Enzo Komatz

Sustainable Aviation Fuel (SAF) production from lower alcohols via the Methanol-to-Jet (MtJ) and Ethanol-to-Jet (EtJ) pathway offer a promising route for decarbonizing the aviation sector, yet systematic comparisons across multiple process configurations remain scarce, and detailed kinetic modelling of alternative upgrading strategies such as Guerbet coupling are largely absent from the literature. This work presents an Aspen Plus based Techno-Economic Assessment (TEA) of one MtJ (P1) and two EtJ pathways being conventional EtJ (P2) and Guerbet-coupling-based EtJ (P3). Defined scenarios investigate the influence of oligomerization product distribution, recycle configurations, and off-gas utilization on the Net Production Costs (NPC) and key technical performance indicators, including carbon, PtL and global efficiency, and Specific Energy Consumption (SEC). Results show that the conventional EtJ pathway P2 achieves the lowest production costs (2.5-3.7 EUR kg-1), followed by the Guerbet-based route P3 (3.8-4 EUR kg-1), while MtJ P1 exhibits the highest costs (3.5-5.2 EUR kg-1) despite the lowest SEC. Feedstock costs dominate the overall production costs across all pathways, while the oligomerization section holds large technical potential for improving overall process performance. Both EtJ pathways achieve substantially higher carbon efficiencies (51-56%) than MtJ (38-50%), due to carbon losses to methane, CO and aromatics in the Methanol-to-Olefins (MtO) conversion as well as lower selectivity towards kerosene range hydrocarbons (HC) in the single-stage co-oligomerization step. While the Guerbet-based route shows competitive simulated costs relative to MtJ, the limited experimental database for this pathway introduces considerable uncertainty, highlighting key research needs for future development and process validation.

This article presents a dataset generated for a techno-economic assessment (TEA) of the methanol-to-jet (MtJ) fuel production pathway. The dataset was produced using a large-scale Monte Carlo (MC) sampling approach applied to a steady-state process model implemented in Aspen Plus V14. The techno-economic evaluation was conducted using an external cost model, with subsequent data processing performed in Python (Version 3.11). In total, three million individual data points were generated by varying key technical and economic input parameters within predefined ranges and are under public access. For each MC sample, the net production cost on a mass basis (NPC_m, EUR kg_jet-fuel⁻¹) of synthetic jet fuel was calculated as the primary economic performance indicator. The dataset comprises both the sampled input parameters and the corresponding techno-economic output variables and is intended to support transparency, reproducibility, and further uncertainty analysis of MtJ fuel production pathways.

Power-to-liquid (PtL) fuels are a central pillar for reducing the climate impact of aviation by enabling the production of drop-in sustainable aviation fuels (SAF) from renewable electricity, hydrogen, and carbon dioxide. Among PtL options, the methanol-to-jet (MtJ) pathway is increasingly discussed, yet comprehensive, data-driven techno-economic uncertainty analyses (UA) remain scarce. This study presents a data-driven techno-economic assessment (TEA) framework that combines large-scale Monte Carlo (MC) sampling with machine-learning (ML) surrogate modeling to quantify cost uncertainty in MtJ fuel production. A detailed steady-state process and cost model implemented in Aspen Plus is used to generate three million MC samples by jointly varying economic, technical, and operational parameters. The resulting net production cost (NPC) distribution exhibits a mean value of approximately 5.7 EUR kg-1 with a standard deviation of 2.3 EUR kg-1 under fully unfiltered uncertainty assumptions. An artificial neural network (ANN) surrogate accurately emulates the Monte Carlo-based techno-economic model, enabling efficient exploration of the uncertainty range. The resulting cost distributions reveal a wide dispersion of net production costs, indicating substantial residual economic uncertainty even under constrained electricity price assumptions. These findings highlight the limitations of deterministic TEA approaches and underline the importance of data-driven assessment for evaluating the economic robustness of methanol-to-jet fuel pathways.

This article presents a dataset generated for a techno-economic assessment (TEA) of the methanol-to-jet (MtJ) fuel production pathway. The dataset was produced using a large-scale Monte Carlo (MC) sampling approach applied to a steady-state process model implemented in Aspen Plus V14. The techno-economic evaluation was conducted using an external cost model, with subsequent data processing performed in Python. In total, three million individual data points were generated by varying key technical and economic input parameters within predefined ranges and are under public access. For each MC sample, the net production cost on a mass basis (NPC_m, EUR _kgjet-fuel^-1) of synthetic jet fuel was calculated as the primary economic performance indicator. The dataset comprises both the sampled input parameters and the corresponding techno-economic output variables and is intended to support transparency, reproducibility, and further uncertainty analysis of MtJ fuel production pathways.