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Current biofuels are mainly derived from second and third generation biomasses. When used in chemical looping gasification (CLG), biomasses can be converted to undiluted syngas that is ready for conversion to biofuels, while the energy-intense air separation needed in conventional gasification is avoided. This work performs a “cradle-to-grave” life-cycle assessment (LCA) for biokerosene production and use in jet engines. The syngas for kerosene synthesis is generated through CLG technology with ilmenite as oxygen carrier and tablets of two types of microalgae and pellets of wheat straw and forest wood as feedstocks. These biomasses are representative precursors of two generations of biofuels. The LCA model covers all steps from biomass cultivation/growth to the final biokerosene combustion in jet engines. The four biomasses and ilmenite originate from inside Spain, and after production and mining they are transported to produce syngas in a CLG plant that is assumed near Zaragoza in Spain. Downstream syngas processing includes catalyzed Water-Gas Shift, Fischer-Tropsch (F-T) synthesis, Dry Reforming and uncatalyzed Pressure Swinging Adsorption and Refining. The syntheses of catalysts and adsorbents are also considered. The biokerosene is finally transported to a nearby airport for use in jet engines. The European Commission’s Environmental Footprint method with sixteen indicators is employed to study the process’s potential life-cycle impacts to climate, atmosphere, ecosystem, biodiversity, human health and natural resource depletion. Among the four biomasses, the microalgae produced from raceway pounds have the highest global warming potential (19.4 kg CO 2 eq. emissions), and this is followed by the microalgae from photobioreactors (16.6 kg CO 2 eq. emissions). Microalgae from the photobioreactor use the least land (only 83.7Pt), while wheat straw occupies the most land (1770Pt) for production of 1 kg biokerosene. Hotspot analyses are done through subprocesses’ contributions, and the most influencing flows and parameters are identified. A series of sensitivity and Monte Carlo analyses are made to study the indicators’ sensitivity and robustness. Finally, a scenario analysis is performed to find options that could be used to lower the life-cycle impacts with microalgae.
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Mei et al. (2026) studied this question.
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