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Software for Efficient Chemical Technologies for Processing of Apatite–Nepheline Ore Waste

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Abstract

Aspects of the methodological support of digital twins were presented by the example of the chemical and energy engineering process of phosphorus production from apatite–nepheline ore waste. Algorithmic support was developed for one of the levels of the hierarchy of the information structure of a self-refining digital twin. This support is intended for the complex optimization of the operation of the phosphorus production plant according to the criterion of the minimum resource consumption. The algorithm is based on the ensemble application of deep neural networks, the training of which can continue during the operation of the plant. The results of a model experiment performed using the created program that implemented the developed algorithmic support of the self-refining digital twin were presented.

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REFERENCES

  1. Mueller, E., Chen, X., and Riedel, R., Chin. J. Mech. Eng., 2017, vol. 30, pp. 1050–1057. https://doi.org/10.1007/s10033-017-0164-7

    Article  Google Scholar 

  2. Trunzer, E., Calá, A., Leitão, P., Gepp, M., Kinghorst, J., Lüder, A., Schauerte, H., Reifferscheid, M., and Vogel-Heuser, B., Prod. Eng. Res. Devel., 2019, vol. 13, pp. 247–257. https://doi.org/10.1007/s11740-019-00902-6

    Article  Google Scholar 

  3. He, B. and Bai, K., Adv. Manuf., 2020. https://doi.org/10.1007/s40436-020-00302-5

  4. Cruz Salazar, L.A., Ryashentseva, D., Lüder, A., and Vogel-Heuser, B., Int. J. Adv. Manuf. Technol., 2019, vol. 105, pp. 4005–4034. https://doi.org/10.1007/s00170-019-03800-4

    Article  Google Scholar 

  5. Redelinghuys, A., Basson, A.,and Kruger, K., Stud. Comput. Intell., 2019, vol. 803, pp. 412–423. https://doi.org/10.1007/978-3-030-03003-2_32

    Article  Google Scholar 

  6. Ramachandran, R.P., Akbarzadeh, M., Paliwal, J., and Cenkowski, S., Food Bioprocess Technol., 2018, vol. 11, pp. 271–292. https://doi.org/10.1007/s11947-017-2040-y

    Article  Google Scholar 

  7. Makarov, V.L., Bakhtizin, A.R., and Beklaryan, G.L., Bus. Inform., 2019, vol. 13, no. 4, pp. 7–16. https://doi.org/10.17323/1998-0663.2019.4.7.16

    Article  Google Scholar 

  8. Li, Z., Fan, X., Chen, G., Yang, G., and Sun, Y., Neural Comput. Appl., 2017, vol. 28, pp. 2247–2253. https://doi.org/10.1007/s00521-016-2195-x

    Article  Google Scholar 

  9. Panchenko, S.V., Dli, M.I., Bobkov, V.I., and Panchenko, D.S., Non-ferrous Met., 2017, vol. 42, pp. 36–42. https://doi.org/10.17580/nfm.2017.01.08

    Google Scholar 

  10. Cheng, J., Wang, P., Li, G., Hu, Q., and Lu, H., Front. Inf. Technol. Electron. Eng., 2018, vol. 19, pp. 64–77. https://doi.org/10.1631/FITEE.1700789

    Article  Google Scholar 

  11. Abdel-Nasser, M. and Mahmoud, K., Neural Comput. Appl., 2019, vol. 17, pp. 2727–2740. https://doi.org/10.1007/s00521-017-3225-z

    Article  Google Scholar 

  12. Bobkov, V.I., Borisov, V.V., Dli, M.I., and Meshalkin,  V.P., Theor. Found. Chem. Eng., 2015, vol. 49, pp. 176–182. https://doi.org/10.1134/S0040579515020025

    Article  CAS  Google Scholar 

  13. Panchenko, S.V., Meshalkin, V.P., Dli, M.I., and Borisov, V.V., Tsvetn. Met., 2015, vol. 4, pp. 55–60. https://doi.org/10.17580/tsm.2015.04.10

    Article  Google Scholar 

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Funding

This work was supported by the Russian Foundation for Basic Research (project no. 18-29-24094 MK) and under a state assignment (project no. FSWF-2020-0019).

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Authors and Affiliations

  1. Mendeleev University of Chemical Technology of Russia, 127047, Moscow, Russia

    V. P. Meshalkin

  2. Kurnakov Institute of General and Inorganic Chemistry, Russian Academy of Sciences, 119991, Moscow, Russia

    V. P. Meshalkin

  3. Branch of National Research University “Moscow Power Engineering Institute”, 214013, Smolensk, Russia

    M. I. Dli, A. Yu. Puchkov & V. I. Bobkov

  4. OOO Research Institute of Economy and Management in the Gas Industry, 119311, Moscow, Russia

    A. S. Kazak

Authors
  1. V. P. Meshalkin
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  2. M. I. Dli
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  3. A. Yu. Puchkov
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  4. V. I. Bobkov
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  5. A. S. Kazak
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Corresponding author

Correspondence to V. I. Bobkov.

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Translated by V. Glyanchenko

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Meshalkin, V.P., Dli, M.I., Puchkov, A.Y. et al. Software for Efficient Chemical Technologies for Processing of Apatite–Nepheline Ore Waste. Dokl Chem 496, 42–47 (2021). https://doi.org/10.1134/S0012500821020014

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  • DOI: https://doi.org/10.1134/S0012500821020014

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