Optimizing material selection and the thickness of radon reduction layer in uranium tailings pond

In this paper, a multi-objective decision model is formulated utilizing both single-layer and three-layer radon reduction layer materials by considering radon emanation rate and cost as decision objectives. Through a multi-objective non-dominated ranking method, the optimal solution is identified fr...

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Veröffentlicht in:Journal of radioanalytical and nuclear chemistry Jg. 333; H. 1; S. 205 - 213
Hauptverfasser: Deng, Nianbiao, Ding, Dexin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.01.2024
Springer
Springer Nature B.V
Schlagworte:
Analysis
Chemistry
Chemistry and Materials Science
Diagnostic Radiology
Emission
Environmental impact
Genetic algorithms
Hadrons
Heavy Ions
Inorganic Chemistry
Materials selection
Mathematical optimization
Methods
Multiple objective analysis
Nuclear Chemistry
Nuclear energy
Nuclear Physics
Optimization
Physical Chemistry
Pollutants
Ponds
Radon
Rankings
Tailings
Thickness
Uranium
Non-dominated multi-objective optimization method
Radon emanation rate
Radon reduction layer material
Uranium tailings pond
ISSN:0236-5731, 1588-2780
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Zusammenfassung:In this paper, a multi-objective decision model is formulated utilizing both single-layer and three-layer radon reduction layer materials by considering radon emanation rate and cost as decision objectives. Through a multi-objective non-dominated ranking method, the optimal solution is identified from a range of feasible alternatives. In the case of the example uranium tailings pond, the optimized three-layer radon reduction layer materials reduce the comprehensive cost by 47.8% compared with the single-layer radon reduction layer materials while meeting the radon emanation rate limit. Furthermore, the utilization of non-dominated multi-objective optimization techniques demonstrates higher efficiency in optimizing each radon reduction layer schemes.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0236-5731
1588-2780
DOI:10.1007/s10967-023-09244-1