Collaborative Research: ECO-CBET: Coupled homogeneous and heterogeneous processes for an environmentally sustainable lignin-first biorefinery

合作研究：ECO-CBET：环境可持续的木质素优先生物精炼厂的均质和异质耦合工艺

• 批准号: 2218938
• 负责人: Andreas Heyden
• 金额: $ 86万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218938&HistoricalAwards=false
• 关键词: Collaborative; Research; ECO; CBET; Coupled

A future, environmentally sustainable chemical industry will likely utilize woody biomass as a renewable carbon source. In many biomass conversion processes, chemical reactions in the fluid phase interact with reactions on the surfaces of catalytic materials during forcing conditions of high pressure and elevated temperature. In a recently proposed wood biorefinery, the lignin component of wood is preferentially dissolved and depolymerized to monomers in the fluid phase. Subsequently, the generated monomers need to be stabilized on a catalytic material to avoid formation of undesirable compounds that cannot be further processed. To improve the environmental sustainability and profitability of such a wood biorefinery, this project will accelerate both the lignin depolymerization and the product stabilization by tuning the solvent fluid properties and designing catalysts that are optimized for the reaction environment. Given the complexity of the lignin reductive catalytic fractionation reaction system, it is hardly possible to directly design the catalyst and fluid properties for woody biomass. Because lignin structure varies for different biomass sources, this project will study the coupled homogeneous and heterogeneous processes under realistic reaction conditions for lignin model compounds instead of actual solid biomass. By working with model reactants that contain the critical bond linkages and whose conversion products are representative of the conversion products of woody biomass, the system complexity is dramatically reduced without fundamentally changing the depolymerization and reductive product stabilization chemistry, enabling the concurrent design of fluid properties and catalyst. Overall, this project constitutes a case study of how to use modern data science tools for tightly integrating fundamental computational and experimental research with life cycle assessment, techno-economic analysis, and regular validation of design principles for the conversion of poplar to expedite the development of an environmentally and economically sustainable biorefinery. The education plan includes postgraduate, graduate, undergraduate, and K-12 students. They will be trained in broad research areas while emphasizing the ability to work collaboratively in addressing difficult environmental problems. To engage all team members in unfamiliar fields, students will give tutorial presentations that are aimed at a diverse audience and that can later be adapted into short interactive web-based lectures and web modules on topics such as life cycle assessment and machine learning. Project participants will broaden the participation of underrepresented groups, women, and those with non-traditional backgrounds in science and engineering by engaging in outreach and training programs.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.

未来，环境可持续的化学工业可能会利用木质生物质作为可再生碳源。在许多生物质转化过程中，在高压和高温的强制条件下，液相中的化学反应与催化材料表面上的反应相互作用。在最近提出的木材生物精炼厂中，木材的木质素成分优先在流体相中溶解并解聚为单体。随后，生成的单体需要在催化材料上稳定，以避免形成无法进一步加工的不良化合物。为了提高木材生物精炼厂的环境可持续性和盈利能力，该项目将通过调整溶剂流体特性和设计针对反应环境优化的催化剂来加速木质素解聚和产品稳定性。鉴于木质素还原催化分馏反应系统的复杂性，直接设计木质生物质的催化剂和流体性能几乎是不可能的。由于不同生物质来源的木质素结构各不相同，因此该项目将研究木质素模型化合物（而不是实际固体生物质）在真实反应条件下的均质和异质耦合过程。通过使用包含关键键键且其转化产物代表木质生物质转化产物的模型反应物，系统复杂性显着降低，而不会从根本上改变解聚和还原产物稳定化学，从而能够同时设计流体性质和催化剂。总体而言，该项目构成了一个案例研究，说明如何使用现代数据科学工具将基础计算和实验研究与生命周期评估、技术经济分析以及杨树转化设计原则的定期验证紧密结合起来，以加快杨树转化的发展。环境和经济上可持续的生物精炼厂。教育计划包括研究生、研究生、本科生和K-12学生。他们将接受广泛研究领域的培训，同时强调协作解决困难环境问题的能力。为了让所有团队成员参与不熟悉的领域，学生将针对不同的受众进行教程演示，随后可以将其改编成简短的交互式网络讲座和网络模块，主题涉及生命周期评估和机器学习等。项目参与者将通过参与外展和培训计划，扩大代表性不足的群体、女性以及具有非传统科学和工程背景的群体的参与。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。