CO2 Transportation Facilities: Economic Optimization Using Genetic Algorithm

According to recent studies, it has been proven that reducing greenhouse gas (GHG) emissions is imperative to prevent global warming and protect the environment [1, 2]. One of the viable options of GHG reduction is carbon capture and storage (CCS) technologies [3] in which CO2 is captured from different sources (such as power plants), then it is transported through pipelines [4, 5], and finally it is being sequestrated for long term in appropriate onshore/offshore reservoirs to prevent entering the atmosphere, which results in reducing adverse greenhouse gas impacts. In recent decades, despite the advancement of carbon capture technology to the point of commercial deployment and acknowledgement of underground reservoir storage as a secure solution, CO2 transportation systems are still a challenging issue [6]. The most expensive components of a CCS chain are the CO2 capture technologies [7]. However, optimal CO2 transportation facility design can drastically lower the project’s overall cost [8–11], especially when the source-sink distance is greater than 100 km. Recent studies demonstrate that the cost of transport facilities in a CCS project is more than anticipated [12]. Therefore, they must be designed in an economically optical manner. Moreover, a cost model intertwined with the pipeline’s hydrodynamic model is necessary [13].