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- W3087064730 abstract "Gasoline blending is an important downstream operation in the refinery. The operation is susceptible to uncertainties such as fluctuation in component quality, fluctuation in demand, and a combination of both. These problems naturally involve multiple objectives and non-linear terms corresponding to the mixing of the components for which the genetic algorithm-based approach is more suitable compared to traditional mathematical programming. In this study, such graphical genetic algorithm-based reactive scheduling approach is developed which can handle dynamic changes in component quality and demand as an additional layer of decision making over the nominal scheduling. Three industrial-scale examples are solved using the developed approach for both single- and two-objective optimizations while handling the uncertainty of 10% increase in demand and 5% decrease in component quality. In single-objective optimization, the production cost is minimized whereas in two-objective optimization additionally the fluctuation in blending processing rate is minimized." @default.
- W3087064730 created "2020-09-25" @default.
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- W3087064730 date "2020-12-01" @default.
- W3087064730 modified "2023-09-27" @default.
- W3087064730 title "Discrete time reactive scheduling of gasoline blending and product delivery in presence of demand and component uncertainties using graphical genetic algorithm" @default.
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- W3087064730 doi "https://doi.org/10.1016/j.compchemeng.2020.107100" @default.
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