Predictive Computational Tools for Biomass Fast Pyrolysis in Fluidized Bed Reactors using Particle-Resolved Direct Numerical Simulation of Reacting Gas-Solid Flow

使用反应气固流的粒子分辨直接数值模拟实现流化床反应器中生物质快速热解的预测计算工具

• 批准号: 1336941
• 负责人: Shankar Subramaniam
• 金额: $ 26万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1336941&HistoricalAwards=false
• 关键词: Predictive; Computational; Tools; Biomass; Fast

1336941SubramaniamThe importance of sustainable energy generation from renewable sources that minimizes environmental impact is now globally recognized, and is a national priority. This project focuses on fundamental scientific understanding of reacting gas-solid flows that is needed to overcome technological barriers for the successful development of one such renewable energy source, namely, the production of biofuels. Predictive computational models of biomass fast pyrolysis are needed for large-scale production of biofuel using fluidized-bed reactors. State-of-the-art computational codes for fluidized-bed reactors use simplistic models for average reaction rates as well as heat and mass transfer correlations developed in the late 1970s, which recent research reveals are problematic. To address this shortcoming, high-fidelity direct numerical simulations will be used in this project to gain fundamental understanding of reacting gas-solid flow. This fundamental understanding will result in physics-based models that will be implemented into a popular open-source multi-fluid code MFIX. The improved and validated model for reacting gas-solid flow can be used by industry to evaluate and optimize fluidized-bed reactors for fast pyrolysis of biomass. These improvements will also result in more accurate simulation of fluidized beds for chemical looping combustion and CO2 capture using dry sorbents. The PI has coordinated a successful recruitment and retention program entitled Women in Mechanical Engineering, and both PIs will continue ongoing efforts to train graduate and undergraduate women and minority students as part of this research project.

1336941Subramaniam 利用可再生能源生产可持续能源并最大限度地减少环境影响的重要性现已得到全球认可，并且已成为国家优先事项。该项目侧重于对气固流反应的基本科学理解，这是克服成功开发一种可再生能源（即生物燃料生产）所需的技术障碍。使用流化床反应器大规模生产生物燃料需要生物质快速热解的预测计算模型。最先进的流化床反应器计算代码使用 20 世纪 70 年代末开发的平均反应速率以及传热传质相关性的简单模型，但最近的研究表明这些模型存在问题。为了解决这个缺点，该项目将使用高保真直接数值模拟来获得对反应气固流的基本了解。这种基本理解将产生基于物理的模型，并将其实现到流行的开源多流体代码 MFIX 中。经过改进和验证的气固流反应模型可用于工业界评估和优化生物质快速热解的流化床反应器。这些改进还将导致更准确地模拟化学循环燃烧和使用干吸附剂捕获二氧化碳的流化床。 PI 协调了一项名为“机械工程领域的女性”的成功招聘和保留计划，作为该研究项目的一部分，两位 PI 将继续努力培训研究生和本科生女性以及少数族裔学生。