A nonlinear interval number programming algorithm for CO2 pipeline transportation design under uncertainties

Abstract

AbstractCarbon capture, utilization and storage (CCUS) technology includes three sub‐systems of CO2 capture: transportation, utilization, and storage. Pipeline transportation is the middle link of CCUS, and its optimization is closely connected with the other two sub‐systems. However, technical and economic parameter uncertainties strongly affect the optimal pipeline cost, which creates a need for flexibility in pipeline design. To solve the flexible optimization design problem in CO2 pipeline transportation, this paper proposes an interval number optimization algorithm. Average levelized cost and system robustness are given as the optimization objectives. A two‐objective, two‐level, two‐step optimization problem is established and solved using a quantum genetic algorithm (QGA). The proposed interval number optimization algorithm makes the optimization process with good decision space, the decision makers can flexibly make decisions based on experimental analysis and subjective preference, and the designed pipeline transportation is flexible and can be combined with the optimization of the other sub‐systems. It can also attain the goal of coordination and unification of CCUS optimization. Numerical studies show that the proposed method can solve the flexible optimization problem effectively in the presence of uncertainties. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.