张磊

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:清华大学

学位:博士

所在单位:化工学院

学科:化学工程

办公地点:西部校区化工实验楼D203

电子邮箱:keleiz@dlut.edu.cn

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An optimization model for carbon capture utilization and storage supply chain: A case study in Northeastern China

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论文类型:期刊论文

发表时间:2018-12-01

发表刊物:APPLIED ENERGY

收录刊物:SCIE、Scopus

卷号:231

页面范围:194-206

ISSN号:0306-2619

关键字:Carbon capture utilization and storage (CCUS); Supply chain optimization; Mixed integer linear programming (MILP); CO2 emission reduction; CO2 pipeline

摘要:In recent years, several strategies have been developed and adopted in a bid to diminish the carbon dioxide (CO2) released into the atmosphere. Carbon capture, utilization and storage (CCUS) system is one of the options. In this paper, we develop a CCUS supply chain superstructure by introducing more comprehensive transportation routes as well as the resultant system deployment schemes. A mixed integer linear programming (MILP) model is proposed to optimize the strategic CCUS deployment in Northeast China by making simultaneous selection of emission sources, capture facilitates, CO2 pipeline, intermediate transportation sites, utilization and storage sites. The CCUS cost includes the cost of flue gas dehydration, CO2 capture, transportation and injection, and revenue from CO2 utilization through enhanced oil recovery (CO2-EOR). The overall network is economically optimized over a 20 years' life span to provide the geographic distribution and scale of capture, utilization and sequestration sites as well as the transportation routes for different scenarios. The results suggest that it is economic feasible to reduce 50% of the current CO2 emissions from the stationary sources at a total annual cost $2.30 billion accompanied with $0.77 billion of revenue generated annually through CO2-EOR. Overall, the optimal CCUS supply chain network correspond to a net cost of $23.53 per ton of CO2. The results are compared with source-sink model and it can be observed that the total annualized net cost is reduced from $1.62 billion to $1.53 billion and the transportation cost are reduced from $0.27 billion to $0.19 billion.