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

Highlights • Carbon capture utilization storage (CCUS) supply chain problem is studied. • All stages for carbon capture, transport and storage are taken into account. • The model is applied to a realistic case study of Northeast China. • Scenarios assessing toward different CO 2 reduction level are implemented. • The intermediate sites for CCUS supply chain lessen transportation and total costs. Abstract In recent years, several strategies have been developed and adopted in a bid to diminish the carbon dioxide (CO 2) 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, CO 2 pipeline, intermediate transportation sites, utilization and storage sites. The CCUS cost includes the cost of flue gas dehydration, CO 2 capture, transportation and injection, and revenue from CO 2 utilization through enhanced oil recovery (CO 2 -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 CO 2 emissions from the stationary sources at a total annual cost $2.30 billion accompanied with $0.77 billion of revenue generated annually through CO 2 -EOR. Overall, the optimal CCUS supply chain network correspond to a net cost of $23.53 per ton of CO 2. 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. [ABSTRACT FROM AUTHOR]