Superstructure Optimization with Embedded Neural Networks for Sustainable Aviation Fuel Production

Abstract

This study presents a multi-objective optimization framework for sustainable aviation fuel (SAF) production, integrating artificial neural networks (ANNs) within a mixed-integer quadratically constrained programming (MIQCP) formulation. By embedding data-driven surrogate models into the mathematical optimization structure, the proposed methodology addresses key limitations of conventional superstructure-based approaches, enabling simultaneous optimization of discrete process choices and continuous operating parameters. The framework captures variable input and output stream compositions, facilitating the joint optimization of target product composition and system design. Application to Fischer-Tropsch (FT) kerosene production demonstrates that cost-minimizing configurations under unconstrained CO2 emissions are dominated by the fossil-based autothermal reforming (ATR) route. Imposing carbon emission constraints necessitates the integration of biomass gasification and direct air capture coupled with carbon sequestration (DAC-CS), resulting in substantially reduced net emissions but higher production costs. At the zero-emission limit, hybrid configurations combining ATR and biomass gasification achieve the lowest costs (~2.38 \$/kg-kerosene), followed closely by biomass gasification-only (~2.43 \$/kg), both of which outperform the ATR-only pathway with DAC-CS (~2.65 \$/kg). In contrast, DAC-only systems relying exclusively on atmospheric CO2 and water electrolysis are prohibitively expensive (~10.8 \$/kg). The results highlight the critical role of the embedded ANNs: optimal process conditions, such as FT reactor pressure and gasification temperature, adapt to changing circumstances, consistently outperforming fixed setups and achieving up to 20% cost savings.