Optimization of porous structures via machine learning for solar thermochemical fuel production

Porous reactant is the key component in solar thermochemical reactions, significantly affecting the solar energy conversion and fuel production performance. Triply periodic minimal surface (TPMS) structures, with analytical expressions and predictable structure-property relationships, can facilitate the design and optimization of such structures. This work proposes a machine learning-assisted framework to optimize TPMS structures for enhanced reaction efficiency, increased fuel production, and reduced temperature gradients. To mitigate the computational cost of conventional high-throughput optimization, neural network regression models were used to for performance prediction based on input features. The training dataset was generated using a three-dimensional multiphysics model for the thermochemical reduction driven by concentrated solar energy considering fluid flow, heat and mass transfer, and chemical reacions. Both uniform and gradient structures were initially assessed by the three-dimensional model showing gradient design in c and ω were necessary for performance enhancement. Further, with our proposed optimization framework, we found that structures with parameters c₁ ​= ​c₂ ​= ​0.5 (uniform in c ) and ω₁ ​= ​0.2, ω₂ ​= ​0.8 (gradient in ω) achieved the highest relative efficiency (f_chem/f_chem,ref) of ​1.58, a relative fuel production (Δδ/Δδ_ref) of ​7.94, and a max relative temperature gradient (dT/dy)/(dT/dy)_ref of 0.26. Kinetic properties, i.e., bulk diffusion and surface exchange coefficient, were also studied showing that for materilas with slow kinetics, the design space in terms of c and ω were highly limited compared to fast kinetics materials. Our framework is adaptable to diverse porous structures and operational conditions, making it a versatile tool for screening porous structures for solar thermochemical applications. This work has the potential to advance the development of efficient solar fuel production systems and scalable industrial applications in renewable energy technologies.