл
Lenguaje:inglés
Publicado: 2024
Colección:Data Mining, Machine Learning and Patern Recognition
Materias:
электронный ресурс
труды учёных ТПУ
IRT-T reactor cooling system
regression and classification models
efficiency of the cooling system
Acceso en línea:https://doi.org/10.1051/itmconf/20245903007
Formato: Electrónico Capítulo de libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=672529

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