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Intelligent Control System Architecture for Phosphorus Production from Apatite-Nepheline Ore Waste

Author

Listed:
  • Maksim Dli

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

  • Andrey Puchkov

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

  • Artem Vasiliev

    (Rector, Moscow University for Industry and Finance “Synergy”, 129090 Moscow, Russia)

  • Elena Kirillova

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

  • Yuri Selyavskiy

    (Department of Economics and Trade, Plekhanov Russian University of Economics (Smolensk Branch), 214030 Smolensk, Russia)

  • Nikolay Kulyasov

    (Higher School of Tariff Regulation, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

Abstract

This paper proposes multilevel architecture for an intelligent control system for the complex chemical energy technological process of yellow phosphorus production from apatite-nepheline ore processing waste. The research revealed that, when controlling this process, one has to deal with large amounts of multiformat and polymodal information, and control goals differ at different levels not only in effectiveness criteria, but also in the structuredness of the level problems. On this basis, it is proposed that intelligent methods be used for the implementation of information processes and control goals at individual levels and the whole system. The artificial intelligence methods underlying the informational model of a control system offer solutions to problems of analyzing control processes at different hierarchy levels, namely the initial level of sensing devices, the levels of programmable logic controllers, dispatching of control and production processes, enterprise management and strategic planning. Besides, the intelligent control system architecture includes analytical and simulation models of processes occurring in the multistage procedure of ore waste processing by a plant consisting of a granulating machine, a conveyor-type multichambercalcining machine, and an ore thermal furnace. The architecture of information support for the control system comprises a knowledge-based inference block intended for implementing the self-refinement of neural network and simulation models. Fuzzy logic methods are proposed for constructing this block. The paper considers the deployment of control algorithms for a phosphorus production system using the Matlab software environment on the basis of a modern complex system development paradigm known as the model-oriented design concept.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6469-:d:652821
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    References listed on IDEAS

    as
    1. 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.
    2. Clyde Holsapple & Mark Sena & William Wagner, 2019. "The perceived success of ERP systems for decision support," Information Technology and Management, Springer, vol. 20(1), pages 1-7, March.
    3. Heung-Jae Lee & Seong-Su Jhang & Won-Kun Yu & Jung-Hyun Oh, 2019. "Artificial Neural Network Control of Battery Energy Storage System to Damp-Out Inter-Area Oscillations in Power Systems," Energies, MDPI, vol. 12(17), pages 1-13, September.
    4. Bing Long & Xiangnan Li & Xiaoyu Gao & Zhen Liu, 2019. "Prognostics Comparison of Lithium-Ion Battery Based on the Shallow and Deep Neural Networks Model," Energies, MDPI, vol. 12(17), pages 1-13, August.
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