Method of structural minimization of the average risk for identification of mass transfer of evaporating oil at tanker loading

UDK: 622.692.4
DOI: 10.24887/0028-2448-2019-10-108-111
Key words: tanker, evaporation losses, oil loading operations, mass transfer, parameter identification problem
Authors: A.A. Korshak (The Pipeline Transport Institute LLC, RF, Moscow), M.T. Gaisin (The Pipeline Transport Institute LLC, RF, Moscow), V.V. Pshenin (Saint-Petersburg Mining University, RF, Saint-Petersburg)

Many complex technical systems are characterized by such properties as nonlinearity, non-equilibrium, stochasticity. The nature of this should be sought in the complex interaction of the constituent elements of such systems. To date, a number of scientific approaches have been developed that allow us to correctly describe such systems or to obtain some reasonable estimates of the main parameters. This paper shows how the use of one of these approaches (structural risk minimization) can be effectively used to identify the parameters of the evaporation process in the course of loading oil into tankers.

Despite the good knowledge of oil evaporation processes, in the case of tanker loading, the usual models face insurmountable difficulties. Deterministic models based on the solution of the system of equations of diffusion, heat and mass transfer, and gas dynamics are very complex in numerical implementation, and demanding on the accuracy and completeness of the original data. Stochastic, based on criterion equations of mass transfer, models need a reliable basis from numerous experimental studies. It should be taken into account here that under the operating conditions of the offshore oil terminals, most of the parameters characterizing the process of oil evaporation during loading are not controlled by the automation systems of tankers and berthing facilities. Thus, there is no reliable source of information about the characteristics of the process under study. Find a compromise in this case allows the proposed method, which is based not only on the results of regression analysis, but also allows to take into account the complexity of the model.


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