«Automation and Informatization of the fuel and energy complex»

Research of the feasibility of neural network modeling of operating modes of compressor stations

UDC: 681.5:622.279
DOI: -

Authors:

¹ Gazprom transgaz Tchaikovsky, Tchaikovsky, Russia
² NIIgazekonomika, Moscow, Russia

Keywords: neural networks, mathematical modeling, compressor stations, gas transport

Annotation:

One of the promising areas of research in the field of modeling and optimization of the modes of complex gas transport systems (GTS) is the use of neural network technologies. The use of such technologies involves the use of a large amount of data and makes it possible to identify complex patterns for constructing neural network models corresponding to the processes occurring at the physical level of gas transportation [1–3]. Compressor stations (CS) are the main object that has the possibility to control the operating modes of GTS. To assess the feasibility of using neural network technologies to solve the problem of optimizing the operation of GTS it is necessary to preliminary analyze the existing mathematical models and select the most suitable ones for obtaining the operational and technological parameters of the CS operation, which will later be used to train neural networks. The necessity of such a study is caused by the feasibility of constructing similar (neural network) models for all elements of GTS, which in turn will make it possible to synthesize the operating characteristics of the entire GTS. The result of the research will be the systematization of the existing knowledge about mathematical models of CS and recommendations for their use for neural network modeling. The models for calculating operating modes and CS equipment required for subsequent optimization problems, presented in [4–8, 16] should be preliminary analyzed.

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