文献类型：期刊文献

英文题名：Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending

作者：Long, Jian[1];Jiang, Siyi[1];Liu, Tianbo[2];Wang, Kai[3];He, Renchu[1];Zhao, Liang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Sinopec Jinan Co, Jinan 250101, Shandong, Peoples R China;[3]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China

年份：2022

卷号：36

期号：12

起止页码：6581

外文期刊名：ENERGY & FUELS

收录：;EI(收录号：20222412227478);WOS:【SCI-EXPANDED(收录号:WOS:000813461500001)】；

基金：The work is supported by the National Key Research and Development Program of China (2021YFB1714300) , the National Natural Science Foundation of China (61925305, 61973124, and 62073142) , and the Fundamental Research Funds for the Central Universities (222202217006) .

语种：英文

外文关键词：Forecasting - Gasoline - Infrared devices - Metadata - Optimization - Petroleum refining - Principal component analysis - Uncertainty analysis

摘要：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.