Synergy mapping-informed learning rate estimation for bipolar membrane electrodialysis in carbon dioxide removal
Year: 2025
Type: article
Abstract: Delivering cost-effective carbon dioxide removal (CDR) will require rapid cost declines in emerging options that still face large technical and market uncertainty. We develop a synergy mapping framework that turns this uncertainty into a set of managed risks by making explicit where bipolar membrane electrodialysis (BMED) for CO 2 removal can co-learn and co-scale with more mature electrochemical systems, notably electrodialysis and electrolyzers, and where BMED needs bespoke innovation. We use two complementary estimates to manage uncertainty: a top-down inversion sets LR targets consistent with a 100 $/tCO₂ levelized cost under established net-zero scenarios, while a bottom-up aggregation, informed by synergy mapping, estimates the achievable LR from transferable components. Top-down yields a goal LR band of 10.74–21.84 % across 2030 and 2050 at 5–100 % shares; bottom-up gives an achievable 12.3 % (with a 95 % confidence interval of 12.24 % - 12.29 %). Overlaying them shows feasibility by 2050 at 25–100 % share, and shortfalls at 5 % in 2050 and all 2030 scenarios, driven by limited early cumulative capacity and BPM bottlenecks. To close gaps, leveraging shared manufacturing expertise from ED and electrolyzers and roll-to-roll membrane scale-up, such as BMED-specific gaps (water-splitting catalyst innovation and BPM production capacity), remain critical. By making explicit where knowledge transfer is feasible and where novel research is still needed, this framework provides practical guidance for stakeholders aiming to bring BMED-CDR to commercial readiness while advancing the technical maturity.
Source: Applied Energy
Authors: Ruoqing Wang, Wei He
Institution: King's College London
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,430
OpenAlex ID: W4416540568