Improvement of methods for designing energy-efficient industrial facilities using digital twins and neural network modeling
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Abstract
The article proposes a comprehensive methodological approach to the design of industrial facilities with increased energy efficiency based on the integration of digital twin technologies and neural network modeling. The research reveals the possibilities of creating high-precision virtual models of physical objects and systems that provide forecasting of energy consumption, revealing hidden patterns and optimizing energy processes. The developed method includes a multi-level digital twin architecture that combines a physical model of an object, an information and analytical platform, a sensor system for real-time data collection, and a predictive module based on artificial neural networks. Empirical verification of the model was performed using the material of the production complexes of the machine-building and metallurgical industries with a total data sample of 12,500 units of measurement over 24 months. The key results of the study demonstrate a 24.7% reduction in energy consumption while maintaining production performance, a 36.2% reduction in peak loads, and an increase in energy consumption forecasting accuracy to 93.8% compared with traditional methods. The proposed methodology makes a significant contribution to the development of the Industry 4.0 concept and can be adapted for various types of industrial facilities, ensuring optimization not only at the design stage, but also during operation. The research results can be used in the development of strategies for the digital transformation of industrial enterprises and the formation of energy efficiency policies.
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References
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