文献类型：期刊文献

英文题名：Sustainable Retrofit of Industrial Utility System Using Life Cycle Assessment and Two-Stage Stochastic Programming

作者：Wang, Qipeng[1];Han, Xiao[1];Zhao, Liang[1];Ye, Zhencheng[1]

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

年份：2022

卷号：10

期号：41

起止页码：13887

外文期刊名：ACS SUSTAINABLE CHEMISTRY & ENGINEERING

收录：;EI(收录号：20224212896280);WOS:【SCI-EXPANDED(收录号:WOS:000875393000001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (Key Program: 62136003) , National Natural Science Fund for Distinguished Young Scholars (61925305) , National Natural Science Foundation of China (22178103) , and Shanghai AI Lab. The forcing dataset used in this study was developed by Data Assimilation and Modeling Center for Tibetan Multi-spheres, Institute of Tibetan Plateau Research, Chinese Academy of Sciences.

语种：英文

外文关键词：Industrial utility system; Renewable energy; Life cycle assessment; Two-stage stochastic optimization; Multiobjective optimization; Uncertainty

摘要：Utility systems provide heat and power to drive manufacturing processes, and they emit large amounts of carbon dioxide. Introducing renewable energy into the traditional industrial utility system can help to reduce carbon emissions significantly. This paper proposed a sustainable retrofit framework for utility systems based on life cycle assessment (LCA) and two-stage stochastic programming (TSSP). A superstructure model of the sustainable utility system was presented first by integrating wind and solar energy with fossil energy. Then, the first-principles models of the wind turbine, solar heat collector, and thermal storage tank were developed to retrofit the utility system under wind speed and solar radiation uncertainty. LCA was used to calculate the global warming potential (GWP) of the utility system, and then the multiobjective environmental and economic optimization model was formulated. The Latin hypercube sampling and k- medoids clustering methods were employed to handle wind speed and solar radiation uncertainty in the TSSP framework. Finally, a case study of an industrial utility system was applied to demonstrate the effectiveness of the proposed method. The optimization results show that the TSSP method can reduce by 4.7% the total annual cost and lower by 3.9% the GWP in comparison to the deterministic optimization of a traditional utility system that does not integrate any renewable energy.