文献类型：期刊文献

英文题名：Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming

作者：Xu, Tiantian[1];Long, Jian[1,2];Zhao, Liang[1];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China

年份：2024

卷号：371

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20242516289354);WOS:【SCI-EXPANDED(收录号:WOS:001339591500001)】；

基金：This work was supported by National Key Research and Develop-ment Program of China (2022YFB3305900) , National Natural Science Foundation of China (62394345, 62373155, and 22178103) , Major Program of Qingyuan Innovation Laboratory (Grant No. 00122002) , and Fundamental Research Funds for the Central Universities (222202317006) .

语种：英文

外文关键词：Refinery; Integrated sustainable energy system; Mixed-integer nonlinear programming; Two-stage stochastic programming; Uncertainty; Greenhouse gas emission

摘要：The coupling of multimedia materials and energy with sustainable energy penetration in large-scale industrial refineries significantly lowers energy consumption and greenhouse gas (GHG) emissions. This paper presents a sustainable retrofitting framework for coupled production materials and steam systems (CPMSS) utilising twostage stochastic programming (TSSP). A novel sustainable energy-integrated CPMSS (SEICPMSS) model that includes wind, solar, and hydrogen energy and considers multiple operating modes and hydrogen consumption of the process units was established. First-principles models of wind turbines, solar thermal collectors, and proton exchange membrane electrolysers were adopted to sustainably retrofit the CPMSS. Latin hypercube sampling and Gaussian mixture model methods were applied to classify high volumes of uncertain wind speed and solar radiation data. A SEICPMSS optimisation model formulated as a mixed-integer nonlinear programming problem was developed by considering the investment costs, carbon taxes, and operational costs. Finally, a series of case studies from the industrial refinery's CPMSS were conducted to illustrate the effectiveness of the proposed method. The optimisation results indicate that the TSSP method can reduce operating costs by 5.3355 x 108 CNY/year and decrease GHG emissions by 7.4104 x 106 t/year compared to the traditional CPMSS without the integration of sustainable energy systems.