Molecular Reconstruction of Complex Hydrocarbon Mixtures for Modeling of Heavy Oil Processing Научная публикация
Сборник | Mathematical Modeling of Complex Reaction Systems in the Oil and Gas Industry Монография, Wiley. США.2024. 480 c. ISBN 9781394220052. Scopus |
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Вых. Данные | Год: 2024, Страницы: 168-186 Страниц : 19 DOI: 10.1002/9781394220052.ch5 | ||
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Информация о финансировании (1)
1 | Министерство науки и высшего образования Российской Федерации (с 15 мая 2018) | FWUR-2024-0037 |
Реферат:
This chapter focuses on one narrow application of mathematical modeling for a very common problem that emerges during the building of kinetic models for oil processing: the composition representation problem and a particular set of methods for solving it. These methods are often grouped together under the umbrella term “molecular reconstruction”. Exponential, normal, beta, gamma distributions, and many more, including even Cauchy distribution, can be obtained through entropy maximization under certain constraints. The first insight into the stochastic reconstruction is the realization that we do know something about the sample. Another framework to model the composition is known as molecular type-homologous series matrix method. Generally speaking, stochastic strategies require more computations, but the difficulty of using them grows slower than for deterministic one. That is part of the reason why the stochastic approach (at least to the best of authors' knowledge) is used more often for heavy fractions.
Библиографическая ссылка:
Glazov N.
, Zagoruiko A.
Molecular Reconstruction of Complex Hydrocarbon Mixtures for Modeling of Heavy Oil Processing
Глава монографии Mathematical Modeling of Complex Reaction Systems in the Oil and Gas Industry. – Wiley., 2024. – C.168-186. – ISBN 9781394220052. DOI: 10.1002/9781394220052.ch5 Scopus OpenAlex
Molecular Reconstruction of Complex Hydrocarbon Mixtures for Modeling of Heavy Oil Processing
Глава монографии Mathematical Modeling of Complex Reaction Systems in the Oil and Gas Industry. – Wiley., 2024. – C.168-186. – ISBN 9781394220052. DOI: 10.1002/9781394220052.ch5 Scopus OpenAlex
Даты:
Опубликована online: | 26 июл. 2024 г. |
Идентификаторы БД:
Scopus: | 2-s2.0-85205634846 |
OpenAlex: | W4401011692 |
Цитирование в БД:
Пока нет цитирований