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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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  • Zhang, Shuai
  • Liu, Linlin
  • Zhang, Lei
  • Zhuang, Yu
  • Du, Jian

Abstract

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.

Suggested Citation

  • Zhang, Shuai & Liu, Linlin & Zhang, Lei & Zhuang, Yu & Du, Jian, 2018. "An optimization model for carbon capture utilization and storage supply chain: A case study in Northeastern China," Applied Energy, Elsevier, vol. 231(C), pages 194-206.
    • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:194-206
    DOI: 10.1016/j.apenergy.2018.09.129
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    10. Yiwei Wu & Hongyu Zhang & Shuaian Wang & Lu Zhen, 2023. "Mathematical Optimization of Carbon Storage and Transport Problem for Carbon Capture, Use, and Storage Chain," Mathematics, MDPI, vol. 11(12), pages 1-14, June.
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    13. Wang, Sijia & Jiang, Lanlan & Cheng, Zucheng & Liu, Yu & Zhao, Jiafei & Song, Yongchen, 2021. "Experimental study on the CO2-decane displacement front behavior in high permeability sand evaluated by magnetic resonance imaging," Energy, Elsevier, vol. 217(C).
    14. Abdoli, B. & Hooshmand, F. & MirHassani, S.A., 2023. "A novel stochastic programming model under endogenous uncertainty for the CCS-EOR planning problem," Applied Energy, Elsevier, vol. 338(C).
    15. Holz, Franziska & Scherwath, Tim & Crespo del Granado, Pedro & Skar, Christian & Olmos, Luis & Ploussard, Quentin & Ramos, Andrés & Herbst, Andrea, 2021. "A 2050 perspective on the role for carbon capture and storage in the European power system and industry sector," Energy Economics, Elsevier, vol. 104(C).
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