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.
Highlights
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
Neural Network Block (NNB) learning is conducted separately for LSTM and convolutional neural networks (CNNs), but in both cases it requires a sufficient number of examples; it was divided into two stages: of Results RNN” and “Interpretation of Results CNN” blocks, it is possible to use a fuzzy model to take into account the existing No-factors influencing the chemical and energy technological system (CETS) description
CNN” are used to conduct generalized analytics for the CETS state—for example, as factors for the fuzzy inference system—and the output of block R goes to the decision-making system with a higher level of the control hierarchy
Summary
Scheme for for chemical chemical and and energy energy technological technological system of phosphorus phosphorus production production from apatite-nepheline ore waste. The concept is based on the assumption that the constructionof an interactive digital model with a high degree of adequacy to real processes, for each physical system it is possible to create a virtual “mirror” image containing all information which significantly speeds up the analysis of the effectiveness of the decisions made and the assessment about the physical system. It should be noted that based on DT technologies; the same concept is used by the industrial corporations Airbus, General the structure can be implemented in both modern and outdated manufacturing entities with. DT concept distinguishes between following types: Digital Twin Prototype (DTP) contains a high-precision model of a real object, but at the same time does not include measurement results and reports coming from it. Digital Twin Instance (DTI) describes a real object and includes information about the model settings, control parameters, sensor readings, and history process. The paper is organized as follows: Section 2 develops the theoretical DTI model and the resulting optimization problem, Section 3 contains the structure for the DTE software and shows the obtained numerical results, and Section 4 presents the study conclusions
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