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

作　　者：Da Xu Lei Zhao Meng Lin 

机构地区：[1]Harbin Institute of Technology,Harbin 150090,China [2]Department of Mechanical and Energy Engineering,Southern University of Science and Technology,Shenzhen 518055,China [3]SUSTech Energy Institute for Carbon Neutrality,Southern University of Science and Technology,Shenzhen 518055,China

出　　处：《Progress in Natural Science:Materials International》2024年第5期895-906,共12页自然科学进展·国际材料(英文版)

基　　金：The National Natural Science Foundations of China under grant no. 52376191 and 52006097;the Nathional Key Research and Development Program of China under grant no. 2023YFB4104600 are acknowledged;The Shenzhen Science and Technology Innovation Commission under grant no. GJHZ20210705141808026;Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems under grant no. ZDSYS20220527171403009;Guangdong Basic and Applied Basic Research Foundation under grant no. 2023A1515011595;Guangdong Major Project of Basic Research under grant no. 2023B0303000002;SUSTech High Level of Special Funds under grant no. G03034K001;Guangdong grant under grant no. 2021QN02L562 are also acknowledged

摘　　要：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 c_(1)= c_(2)= 0.5(uniform in c) and ω_(1)= 0.2, ω_(2)= 0.8(gradient in ω) achieved the highest relative efficiency(fchem/fchem,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.

关 键 词：POROUS OPTIMIZATION KINETICS 

分 类 号：TQ517[化学工程] TP181[自动化与计算机技术—控制理论与控制工程]