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Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling

Author

Listed:
  • Alexey I. Shinkevich

    (Logistics and Management Department, Kazan National Research Technological University, 420015 Kazan, Russia)

  • Tatiana V. Malysheva

    (Logistics and Management Department, Kazan National Research Technological University, 420015 Kazan, Russia)

  • Yulia V. Vertakova

    (Kursk Branch, Financial University under the Government of the Russian Federation, 305016 Kursk, Russia)

  • Vladimir A. Plotnikov

    (General Economic Theory and History of Economic Thought Department, St. Petersburg State University of Economics, 191023 St. Petersburg, Russia)

Abstract

Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.

Suggested Citation

  • Alexey I. Shinkevich & Tatiana V. Malysheva & Yulia V. Vertakova & Vladimir A. Plotnikov, 2021. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:322-:d:494760
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    References listed on IDEAS

    as
    1. Yulia V. Vertakova & Vladimir A. Plotnikov, 2019. "The Integrated Approach to Sustainable Development: The Case of Energy Efficiency and Solid Waste Management," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 194-201.
    2. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    3. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    4. Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
    5. Marina V. Shinkevich & Yulia V. Vertakova & Farida F. Galimulina, 2020. "Synergy of Digitalization within the Framework of Increasing Energy Efficiency in Manufacturing Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 456-464.
    6. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
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    Cited by:

    1. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.

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