Session: 17-02: Symposium Steinfeld - Solar fuels via two-step cycles + the addition

Paper Number: 138391

138391 - Porous Structure Optimization via Machine Learning for Solar Thermochemical Fuel Production 

Abstract: 

Solar-driven thermochemical fuel processing has shown potential for efficient large-scale solar fuel production due to its broadband solar absorption and favorable thermodynamics and kinetics at high operating temperatures. Optimization of the porous structure is essential to achieve high solar-to-fuel efficiency in solar thermochemical fuel production. The porous structure directly converts concentrated solar radiation into heat and facilitates heat and mass transfer, as well as provides sites for chemical reactions. An ideal porous structure is expected to have a large surface area to provide reactive sites, an extensive mass loading for high per-volume reactant mass, a slight pressure drop in fluid space to facilitate the gaseous mass transfer, and uniform solar energy absorption to guarantee thermo-mechanical stability. These optimization objectives demand a comprehensive understanding of the transport and conversion processes in porous structures. The direct 3D multiphysics model based on real morphology is time-consuming and costly to solve. Its further coupling to a conventional optimization algorithm, such as the gradient descent method for structure optimization, is challenging.

Typically, the porous structure ceramics are made from a polymer foam-forming templates. These structures feature high specific surface area and porosity, but highly random strut and pore network, which hinders precise designing and manufacturing. The triply periodic minimum surface (TPMS) structures are known for their well-defined mathematically controllable morphology and designing flexibility, providing great ease in structure optimization, so they are introduced into the optimization.

In this study, we introduced a machine learning-aided porous structure optimization method for solar thermochemical fuel production. The machine learning tool was used to link the TPMS structures' design parameters with the fuel production performance, temperature gradient, and gaseous flow pressure drop. The training data were calculated from a direct pore-level multiphysics model with various TPMS structures. The reaction model in this study considered both charge carriers' bulk diffusion and surface reactions, enabling the investigation of the material's kinetics on fuel production performance. When the porous structures became more dense, the curves of fuel production tended to cluster, so the difference made by the bulk diffusion became less evident with the decreasing porous structure porosity. For the structures with relatively large surface area and material kinetics, there is an optimization between fuel production rate and gas phase mass transport efficiency which were controlled mainly by the structures' specific surface area and porosity respectively.   

The proposed optimization method can be utilized for porous structure evaluation and optimization for high-performing solar thermochemical fuel generation.

Presenting Author: Meng Lin Southern University of Science and Technology

Presenting Author Biography: Meng Lin is an assistant professor heading the Solar Energy Conversion and Utilization Laboratory (SECUL) at the Southern University of Science and Technology (SECUL), Shenzhen. He received his PhD (2018) in the mechanical engineering from EPFL, Switzerland. Between 2018 and 2019, he was a postdoctoral researcher at the Joint Center of Artificial Photosynthesis (JCAP) and the Chemistry and Chemical Engineering Division of California Institute of Technology (Caltech). In 2019, he joined the department of mechanical and energy engineering at SUSTech with research focus on the engineering of high-performance solar conversion materials, devices, and systems to fulfill industrial-scale needs for electricity, heat, fuels, or a combination thereof.

Authors:

Da Xu Southern university of science and technology
Meng Lin Southern University of Science and Technology