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

• 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; Capturing; impact; real; complex; biomass

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.

人们对气候变化和能源安全的担忧激发了利用生物量减少化石燃料消费的兴趣。生物质气化代表一种可持续，灵活且潜在的碳中性技术，可发电并生产液体燃料和化学物质。在气化过程中形成的小型多孔生物量char颗粒的复杂几何形状对气体速率产生了强烈的影响，这反过来影响了气化炉输出，例如转化率和效率。通过研究来自高分辨率和三个维度成像的几个原料中的一系列生物量CHAR颗粒的气化，该项目将产生基本知识和建模能力，从而对现实粒子几何形状对气体的影响产生影响。这将使该领域超越基于理想化的粒子结构的当前理解，并通过帮助促进清洁生物能源技术来使社会受益，从而减少污染，减轻环境损害并提高效率。该项目将通过培训实验和建模技术的培训本科生和研究生来支持教育，并通过将建模工具和数据纳入共同的本科和研究生课程，以增强学生的学习和参与度。该项目的目标是了解气化过程中实际生物量CHAR形态的影响，并使用该知识来创建一个工作流程，以在反应堆尺度计算流体动力学代码中为预测，计算高效，粒子尺度建模。该方法是对数百个将与高分辨率X射线微型层析成像成像的数百个单独的生物量CHAR颗粒进行直接的三维孔隙分解模拟，以了解粒子尺度上扩散，反应和形态的耦合。假设气化行为与基于具有均匀，未解决的孔隙率的粗粒模型的当前理解不同，因为实际生物量char包含复杂的，大规模的且通常是各向异性孔。通过创建和传播自动化工作流程，以量化和建模真实生物量CHAR粒子分布中存在的形态范围，将利用洞察力和丰富的数据集来改善反应器规模代码中使用的粒子尺度模型。工作流将包括图像分析程序，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粒子进行分类。为了完成工作流程，将创建和参数化适用于反应器规模的代码中的工作流程，基于物理的气化模型，将耦合粒子和反应器 - 统计结合起来。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查的审查标准来通过评估来通过评估来获得支持的。