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Adsorption-Based Methane Removal from Dilute Non-fossil Sources: Process Concept and Sorbent Screening
Kian Karimi1, Matteo Gazzani1,2
1Copernicus Institute of Sustainable Development, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands.
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Methane removal from dilute emission sources is gaining attention as a climate mitigation strategy targeting hard-to-abate non-fossil emissions; however, its process-level feasibility remains largely unexplored. This work develops an equilibrium-based, zero-dimensional (0D) vacuum-temperature swing adsorption (VTSA) model that enables rapid, physics-informed simulation of CH4-CO2-N2 separations and supports high-throughput sorbent screening (using here the NIST/ARPA-E adsorption data). The model is verified against a detailed one-dimensional (1D) rate-based model and coupled with a process concept incorporating subambient adsorption and heat integration. Three representative concentration regimes were investigated: semiclosed dairy barns (100 ppm of CH4), open barns and biogenic distributed sources (25 ppm of CH4), and direct-air-capture-like conditions (2 ppm of CH4). Across these cases, different existing materials were identified, including activated carbons, MOFs, and zeolites. Methane purities range from few percentage points to double-digit values, where the latter need to be confirmed with improved experimental characterization in the low partial pressure regime. Exergy consumptions are significant in all cases, with values starting from around 60 MJ/kgCH4, which, however, corresponds to around 2 MJ/kgCO2eq on a 100 years global warming potential basis. Direct methane removal from air appears to be extremely challenging, requiring a breakthrough on both material and process. These results indicate that methane capture from agriculture dilute sources is potentially viable with existing sorbents using a VTSA cycle specifically engineered for the purpose. Yet, they also highlight that further experimental characterization, validation, and detailed process modeling, for example, with rate-based 1D models and competitive isotherms, are needed to confirm these results and advance this separation process.
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