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

捕捉气化过程中真实、复杂的生物质炭颗粒形态的影响

基本信息

批准号：
2211062
负责人：
Simcha Singer
金额：
$ 27.51万
依托单位：
Marquette University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211062&HistoricalAwards=false
关键词：
Capturing impact real complex biomass

项目摘要

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.

对气候变化和能源安全的关切激发了人们对利用生物质减少化石燃料消耗的兴趣。生物质气化是一种可持续、灵活和潜在的碳中和技术，用于发电和生产液体燃料和化学品。气化过程中形成的小的多孔生物质炭颗粒的复杂几何形状对气化速率有很大影响，这反过来又影响气化炉的输出，如转化率和效率。通过研究气化的一系列生物质炭颗粒从几个原料成像在高分辨率和三维这个项目将产生基本知识和建模能力的影响，现实的颗粒几何形状的气化。这将推动该领域超越目前基于理想化颗粒结构的理解，并将通过帮助推进清洁生物能源技术来减少污染，减轻环境破坏和提高效率，从而造福社会。该项目将通过培训本科生和研究生的实验和建模技术来支持教育，并将建模工具和数据纳入联合本科生和研究生课程，以提高学生的学习和参与度。该项目的目标是了解气化过程中真实的生物质炭形态的影响，并利用这些知识在反应器规模的计算流体动力学代码中创建预测性、计算效率高的颗粒规模建模工作流程。该方法是对来自多种原料的数百个单独的生物质炭颗粒进行直接的三维孔隙解析模拟，这些颗粒将用高分辨率X射线微计算机断层扫描成像，以了解扩散、反应和形态的耦合。在颗粒尺度上。气化行为被假设为不同于基于具有均匀的、未分辨的孔隙度的粗粒度模型的当前理解，因为真实的生物质炭包含复杂的、大规模的并且通常是各向异性的孔隙。将利用这些见解和丰富的数据集，通过创建和传播自动化工作流程来量化和模拟真实的生物质炭颗粒分布中存在的形态范围，从而改进反应器规模代码中使用的颗粒规模模型。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其预期的气化行为对颗粒进行分类。为了完成工作流程，将创建和参数化适合纳入反应堆规模代码的高效的基于物理的气化模型，耦合粒子和反应堆规模。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。