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

生物炭是热化学生物质转化的一种富含碳的固体副产品。它在食品、能源和水系统中有潜在的应用。该项目旨在通过以下方式推进潜在的生物炭应用：(1)使用人工智能（机器学习）方法来预测生物质原料、转化途径和生物炭应用的各种组合的过程数据和生命周期评估（LCA）；(2)构建食物-能量-水（FEW）关系中生物炭系统建模和分析的综合框架；(3)通过不同地理、时间和社会经济背景下的现实案例研究来展示该框架。教育和外展目标包括(1)通过开发关于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)开发课堂和网络课程，提供培训和职业发展机会，将本科生和研究生的研究与教育活动相结合；(3)建立一个国际学者网络，研究生物炭可持续性和跨学科研究社区，长期目标是形成一个国际研究和教育计划。该项目旨在通过整合LCA、技术经济分析（TEA）、地理信息系统（GIS）、机器学习和动态建模，弥合生物炭生产和有效应用方面的知识差距，提高FEW的可持续性。了解不同生物质基质对不同生物炭应用对环境、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。集成先进的建模工具，包括LCA、GIS和机器学习，这些工具通常用于不同的学科，是该方法的一个重要特征。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。该奖项反映了美国国家科学基金会的法定使命，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.