Determination of Economic Losses of Gas Transportation Companies from Accidents on Gas Transmission Pipelines [Визначення економічних втрат газотранспортних підприємств від виникнення аварійних ситуацій на магістральних газопроводах]
Journal Title: Traektoriâ Nauki - Year 2019, Vol 5, Issue 1
Abstract
Selection of information was conducted to streamline the theoretical provisions and develop practical recommendations for determining the economic losses of gas transportation companies from accidents and failures on gas transmission pipelines, caused, in particular, by corrosion damage to a pipeline. The classification of losses due to the influence of corrosion on the linear part of gas transmission pipelines was considered. It was clarified that the structure of losses of gas transportation enterprises from corrosion during the operation of the linear part of gas transmission pipelines includes the cost of protecting the elements of structures from corrosion; losses from corrosion of metals. It was proved that the size of economic losses from accidents and failures on gas transmission pipelines to a large extent depends on the quality of the anti-corrosion protection system (measures, methods, ways and means of control), and also on observance of the rules of safe operation. The method of calculating economic losses, caused by accidents and failures on gas pipelines (taking into account the cost of compensation for losses from environmental pollution) is proposed, which takes into account the approach of neural networks and has advantages over the existing ones, as it characterizes: nonlinear quality criterion; the influence of energy characteristics of the interphase layers and aggressive agents on the corrosion processes in metal; nonlinear character of the monitoring system. On the basis of neural networks, a method of controlling the parameters, characterizing the technical state of steel structures in the oil and gas industry was developed, taking into account the nonlinear model for optimizing information and financial flows. The approach of neural networks can reduce errors in estimating the parameters of a nonlinear model that describes the system for monitoring underground pipelines.
Authors and Affiliations
Lyubomyr Sopilnyk, Ruslan Skrynkovskyy, Vitalii Lozovan, Volodymyr Yuzevych, Grzegorz Pawlowski
Keywords
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