Capturing the impact of real, complex biomass char particle morphology during gasification

对气候变化和能源安全的担忧激发了人们利用生物质来减少化石燃料消耗的兴趣。生物质气化是一种可持续的、灵活的、潜在的碳中性发电和生产液体燃料和化学品的技术。气化过程中形成的细小、多孔的生物质焦颗粒的复杂几何形状对气化速度有很大影响，进而影响气化炉的转化率和效率。通过研究以高分辨率和三维成像的几种原料的一系列生物质焦颗粒的气化，该项目将产生关于真实颗粒几何形状对气化影响的基本知识和建模能力。这将使该领域超越目前基于理想化颗粒结构的理解，并将通过帮助推进减少污染、减轻环境破坏和提高效率的清洁生物能源技术而造福社会。该项目将通过对本科生和研究生进行实验和建模技术培训，并将建模工具和数据纳入本科生和研究生联合课程，以加强学生的学习和参与，从而支持教育。该项目的目标是了解气化过程中实际生物质焦形态的影响，并利用这些知识创建一个工作流程，用于在反应堆规模的计算流体动力学程序中进行预测性、计算效率高的颗粒尺度建模。该方法是对来自几种原料的数百个单独的生物质焦颗粒进行直接的三维孔分辨模拟，这些颗粒将用高分辨率的X射线微计算机断层扫描进行成像，以了解颗粒尺度上扩散、反应和形态的耦合。气化行为被假设为不同于目前基于具有均匀、未分解的孔隙率的粗粒模型的理解，因为真实的生物质焦包含复杂的、大规模的、通常是各向异性的孔。这些洞察力和丰富的数据集将被用来改进反应堆规模代码中使用的颗粒尺度模型，方法是创建和传播一个自动化工作流程，以量化真实生物质焦颗粒分布中存在的形态范围并对其进行建模。该工作流程将包括一个图像分析程序，以同时量化数百个颗粒的三维形态，然后使用机器学习根据它们预期的气化行为对颗粒进行分类。为了完成工作流程，将创建适合纳入反应堆规模代码的高效、基于物理的气化模型，并将其参数化，将颗粒和反应堆规模耦合起来。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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. 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.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.