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- W4226300541 abstract "Gasoline blending under uncertainty in the refinery value chain optimization has gained tremendous attention. This paper proposes a data-driven chance-constrained programming approach to address this issue and guarantee the benefit of the refinery value chain. First, the blending effect model is introduced to capture the uncertainties in component properties, where the blending effect value is estimated from historical process data by the recursive least-squares (RLS). Second, a chance-constrained gasoline blending model is proposed to ensure the on-specification products with a high probability in uncertain environments. Third, the Wasserstein generative adversarial networks (WGANs) are employed to generate blending effect data unsupervised. Fourth, a scenario-based approach is used to reformulate the chance-constrained gasoline blending problem based on sufficient generated data. Accounting for the complexity of the resulting large-scale optimization, a sequential algorithm is applied to reduce the computational cost. Finally, an industrial case study of gasoline blending is presented to demonstrate its applicability." @default.
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- W4226300541 date "2022-04-08" @default.
- W4226300541 modified "2023-10-16" @default.
- W4226300541 title "A Scenario-Based Chance-Constrained Program for Gasoline Blending under Uncertainty" @default.
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- W4226300541 doi "https://doi.org/10.1021/acs.iecr.1c04736" @default.
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