CONTROL OF GAS PIPELINE OPERATION MODES AND LEAK DETECTION USING MACHINE LEARNING

Machine learning methods are a modern tool for managing manufacturing enterprises with a wide range of applications. In particular, when selecting operating modes of leading equipment for complex technological processes, the use of machine learning allows optimizing the use of equipment capabilities, preventing incidents and emergency situations. The paper simulates steady-state operating modes of a gas pipeline with leaks using the standard technique described in STO Gazprom 2-3.5-051-2006, as well as using the same technique but dividing the pipeline into a large number of short sections, and solving differential equations of motion and gas temperature change using the finite difference method. An analysis of the possibility of using these methods to generate a large amount of data that will allow using machine learning models to detect leaks and localize them is made.