Optimal discrete pipe sizing for tree-shaped CO2 networks

For industries like the cement industry, switching to a carbon-neutral production process is impossible. They must rely on carbon capture, utilization and storage (CCUS) technologies to reduce their production processes’ inevitable carbon dioxide ( CO 2 ) emissions. For transporting continuously large amounts of CO 2 , utilizing a pipeline network is the most effective solution; however, building such a network is expensive. Therefore minimizing the cost of the pipelines to be built is extremely important to make the operation financially feasible. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO 2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO 2 regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: (a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and (b) the thermophysical modeling, including mixing effects, the Joule–Thomson effect, and heat exchange with the surrounding environment. We demonstrate the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO 2 network in Western Germany. Further, we show the robustness of the algorithm by solving a large artificially created set of network instances.