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Maritime carbon dioxide (CO2) transport plays a pivotal role in facilitating carbon capture and storage (CCS) systems by connecting emission sources with appropriate storage sites. This process often incurs significant transportation costs, which must be carefully balanced against penalties for untransported CO2 resulting from cost-driven decisions. This study addresses the CO2 storage site location and transport assignment (CSSL-TA) problem, aiming to minimize total tactical costs, including storage site construction, ship chartering, transportation, and penalties for direct CO2 emissions. We formulate the problem as a mixed-integer programming (MIP) model and demonstrate that the objective function exhibits submodularity, reflecting diminishing returns in facility investment and ship operations. A case study demonstrates the model’s effectiveness and practical value, revealing that optimal storage siting, strategic ship chartering, route allocation, and efficient transportation significantly reduce both transportation costs and emissions. To enhance practical applicability, a two-stage planning framework is proposed, where the first stage selects storage sites, and the second employs a genetic algorithm (GA) for transport assignment. The GA-based solution achieves a total cost only 2.4% higher than the exact MIP model while reducing computational time by 57.9%. This study provides a practical framework for maritime CO2 transport planning, contributing to cost-effective and sustainable CCS deployment.
Details
Linear programming;
Carbon dioxide;
Integer programming;
Ports;
Sustainability;
Optimization;
Fines & penalties;
Site location;
Transportation planning;
Operating costs;
Climate change;
Carbon sequestration;
Efficiency;
Marine transportation;
Emissions;
Ships;
Genetic algorithms;
Infrastructure;
Planning;
Decision making;
Objective function;
Carbon dioxide emissions;
Sustainable transportation;
Flexibility;
Effectiveness;
Supply chains;
Mixed integer;
Computing time;
Carbon capture and storage
; Yang, Ying 2 ; Du Yuquan 3
; Wang Shuaian 2 1 School of Transportation Science and Engineering, Beihang University, Beijing 100191, China; [email protected]
2 Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China; [email protected]
3 La Trobe Business School, La Trobe University, Melbourne, VIC 3086, Australia; [email protected]