Predicting biochar properties and pyrolysis life-cycle inventories with compositional modeling

Biochar, formed through slow pyrolysis of biomass, has garnered attention as a pathway to bind atmospheric carbon in products. However, life cycle assessment data for biomass pyrolysis have limitations in data quality, particularly for novel processes. Here, a compositional, predictive model of slow pyrolysis is developed, with a focus on CO2 fluxes and energy products, reflecting mass-weighted cellulose, hemicellulose, and lignin pyrolysis products for a given pyrolysis temperature. This model accurately predicts biochar yields and composition within 5 % of experimental values but shows broader distributions for bio-oil and syngas (typically within 20 %). This model is demonstrated on common feedstocks to quantify biochar yield, energy, and CO2 emissions as a function of temperature and produce key life cycle inventory flows (e.g., 0.73 kg CO2/kg poplar biochar bound carbon at 500 °C). This model can be adapted to any lignocellulosic biomass to inform development of pyrolysis processes that maximize carbon sequestration.