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Kim B, Manchuri AR, Oh GT, Lim Y, Son Y, Choi S, Kang M, Jang J, Ha J, Cho CH, Lee MW, Lee DS. Experimental analysis and prediction of radionuclide solubility using machine learning models: Effects of organic complexing agents. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:134012. [PMID: 38492397 DOI: 10.1016/j.jhazmat.2024.134012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/02/2024] [Accepted: 03/10/2024] [Indexed: 03/18/2024]
Abstract
Radioactive wastes contain organic complexing agents that can form complexes with radionuclides and enhance the solubility of these radionuclides, increasing the mobility of radionuclides over great distances from a radioactive waste repository. In this study, four radionuclides (cobalt, strontium, iodine, and uranium) and three organic complexing agents (ethylenediaminetetraacetic acid, nitrilotriacetic acid, and iso-saccharic acid) were selected, and the solubility of these radionuclides was assessed under realistic environmental conditions such as different pHs (7, 9, 11, and 13), temperatures (10 °C, 20 °C, and 40 °C), and organic complexing agent concentrations (10-5-10-2 M). A total of 720 datasets were generated from solubility batch experiments. Four supervised machine learning models such as the Gaussian process regression (GPR), ensemble-boosted trees, artificial neural networks, and support vector machine were developed for predicting the radionuclide solubility. Each ML model was optimized using Bayesian optimization algorithm. The GPR evolved as a robust model that provided accurate predictions within the underlying solubility patterns by capturing the intricate relationships of the independent parameters of the dataset. At an uncertainty level of 95%, both the experimental results and GPR simulated estimations were closely correlated, confirming the suitability of the GPR model for future explorations.
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Affiliation(s)
- Bolam Kim
- Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Amaranadha Reddy Manchuri
- Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Gi-Taek Oh
- Department of Chemical Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea
| | - Youngsu Lim
- Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea
| | - Yuhwa Son
- LILW Technology Team, Korea Radioactive Waste Agency, 19 Chunghyochun-gil, Gyeongju-si, Gyeongsangbuk-do 38062, Republic of Korea
| | - Seho Choi
- LILW Technology Team, Korea Radioactive Waste Agency, 19 Chunghyochun-gil, Gyeongju-si, Gyeongsangbuk-do 38062, Republic of Korea
| | - Myunggoo Kang
- LILW Technology Team, Korea Radioactive Waste Agency, 19 Chunghyochun-gil, Gyeongju-si, Gyeongsangbuk-do 38062, Republic of Korea
| | - Jiseon Jang
- HLW Technology Development Institute, Korea Radioactive Waste Agency, 174 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
| | - Jaechul Ha
- LILW Technology Team, Korea Radioactive Waste Agency, 19 Chunghyochun-gil, Gyeongju-si, Gyeongsangbuk-do 38062, Republic of Korea
| | - Chun-Hyung Cho
- HLW Technology Development Institute, Korea Radioactive Waste Agency, 174 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea
| | - Min-Woo Lee
- Department of Chemical Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea
| | - Dae Sung Lee
- Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.
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