Journal of Radiation Protection and Research

The Korean Association for Radiation Protection (대한방사선방어학회)
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A Preliminary Study on Evaluation of TimeDependent Radionuclide Removal Performance Using Artificial Intelligence for Biological Adsorbents

  • Janghee Lee (Department of Advanced Nuclear Engineering, Pohang University of Science and Technology) ;
  • Seungsoo Jang (Department of Advanced Nuclear Engineering, Pohang University of Science and Technology) ;
  • Min-Jae Lee (Department of Advanced Nuclear Engineering, Pohang University of Science and Technology) ;
  • Woo-Sung Cho (Department of Advanced Nuclear Engineering, Pohang University of Science and Technology) ;
  • Joo Yeon Kim (Department of Energy Policy and Engineering, KEPCO International Nuclear Graduate School) ;
  • Sangsoo Han (SierraBASE Co. Ltd.) ;
  • Sung Gyun Shin (SierraBASE Co. Ltd.) ;
  • Sun Young Lee (Department of Bioindustry and Bioresource Engineering, Sejong University) ;
  • Dae Hyuk Jang (Department of Bioindustry and Bioresource Engineering, Sejong University) ;
  • Miyong Yun (Department of Bioindustry and Bioresource Engineering, Sejong University) ;
  • Song Hyun Kim (Department of Energy Policy and Engineering, KEPCO International Nuclear Graduate School)
  • Received : 2023.02.28
  • Accepted : 2023.11.24
  • Published : 2023.12.31
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Abstract

Background: Recently, biological adsorbents have been developed for removing radionuclides from radioactive liquid waste due to their high selectivity, eco-friendliness, and renewability. However, since they can be damaged by radiation in radioactive waste, a method for estimating the bio-adsorbent performance as a time should consider the radiation damages in terms of their renewability. This paper aims to develop a simulation method that applies a deep learning technique to rapidly and accurately estimate the adsorption performance of bio-adsorbents when inserted into liquid radioactive waste. Materials and Methods: A model that describes various interactions between a bio-adsorbent and liquid has been constructed using numerical methods to estimate the adsorption capacity of the bio-adsorbent. To generate datasets for machine learning, Monte Carlo N-Particle (MCNP) simulations were conducted while considering radioactive concentrations in the adsorbent column. Results and Discussion: Compared with the result of the conventional method, the proposed method indicates that the accuracy is in good agreement, within 0.99% and 0.06% for the R2 score and mean absolute percentage error, respectively. Furthermore, the estimation speed is improved by over 30 times. Conclusion: Note that an artificial neural network can rapidly and accurately estimate the survival rate of a bio-adsorbent from radiation ionization compared with the MCNP simulation and can determine if the bio-adsorbents are reusable.

Keywords

Acknowledgement

This work was supported by Korea Hydro & Nuclear Power Co., Ltd. (No. 2020-Tech-14), the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20203210100390, Development of Eco-friendly Biomaterial to Improve the Treatment Performance of Radioactive Liquid Waste from Decommissioning) and the National Research Foundation of Korea (NRF) with the funding of the government (Ministry of Science and ICT) (No. 2021M2D2A20184452161082139290201).

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