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Muñoz D, Thomas AE, Cotton J, Bertrand J, Chinesta F. Hybrid Twins Modeling of a High-Level Radioactive Waste Cell Demonstrator for Long-Term Temperature Monitoring and Forecasting. SENSORS (BASEL, SWITZERLAND) 2024; 24:4931. [PMID: 39123978 PMCID: PMC11314968 DOI: 10.3390/s24154931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/12/2024] [Accepted: 07/25/2024] [Indexed: 08/12/2024]
Abstract
Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in a digital twin) reflecting the actual physical evolution of the installation over the long term (i.e., a century), the ultimate objective being to assess that the repository components/processes are effectively following the expected trajectory towards the closure phase. Data prediction involves using historical data and statistical methods to forecast future outcomes, but it faces challenges such as data quality issues, the complexity of real-world data, and the difficulty in balancing model complexity. Feature selection, overfitting, and the interpretability of complex models further contribute to the complexity. Data reconciliation involves aligning model with in situ data, but a major challenge is to create models capturing all the complexity of the real world, encompassing dynamic variables, as well as the residual and complex near-field effects on measurements (e.g., sensors coupling). This difficulty can result in residual discrepancies between simulated and real data, highlighting the challenge of accurately estimating real-world intricacies within predictive models during the reconciliation process. The paper delves into these challenges for complex and instrumented systems (multi-scale, multi-physics, and multi-media), discussing practical applications of machine and deep learning methods in the case study of thermal loading monitoring of a high-level waste (HLW) cell demonstrator (called ALC1605) implemented at Andra's underground research laboratory.
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Affiliation(s)
- David Muñoz
- PIMM Laboratory, Arts et Métiers Institute of Technology, Centre National de la Recherche Scientifique (CNRS), 151 Boulevard de l’Hôpital, 75013 Paris, France;
| | - Anoop Ebey Thomas
- ESI Group, Symbiose 2, 10 Avenue Aristide Briand, 92220 Bagneux, France;
| | - Julien Cotton
- Andra, French National Radioactive Waste Management Agency, 92298 Châtenay-Malabry, France; (J.C.); (J.B.)
| | - Johan Bertrand
- Andra, French National Radioactive Waste Management Agency, 92298 Châtenay-Malabry, France; (J.C.); (J.B.)
| | - Francisco Chinesta
- PIMM Laboratory, Arts et Métiers Institute of Technology, Centre National de la Recherche Scientifique (CNRS), 151 Boulevard de l’Hôpital, 75013 Paris, France;
- ESI Group, Symbiose 2, 10 Avenue Aristide Briand, 92220 Bagneux, France;
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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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Luo J, Ma X, Ji Y, Li X, Song Z, Lu W. Review of machine learning-based surrogate models of groundwater contaminant modeling. ENVIRONMENTAL RESEARCH 2023; 238:117268. [PMID: 37776938 DOI: 10.1016/j.envres.2023.117268] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 09/04/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023]
Abstract
Heavy computational load inhibits the application of groundwater contaminant numerical model to groundwater pollution source identification, remediation design, and uncertainty analysis, since a large number of model runs are required for these applications. Machine learning-based surrogate models are an effective approach to enhance the efficiency of the numerical models, and have recently attracted considerable attention in the field of groundwater contaminant modeling. Here, we review 120 research articles on machine learning-based surrogate models for groundwater contaminant modeling that were published between 1994 and 2022. We outline the state of the art method, identify the most significant research challenges, and suggest potential future directions. The six major applications of machine learning-based surrogate models are groundwater pollution source identification, groundwater remediation design, coastal aquifer management, uncertainty analysis of groundwater, groundwater monitoring network design, and groundwater transport parameters inversion. Together, these account for more than 90% of the studies we review. Latin hypercube sampling (LHS) is the most widely used sampling method, and artificial neural networks (ANNs) and Kriging are the two most widely used methods for constructing surrogate model. No method is universally superior, the advantages and disadvantages of different methods, as well as the applicability of these methods for different application purposes of groundwater contaminant modeling were analyzed. Some recommendations on the method selection for various application fields are given based on the reviews and experiences. Based on our review of the state-of-the-art, we suggest several future research directions to enhance the feasibility of the machine learning-based surrogate models of groundwater contaminant modeling: the alleviation of the curse of dimensionality, enhancing transferability, practical applications for real case studies, multi-source dada fusion, and real-time monitoring and prediction.
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Affiliation(s)
- Jiannan Luo
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China.
| | - Xi Ma
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China
| | - Yefei Ji
- Songliao Water Resources Commission, Ministry of Water Resources, Changchun 130021, China
| | - Xueli Li
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China
| | - Zhuo Song
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China
| | - Wenxi Lu
- Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China
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Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints. Processes (Basel) 2022. [DOI: 10.3390/pr10112365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Gekko is an optimization suite in Python that solves optimization problems involving mixed-integer, nonlinear, and differential equations. The purpose of this study is to integrate common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR), support vector regression (SVR), and artificial neural network (ANN) models into Gekko to solve data based optimization problems. Uncertainty quantification (UQ) is used alongside ML for better decision making. These methods include ensemble methods, model-specific methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared against the current partial quadratic mixture (PQM) model in an optimization problem in Gekko. GPR with conformal uncertainty was chosen as the best substitute model as it had a lower mean squared error of 0.0025 compared to 0.018 and more confidently predicted a higher waste loading of 37.5 wt% compared to 34 wt%. The example problem shows that these tools can be used in similar industry settings where easier use and better performance is needed over classical approaches. Future works with these tools include expanding them with other regression models and UQ methods, and exploration into other optimization problems or dynamic control.
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