Brightcon 2026, hackathon and courses

Power-to-Gas technologies that convert CO₂ into renewable biomethane are attracting increasing interest as routes for long-term energy storage, carbon valorisation, and decarbonisation of the gas grid. Among them, electromethanogenesis, where electrotrophic methanogenic archaea reduce CO₂ to CH₄ at a biocathode driven by renewable electricity, is a promising but highly coupled bioelectrochemical process. Its development requires modelling tools able to connect electrochemical, biological, mass-transfer, thermodynamic, and energy-related phenomena across scales, from microbial biofilm behaviour to industrial deployment.

Within the Fuels-C Horizon Europe project, we have developed a modular digital twin for an electromethanogenesis bioelectrochemical system. The tool integrates coupled sub-models describing electrochemical polarisation, anodic biofilm growth, cathodic methanogenesis, gas–liquid mass transfer, carbonate speciation, and energy balance within a unified dynamic simulation framework. The model was calibrated and validated against experimental time-series data obtained under two different CO₂ feeding conditions, showing close agreement with measured electrochemical and biological responses across both scenarios.

A distinctive feature of the tool is its industrial scale-up module, which translates cell-level experimental performance into production targets, infrastructure requirements, and preliminary cost indicators. This functionality makes key bottlenecks visible and quantitative, supporting the prioritisation of future experiments and longer-term deployment strategies. In addition, the model outputs and parameter sets can be connected with Life Cycle Assessment workflows to evaluate prospective environmental impacts under realistic scale-up scenarios.

Beyond the specific electromethanogenesis case study, this work illustrates the value of open, modular digital twins within the sustainability engineering ecosystem. By making model structure, assumptions, calibration data, and scale-up logic explicit and reusable, the approach enables collaborative refinement across institutions, establishes traceable links between laboratory observations and real-world projections, and reduces duplicated modelling effort. This aligns directly with the BrightCon 2026 focus on connecting tools, data, and people to accelerate transparent, reproducible, and actionable sustainability assessment.