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Carbon capture and storage (CCS) technologies have widely emerged as a critical greenhouse gas reduction solution for closing the energy gap, while the world makes continuous efforts toward developing robust carbon-neutral technologies to mitigate climate changes. This research presents an economic optimization model for carbon dioxide (CO 2 ) transportation via a pipeline from a source to a sink, in which the location of the booster stations is strategically determined to minimize the cost while satisfying the design and operational constraints. The work presents a general and flexible computational framework for CO 2 pipeline transportation design by considering different factors such as inlet and outlet pipeline pressure, topographical conditions of terrain, pipeline power input, and the distance between the sink and the source. Genetic algorithms (GAs) are employed to find the best design parameters for minimizing the total cost. Results demonstrate that pipeline diameter and pipeline elevation are two factors which significantly affect CO 2 pressure drop, optimal design parameters, and the associated cost. The computational framework presented in this research is very general and compatible to the incorporation of a wide range of cost models and design limitations to generate the optimal design in different scenarios.

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