Abstract
By 2050 around 12% of cumulative emissions reductions will come from Carbon Capture, Utilisation and Storage (CCUS) making it an essential component in the path towards net zero [1]. Focus will initially be on the retrofitting of fossil fuel power plants, which will shift to hard-to-decarbonise industries such as iron, steel, and concrete [1]. Such industries are often grouped together in industrial clusters. Comprising both large and small point sources concentrated over a defined geographical area, industrial clusters offer an opportunity to maximise the impact of CCUS whilst also improving economic feasibility [2]. The North Sea Port (NSP) cluster an example of this. Within the NSP cluster an initial set of five emitters are to join a capture, conditioning, and transport network by 2030. From there other emitters within the area will be able to join incrementally to 2050 [3]. However, the emitters who join and the timing of their connection will have a significant effect on the evolution the network. The pipeline network design will balance design requirements for initial emitters in a backbone network, with requirements for encouraging and enabling expansion. This study builds on scenarios defined between 2030 and 2050 [3] and applies a multi-period evolutionary-based approach (Steiner tree with Obstacles Genetic Algorithm (StObGA)) to predict pipeline year-on-year network growth in the NSP cluster. This provides a novel approach to the problem. The results are used in an examination of the potential growth of the pipeline network and an investigation of trade-offs necessary in the infrastructure design.