Education & Certifications

  • BE, American University of Beirut, Mechanical Engineering (2020)
  • MSc, ETH Z├╝rich, Process Engineering (2022)

Lab Affiliations

All Publications

  • Life Cycle Economic and Environmental Assessment of Producing Synthetic Jet Fuel Using CO2/Biomass Feedstocks. Environmental science & technology Saad, D. M., Terlouw, T., Sacchi, R., Bauer, C. 2024


    The aviation industry is responsible for over 2% of global CO2 emissions. Synthetic jet fuels generated from biogenic feedstocks could help reduce life cycle greenhouse gas (GHG) emissions compared to petroleum-based fuels. This study assesses three processes for producing synthetic jet fuel via the synthesis of methanol using water and atmospheric CO2 or biomass. A life cycle assessment and cost analysis are conducted to determine GHG emissions, energy demand, land occupation, water depletion, and the cost of producing synthetic jet fuel in Switzerland. The results reveal that the pathway that directly hydrogenates CO2 to methanol exhibits the largest reductions in terms of GHG emission (almost 50%) compared to conventional jet fuel and the lowest production cost (7.86 EUR kgJF-1); however, its production cost is currently around 7 times higher than the petroleum-based counterpart. Electrical energy was found to be crucial in capturing CO2 and converting water into hydrogen, with the sourcing and processing of the feedstocks contributing to 79% of the electric energy demand. Furthermore, significant variations in synthetic jet fuel cost and GHG emissions were shown when the electricity source varies, such as utilizing grid electricity pertaining to different countries with distinct electricity mixes. Thus, upscaling synthetic jet fuels requires energy-efficient supply chains, sufficient feedstock, large amounts of additional (very) low-carbon energy capacity, suitable climate policy, and comprehensive environmental analyses.

    View details for DOI 10.1021/acs.est.4c01578

    View details for PubMedID 38753974

  • The need for speed - optimal CO2 hydrogenation processes selection via mixed integer linear programming COMPUTERS & CHEMICAL ENGINEERING Saad, D. M., Alnouri, S. Y. 2022; 164