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Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach

Author

Listed:
  • Maksim Dli

    (Department of Information Technologies in Economics and Management, National Research University (Moscow Power Engineering Institute, Smolensk Branch), 214013 Smolensk, Russia)

  • Andrei Puchkov

    (Department of Information Technologies in Economics and Management, National Research University (Moscow Power Engineering Institute, Smolensk Branch), 214013 Smolensk, Russia)

  • Valery Meshalkin

    (Department of Logistics and Economic Informatics, Mendeleev University of Chemical Technology, 125993 Moscow, Russia)

  • Ildar Abdeev

    (Department of Technological Machines and Equipment, Bashkir State University, 450076 Ufa, Russia)

  • Rail Saitov

    (Department of Technological Machines and Equipment, Bashkir State University, 450076 Ufa, Russia)

  • Rinat Abdeev

    (Department of Technological Machines and Equipment, Bashkir State University, 450076 Ufa, Russia)

Abstract

The paper presents a structure of the digital environment as an integral part of the “digital twin” technology, and stipulates the research to be carried out towards an energy and recourse efficiency technology assessment of phosphorus production from apatite-nepheline ore waste. The problem with their processing is acute in the regions of the Russian Arctic shelf, where a large number of mining and processing plants are concentrated; therefore, the study and creation of energy-efficient systems for ore waste disposal is an urgent scientific problem. The subject of the study is the infoware for monitoring phosphorus production. The applied study methods are based on systems theory and system analysis, technical cybernetics, machine learning technologies as well as numerical experiments. The usage of “digital twin” elements to increase the energy and resource efficiency of phosphorus production is determined by the desire to minimize the costs of production modernization by introducing advanced algorithms and computer architectures. The algorithmic part of the proposed tools for energy and resource efficiency optimization is based on the deep neural network apparatus and a previously developed mathematical description of the thermophysical, thermodynamic, chemical, and hydrodynamic processes occurring in the phosphorus production system. The ensemble application of deep neural networks allows for multichannel control over the phosphorus technology process and the implementation of continuous additional training for the networks during the technological system operation, creating a high-precision digital copy, which is used to determine control actions and optimize energy and resource consumption. Algorithmic and software elements are developed for the digital environment, and the results of simulation experiments are presented. The main contribution of the conducted research consists of the proposed structure for technological information processing to optimize the phosphorus production system according to the criteria of energy and resource efficiency, as well as the developed software that implements the optimization parameters of this system.

Suggested Citation

  • Maksim Dli & Andrei Puchkov & Valery Meshalkin & Ildar Abdeev & Rail Saitov & Rinat Abdeev, 2020. "Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach," Energies, MDPI, vol. 13(21), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5829-:d:441717
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    References listed on IDEAS

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    1. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    2. Valery Meshalkin & Vladimir Bobkov & Maksim Dli & Vincenzo Dovì, 2019. "Optimization of Energy and Resource Efficiency in a Multistage Drying Process of Phosphate Pellets," Energies, MDPI, vol. 12(17), pages 1-17, September.
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    5. Xiaohui Zhang & Xinhua Liu & Shufeng Tang & Grzegorz Królczyk & Zhixiong Li, 2019. "Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 12(17), pages 1-24, August.
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    8. Om Prakash Verma & Toufiq Haji Mohammed & Shubham Mangal & Gaurav Manik, 2018. "Optimization of steam economy and consumption of heptad’s effect evaporator system in Kraft recovery process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 111-130, February.
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    Cited by:

    1. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.
    2. Maksim Dli & Andrey Puchkov & Artem Vasiliev & Elena Kirillova & Yuri Selyavskiy & Nikolay Kulyasov, 2021. "Intelligent Control System Architecture for Phosphorus Production from Apatite-Nepheline Ore Waste," Energies, MDPI, vol. 14(20), pages 1-13, October.

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