NSF Award
2211062
Concerns about climate change and energy security have spurred interest in utilizing biomass to reduce the consumption of fossil fuels. Biomass gasification represents a sustainable, flexible, and potentially carbon-neutral technology to generate electricity and to produce liquid fuels and chemicals. The complex geometry of the small, porous biomass char particles formed during gasification has a strong impact on the gasification rate, which in turn affects gasifier outputs like conversion and efficiency. By studying gasification of a range of biomass char particles from several feedstocks imaged at high-resolution and in three dimensions this project will generate fundamental knowledge and modeling capabilities for the impact of realistic particle geometries on gasification. This will advance the field beyond current understanding based on idealized particle structures and will benefit society by helping to advance clean bioenergy technologies that reduce pollution, mitigate environmental damage, and increase efficiency. The project will support education by training undergraduate and graduate students in experimental and modeling techniques, and by incorporating modeling tools and data into a joint undergraduate and graduate course to enhance student learning and engagement. <br/><br/>The goals of this project are to understand the impacts of real biomass char morphology during gasification, and to use that knowledge to create a workflow for predictive, computationally efficient, particle-scale modeling in reactor-scale computational fluid dynamics codes. The approach is to perform direct three-dimensional pore-resolving simulations for many hundreds of individual biomass char particles from several feedstocks that will be imaged with high-resolution X-ray microcomputed tomography, to understand the coupling of diffusion, reaction, and morphology at the particle-scale. Gasification behavior is hypothesized to differ from current understanding based on coarse-grained models with homogeneous, unresolved porosity because real biomass char contains complex, large-scale, and often anisotropic pores. The insights and rich data sets will be leveraged to improve particle-scale models used in reactor-scale codes by creating and disseminating an automated workflow to quantify and model the range of morphologies present in real biomass char particle distributions. The workflow will consist of an image analysis routine to quantify the three-dimensional morphology of hundreds of particles simultaneously and will then use machine learning to classify particles according to their expected gasification behavior. To complete the workflow, efficient, physics-based gasification models appropriate for incorporation in reactor-scale codes will be created and parameterized, coupling particle- and reactor-scales.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
