CAREER: Biochar Systems for Sustainable Applications in the Food-Energy-Water Nexus

生物炭是生物质热化学转化的富碳固体副产品。它在食品、能源和水系统中具有潜在的应用。该项目旨在通过（1）使用人工智能来推进潜在的生物炭应用生物质原料、转化途径和生物炭应用的各种组合的预测过程数据和生命周期评估（LCA）的（机器学习）方法;（2）建立用于模拟和分析食物-能源-水（FEW）关系中的生物炭系统的综合框架;以及（3）通过不同地理、时间和社会经济背景下的真实案例研究来展示该框架。教育和外联目标包括：（1）通过开发一个关于FEW和生物炭可持续性的多媒体包，吸引代表性不足的学生进入STEM领域;（2）开发课堂和在线课程，并提供培训和专业发展机会，以整合本科生和研究生的研究和教育活动;（3）建立一个国际学者网络，研究FEW、生物炭可持续性、和跨学科的研究社区，该项目的目标是弥合生物炭生产和有效利用生物炭的知识差距，通过整合生命周期评估、技术经济分析（TEA）、地理信息系统（GIS）、机器学习和动态建模，提高FEW的可持续性。了解不同生物质基质在不同生物炭应用中对环境、经济和社区的影响，将为在不同社会经济、气候变化和资源限制条件下进一步设计和实施大规模生物炭系统奠定基础。该方法的一个重要特点是集成了不同学科中常用的先进建模工具，包括LCA、GIS和机器学习。通过整合工程、环境科学、自然科学和数据科学的先进建模方法，该项目旨在展示跨学科研究如何创造更好的社会成果。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Biochar is a carbon-rich solid byproduct of thermochemical biomass conversions. It has potential applications in food, energy, and water systems. This project aims to advance potential biochar applications by (1) using artificial intelligence (machine learning) approaches to predict process data and life cycle assessment (LCA) of various combinations of biomass feedstocks, conversion pathways, and applications of biochar; (2) building an integrated framework for modeling and analysis of biochar systems in the food-energy-water (FEW) nexus; and (3) demonstrating the framework through real-world case studies in different geographic, temporal, and socioeconomic contexts. The educational and outreach objectives include (1) attracting underrepresented students to STEM fields by developing a multimedia package for FEW and biochar sustainability; (2) developing both in-class and online courses and providing training and professional development opportunities to integrate research and education activities for undergraduate and graduate students; (3) developing an international network of scholars for FEW, biochar sustainability, and interdisciplinary research communities with a long-term goal of forming an international research and education program.This project targets bridging knowledge gaps for biochar production and effective applications in enhancing FEW sustainability by integrating LCA, technico-economic analysis (TEA), Geographic Information System (GIS), machine learning, and dynamic modeling. Understanding the impacts of using various biomass substrates for different biochar applications on the environment, economics, and communities will lay a foundation for the further design and implementation of large-scale biochar systems under different socioeconomic, climate change, and resource limiting conditions. Integration of advanced modeling tools including LCA, GIS, and machine learning that are commonly used in different disciplines is an important feature of the approach. Through integration of advanced modeling methods from engineering, environmental science, natural science, and data science, this project seeks to demonstrate how transdisciplinary research can create improved societal outcomes.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.