A Microflow and Bioinspired Dendritic Topology Optimization for CO₂ Capture, Utilization and Storage Hubs in China: Integrating Microfluid Dynamics with Macro-Scale Infrastructure Design

Large-scale carbon capture, utilization, and storage (CCUS) is pivotal for global net-zero transitions, yet its deployment is constrained by suboptimal pipeline network designs that oversimplify techno-economic models, enforce rigid topologies, and computation bottlenecks. Here, we present a preferential-flow approach in porous and bioinspired algorithms that bridge microfluid dynamics and macroscale infrastructure design to address these bottlenecks. It mimics pore-scale preferential flow, or piping flow, in porous media, in which fluids naturally follow the least-resistance paths. This algorithm leverages the mathematical similarity between microscale hydraulics, pipeline flow, and cost models to facilitate cost-optimized network evolution. Nationwide simulations reveal that standalone CCUS projects in four coal-based energy and industrial sectors achieve 5.7 Gt/a at levelized costs of <$90/ton. In contrast, hub-and-spoke clusters reach 6.5 Gt/a under the same threshold in China. The resulting networks feature bimodal centroid-governed architectures that route carbon fluxes between emission sources and storage sinks while circumventing high-cost zones, with distinct regional signatures: radial configurations in northwestern basins, north–south corridors across southern China, multicentric layouts in the northeast, and land-ocean integrated west-to-east pathways in eastern regions. Validation in Ningxia demonstrates >80% emission reduction at <$50 per ton, outperforming current benchmarks. This framework advances CCUS infrastructure design through four key breakthroughs: enhanced physical fidelity by incorporating microscale flow phenomena; continuous-variable simulation capacity; 32k-grid computations completed in 5 min (compared to days for conventional approaches); and quantitative cost characterization via a novel “Carbon Reduction Matrix”. By reconciling principles of natural systems with macro-scale decarbonization imperatives, this scalable tool redefines CCUS design paradigms, providing a flexible and efficient pathway to accelerate global net-zero transitions.