Thermodynamic-environmental-economic nexus for sawdust to liquefied synthetic natural gas: Irreversibility-driven process optimization

Biomass-to-synthetic natural gas (SNG) pathways hold significant potential for infrastructure-compatible decarbonisation, yet no prior study has simultaneously quantified the thermodynamic, environmental, and economic performance of a complete sawdust-to-liquefied SNG (LSNG) chain within a single assessment framework. Here we present an integrated Aspen Plus model encompassing steam gasification of sawdust (5,000 kg h⁻1), two-stage catalytic methanation, pressure-swing adsorption, and cryogenic liquefaction via single (SMR) and dual (DMR) mixed-refrigerant cycles. A system-wide exergy analysis, a cradle-to-gate life cycle assessment (ReCiPe 2016 Midpoint H, five impact categories), and a discounted cash-flow techno-economic assessment were performed across the entire value chain. Optimal gasification conditions (steam-to-biomass ratio 0.56–0.60, temperature > 1200 °C) yielded syngas with a lower heating value of 40.75 MJ m−3. Two-stage methanation achieved a CH4 mole fraction of 0.45, exceeding published single-stage benchmarks by 7–29%. Exergy analysis identified tar separation and syngas cooling as the dominant irreversibility sites (∼50% of total destruction), establishing heat integration as the highest-priority design improvement. The DMR pathway delivered a global warming potential of 518–560 kg CO2-eq t⁻1 LSNG (up to 31% below fossil LNG), reduced fossil resource depletion by > 97%, and lowered acidification potential by 37–45%. The DMR break-even price of 465 USD t⁻1 falls within the historical LNG market range. The overarching finding is that irreversibility minimisation serves as a unified design objective: second-law efficiency gains propagate directly into reduced environmental burdens and improved economic viability, establishing the quantitative thermodynamic–LCA nexus for biomass-to-LSNG systems to the authors’ knowledge for the first time within a single Aspen Plus framework.