Screening for carbon value in a future bioeconomy through carbon oxidation states and statistical entropy

Abstract

The growing need for renewable resources calls for novel decision-making methods that can guide the efficient exploitation and valorisation of biomass under conditions of limited process-specific data. This study introduces a novel state-based screening method for bioresource management that evaluates biomass conversion pathways using only compositional information, thereby avoiding reliance on data-intensive, kinetic, techno-economic or process-specific parameters. The framework is built on two state variables with a direct link to the thermodynamic minimum work required for this transformation: compositional entropy (H, capturing energy expenses to reduce molecular complexity) and carbon oxidation state (OSC, capturing energy expenses for carbon reductions). Four biomass conversion processes were studied to evaluate the applicability of such framework: (i) reductive catalytic fractionation, (ii) gasification, (iii) pyrolysis, and (iv) combustion. These conversions were selected as they cover the entire range from reducing to oxidising the biomass. The findings indicate that H effectively captures compositional complexity, while OSC reflects the energetic distance from reduced carbon products typical of the petrochemical value chain. The method reveals trade-offs between purification, energy requirements and value creation, thereby offering insights into the very fundamentals behind energy requirements and economic feasibility. The proposed framework therefore offers a practical screening tool for comparing potential end products from a given biomass feedstock when detailed process data are unavailable. This method helps identify transformations that are more likely to minimise thermodynamic work while preserving carbon value.