DOI QR코드

DOI QR Code

Fuzzy optimization of radon reduction by ventilation system in uranium mine

  • Meirong Zhang (School of Resources Environment and Safety Engineering, University of South China) ;
  • Jianyong Dai (School of Resources Environment and Safety Engineering, University of South China)
  • 투고 : 2022.07.26
  • 심사 : 2023.02.14
  • 발행 : 2023.06.25

초록

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (No: 51174116).

참고문헌

  1. B. Dai, Q.G. Zhao, M. Zhang, et al., Numerical simulation of radon concentration in soil of hehuan road in tan Lu fault suqian segment, Technol. Earthq. Disaster Prev. 16 (2021) 220-228, https://doi.org/10.11899/zzfy20210123.
  2. J.B. Wu, H. Zhang, H.J. Su, Numerical simulation of migration rule of fault gas radon in different types of overburdens, Acta Seismologica Sinica 36 (2014) 118-128+159, https://doi.org/10.3969/j.issn.0253-3782.2014.01.010.
  3. T.D. Rao, S. Chakraverty, Forward and inverse techniques for fuzzy fractional systems applied to radon transport in soil chambers, Chaos, Solit. Fractals 147 (2021), 110916, https://doi.org/10.1016/j.chaos.2021.110916, 3.
  4. B. Liu, K. Yao, Uncertain multilevel programming: algorithm and applications, Comput. Ind. Eng. 89 (2015) 235-240, https://doi.org/10.1016/j.cie.2014.09.029.
  5. V.C. Gerogiannis, Preface to the special issue on "applications of fuzzy optimization and fuzzy decision Making&rdquo, Mathematics 9 (2021), https://doi.org/10.3390/math9233009.
  6. F. Jimenez, J.L. Verdegay, Solving fuzzy solid transportation problems by an evolutionary algorithm based parametric approach, Fuzzy Set Syst. 117 (1999) 485-510, https://doi.org/10.1016/S0377-2217(98)00083-6.
  7. X.J. Li, Z. Zhang, P.H. Hu, et al., Development process of radon reduction by ventilation in uranium mines in China, Dev.history.Uranium.mine.Vent. China.Radiat. Protect. 41 (2021) 9-16. http://journal01.magtech.org.cn/Jwk3_fsfh/CN/Y2021/V41/I1/9.
  8. Z.K. Yang, Y. Niu, Influence of temperature, enclosure and ventilation on radon concentration distribution in a blind roadway, Computational Physics 38 (2021) 456-464, https://doi.org/10.19596/j.cnki.1001-246x.8295.
  9. X.S. Su, D. Ji, Y. Liu, et al., Simulation study on the effect of length variation of ventilation pipeline on deuterium concentration in uranium mine, J. Isot. 31 (2018) 256-261, https://doi.org/10.7538/tws.2018.31.04.0256.
  10. L.Q. Yang, B.H. Li, D. Zhao, et al., Numerical simulation for radon migration in the homogeneous overburden layer above uranium ore body, Uranium Geol. 36 (2020) 441-452, https://doi.org/10.3969/j.issn.1000-0658.2020.05.014.
  11. H. Garg, M. Rani, S.P. Sharma, et al., Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment, Expert Syst. Appl. 41 (2014) 3157-3167, https://doi.org/10.1016/j.eswa.2013.11.014.
  12. M.Y. Cheng, D. Prayogo, A novel fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving structural optimization problems, Eng. Comput. 33 (2017) 55-69, https://doi.org/10.1007/s00366-016-0456-z.
  13. H.C. Wu, T. Chen, C.H. Huang, A piecewise linear FGM approach for efficient and accurate FAHP analysis: smart backpack design as an example, Mathematics 8 (2020) 1319, https://doi.org/10.3390/math8081319.
  14. M.C. Carnero, Waste segregation FMEA model integrating intuitionistic fuzzy set and the PAPRIKA method, Mathematics 8 (2020) 1-29, https://doi.org/10.3390/math8081375.
  15. M.C. Chiu, T.C.T. Chen, K.W. Hsu, Modeling an uncertain productivity learning process using an interval fuzzy methodology, Mathematics 8 (2020) 998, https://doi.org/10.3390/math8060998.
  16. H. Kim, H.Y. Jung, ridge fuzzy regression modelling for solving multicollinearity, Mathematics 8 (2020) 1572, https://doi.org/10.3390/math8091572.
  17. M.H. Wang, W.C. Yeh, T.C. Chu, et al., Solving multi-objective fuzzy optimization in wireless smart sensor networks under uncertainty using a hybrid of IFR and SSO algorithm, Energies 11 (2018) 2385, https://doi.org/10.3390/en11092385.
  18. J.F. Tang, D.W. Wang, An interactive approach based on a genetic algorithm for a type of quadratic programming problems with fuzzy objective and resources, Comput. Oper. Res. 24 (1997) 413-422, https://doi.org/10.1016/S0305-0548(96)00059-7.