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Development of an artificial neural network for a combined model of the uranium extraction process

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Abstract

Based on a conducted literature review, a training sample was compiled. The selected optimal parameters for training an artificial neural network included its structure, activation function, output layer transfer function, and the number of neurons in hidden layers. The results of calculations using the developed artificial neural network have an uncertainty of less than 1%, which confirms its suitability for creating a digital twin of a technological process.

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Acknowledgements

The study was carried out within the framework of the Federal Project “Advanced Engineering Schools” (scientific project PISh-NIR-2023-010 “Development of mathematical models and digital twins for technological equipment of a closed nuclear fuel cycle”).

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Authors and Affiliations

  1. Tomsk Polytechnic University, Tomsk, Russian Federation

    I. S. Nadezhdin, A. M. Emelyanov & S. N. Liventsov

Authors
  1. I. S. Nadezhdin
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  2. A. M. Emelyanov
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  3. S. N. Liventsov
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Correspondence to I. S. Nadezhdin.

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Translated from Atomnaya Energiya, Vol. 135, No. 5–6, pp. 183–187, November–December, 2023.

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Nadezhdin, I.S., Emelyanov, A.M. & Liventsov, S.N. Development of an artificial neural network for a combined model of the uranium extraction process. At Energy 135, 235–241 (2024). https://doi.org/10.1007/s10512-024-01107-6

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  • DOI: https://doi.org/10.1007/s10512-024-01107-6

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