ITM Web Conf.
Volume 59, 2024II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|Number of page(s)
|Data Mining, Machine Learning and Patern Recognition
|25 January 2024
Supervised machine learning with regression for the IRT-T reactor cooling system
Tomsk Polytechnic University, Nuclear Fuel Cycle Department,
Lenina Ave 2,
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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