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

Maksim Dli, Andrei Puchkov, Valery Meshalkin, Ildar Abdeev, Rail Saitov and Rinat Abdeev
Additional contact information
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

Energies, 2020, vol. 13, issue 21, 1-13

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.

Keywords: digital twin; computational intelligence for modeling and control; apatite-nepheline ore waste processing; energy and resource efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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