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利用可再生能源进行可持续能源发电，将对环境的影响降至最低，这一重要性现已得到全球认可，并已成为国家优先事项。该项目侧重于对反应气固流动的基本科学理解，这是克服成功开发一种这种可再生能源的技术障碍所必需的，即生产生物燃料。利用沸腾床反应器大规模生产生物燃料，需要建立生物质快速热解预测计算模型。最先进的沸腾床反应器的计算代码使用的是20世纪70年代末开发的平均反应速率以及热量和质量传递关联式的简化模型，最近的研究表明这是有问题的。为了解决这一缺点，本项目将使用高保真直接数值模拟，以获得对反应气固两相流动的基本了解。这种基本的理解将导致基于物理的模型，这些模型将被实施到流行的开源多流体代码MFIX中。改进和验证的气固两相反应模型可用于工业评价和优化生物质快速热解的沸腾床反应器。这些改进还将导致更准确地模拟使用干式吸着剂的化学循环燃烧和二氧化碳捕集的流态化床。作为这一研究项目的一部分，两家私人投资机构将继续努力培训研究生和本科生以及少数族裔学生。