Fuzzy optimal control of multilayer coverage based on radon exhalation dynamics in uranium tailings.
Sci Rep 2023;
13:4414. [PMID:
36932170 PMCID:
PMC10023809 DOI:
10.1038/s41598-023-31518-7]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
Radon exhalation from uranium tailings has seriously affected environmental safety and human health. Many uncertain parameters, such as diffusion coefficient, porosity, percolation rate, material particle size, etc., are related to the diffusion and migration of radon. Moreover, cover materials, cover layers, and cover thickness are the main instruments to control radon exhalation, and the radon reduction effect of single-layer mulching is often inferior to that of the multilayer. Hence, achieving radon control with multilayer coverage under uncertain environment is an urgent problem that must be solved in the area of nuclear safety and radiation environment. In an attempt to address the issue, a dynamic model of radon exhalation with multilayer coverage is constructed using radon percolation-diffusion migration equation, and triangular membership functions inscribe the model parameters; the objective functions of the left and right equations of the model are constructed, and their extreme value intervals are obtained using immunogenetic algorithm. Then, subject to the total cost and thickness of multilayer covering materials, the fuzzy objective and constraint models of radon exhalation are constructed, and the fuzzy aggregation function is reconstructed according to the importance of the fuzzy objective and constraint models, where ultimately, the optimal radon control decision by swarm intelligence algorithm under different possibility levels and importance conditions can be obtained. An example is then used to validate the effectiveness of the radon exhalation model, and to demonstrate that fuzzy optimization provides a database of decision-making schemes regarding multilayer coverage, and guidance for optimal control and flexible construction management.
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