With
the increasing challenges in environmental protection, product
quality, and market profit, the development of a feasible optimization
approach coupling accurate oil property online prediction has become
crucial to the intelligent production of refineries. However, traditional
property modeling with near-infrared (NIR) spectroscopy cannot dynamically
extract the synergistic effect between wavelengths and cause inaccurate
property prediction. The success rate of the data-driven robust optimization
(DDRO) of the refining process is influenced by process uncertainty.
Thus, we proposed a modified hybrid strategy on online NIR prediction
modeling with adjustable characteristic wavelength selection and DDRO
under uncertainty for the refining process. An adjustable feature
space variable extraction (AFSVE) method was first proposed to dynamically
select the characteristic wavelengths for constructing accurate property
prediction models. A data-driven robust optimization of gasoline blending
model was rendered through dual transformation using the uncertainty
set of oil property derived from principal component analysis combined
with robust kernel density estimation. An industrial application showed
the best prediction accuracy of property models with the AFSVE method,
and a high blending success rate was obtained by the proposed modified
hybrid strategy, which hedges against uncertainties, including measuring
error and blending effect, and boosts environmentally friendly and
intelligent process operation.
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