A Preliminary Study on Evaluation of Time-Dependent Radionuclide Removal Performance Using Artificial Intelligence for Biological Adsorbents
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- Title
- A Preliminary Study on Evaluation of Time-Dependent Radionuclide Removal Performance Using Artificial Intelligence for Biological Adsorbents
- Authors
- LEE, JANGHEE; Jang, Seungsoo; LEE, MIN JAE; Cho, Woo-Sung; Kim, Joo Yeon; Han, Sangsoo; Shin, Sung Gyun; Lee, Sun Young; Jang, Dae Hyuk; Yun, Miyong; Kim, Song Hyun
- Date Issued
- 2023-12
- Publisher
- 대한방사선방어학회
- 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. © 2023 The Korean Association for Radiation Protection.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/123071
- DOI
- 10.14407/jrpr.2023.00094
- ISSN
- 2508-1888
- Article Type
- Article
- Citation
- Journal of Radiation Protection and Research, vol. 48, no. 4, page. 175 - 183, 2023-12
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