THE USE OF NEURAL NETWORKS FOR DESIGN CALCULATIONS OF GAS SUPPLY SYSTEMS IN SETTLEMENTS

Abstract

The article explores the application of neural networks for performing hydraulic calculations in gas supply systems. The primary objective of the study is to develop a configuration and methodology for applying machine learning to predict design and energy parameters of gas networks, taking into account a wide range of load conditions along pipeline sections. Traditional design methods, including mathematical and hydrodynamic models, have certain limitations in terms of computational speed and adaptability to dynamic changes that may occur under real operating conditions of gas supply systems. The paper proposes the use of neural networks, particularly deep learning techniques, to automate and enhance the design process. The key stages and requirements for training neural networks to solve gas supply system design tasks are defined, including data selection, network architecture configuration, and accuracy assessment of the obtained results. The study highlights the importance of high-quality data preparation and proper parameter selection to ensure model adaptability to the specific conditions of each gas distribution system. One of the critical aspects addressed in the paper is the comparison of neural networks with traditional calculation methods, such as hydrodynamic models and other mathematical approaches. The advantages and limitations of neural networks are evaluated, emphasizing their ability to quickly adapt to changing conditions and to accurately predict parameters of gas supply systems. The article also presents recommendations for integrating neural networks into existing software tools for gas supply system design, combining traditional engineering methods with advanced technologies to improve design efficiency. Special attention is given to the potential of deep learning to enhance prediction accuracy and to optimize design parameters under dynamic operating conditions. Through verification and accuracy assessment of neural network predictions, the study demonstrates that artificial neural networks can reliably forecast design and energy parameters across a broad range of operating modes. The results of the research provide a foundation for the development of new approaches to the design and optimization of gas supply systems that meet current requirements for efficiency, accuracy, and adaptability under variable loads and conditions.