文献类型：期刊文献

英文题名：Synergizing low-carbon planning and operation for sustainable integrated refinery-petrochemical processes under arrival time uncertainty: A large-scale hierarchical energy-efficiency optimization perspective

作者：Zhang, Tingwei[1,2];Shen, Feifei[2];Li, Zhi[1,2];Peng, Xin[1,2];Zhong, Weimin[2]

机构：[1]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：377

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20243917081905);WOS:【SCI-EXPANDED(收录号:WOS:001321217700001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), National Natural Science Fund for Distinguished Young Scholars (61925305), National Natural Science Foundation of China (62303186, 62173145), Fundamental Research Funds for the Central Universities (222202417006).

语种：英文

外文关键词：Integrated refinery-petrochemical processes; Hierarchical synchronization optimization; Energy-efficiency; Low-carbon decision

摘要：Neglecting interaction among the planning and operational layers in the integrated refinery-petrochemical process can seriously compress low-carbon decision schemes' accuracy and global applicability. Accordingly, this paper constructs a multi-layer joint low-carbon production decision-making model to facilitate the interaction among the planning and operational layers. Considering the crude carrier arrival delays and raw materials arrival time uncertainties, a multi-period decomposition operation of production planning is conducted. Synchronizing the number of raw materials arrivals at the site with the number of planning cycles ensures information flow interactability. Subsequently, a hierarchical synchronization optimization framework is developed to mitigate the complexity and problem-solving difficulty of the joint decision optimization model. Additionally, a personalized problem-solving strategy that concurrently covers the carbon tax scenario and an improved optimization algorithm is designed to improve the efficiency of solving decomposed local optimization problems. The proposed model and methods are applied to a case study in a practical integrated refinery-petrochemical production site. The results indicate that the joint low-carbon production decision optimization model can effectively explore the energy-saving and emission-reduction potential at the planning and operational layers. After optimization, CO2 emissions are reduced from 1048.69 ktCO(2)eq/month to 987.19 kt CO(2)eq/month (a 5.9% reduction), and energy consumption decreased from 61520.2 tce/month to 54645.8 tce/month (an 11.2% reduction). The proposed model and methodology can serve as a foundational and optimization support for low-carbon production decision-making in integrated refinery-petrochemical processes.