NRT-HDR: Data-Driven Sustainable Engineering for a Circular Economy

NRT-HDR：数据驱动的循环经济可持续工程

• 批准号: 2021871
• 负责人: Elke Rundensteiner
• 金额: $ 299.93万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021871&HistoricalAwards=false
• 关键词: NRT; HDR; Data; Driven; Sustainable

The current ‘take-make-waste’ economic model relies on the irreversible conversion of non-renewable raw materials to products. The health, environmental, and economic burden of hazardous byproducts of this process falls disproportionately on the most vulnerable communities. To drive change towards a sustainable future and improved outcomes for all, there is a need to train future leaders in the circular economy that maximizes use of renewable materials, increases the efficiency of manufacturing processes, upcycles wastes into valuable byproducts, and minimizes the harm of irreducible waste streams. Despite much effort, educational models training students to become experts in increasingly isolated fields and without regard for communication with stakeholders have not yielded the desired results. This National Science Foundation Research Traineeship award to Worcester Polytechnic Institute will address this demand by establishing the CEDAR Program (Circular Economy and Data Analytics Engineering Research for Sustainability) to educate an outward facing community of scholars versed in data-driven sustainable engineering solutions to the challenges preventing transition to a circular economy. The CEDAR traineeship anticipates providing a comprehensive training opportunity for one hundred and twenty (120) graduate students, including thirty (30) funded PhD-level trainees, in disciplines ranging from chemical sciences (chemistry, biology, physics, engineering) to data sciences (computing, business analytics, statistics, mathematics) with a convergent focus on advancement of circular economies. The CEDAR traineeship interweaves technical and social considerations and focuses on the well-being of our society. This integrated traineeship in data and chemical sciences will develop leaders ready to transform industrial chemical processes towards a green and circular economy by addressing fundamental problems in three critical areas: (1) development of atom and energy efficient processes, (2) understanding and deploying upcycling, and (3) minimizing harm from the irreducible waste streams that cannot be avoided. Communication and collaboration across traditional disciplinary boundaries are fostered via professional workshops and convergent research seminars team-taught by instructors from distinct disciplines. To drive change, trainees learn leadership skills by participating in multi-way exchanges of ideas and advocacy with audiences ranging from the general public to academia, government, and industry. Connections with industry made by participating in CEDAR community retreats, CEDAR External Advisory Board meetings and in the internship program will strengthen trainee professional and career development, and infuse talent into the sustainable economy. The CEDAR program will tackle the underrepresentation of women and minorities in STEM disciplines by employing innovative minority-centric recruiting strategies using peer ambassadors to cultivate relationships with faculty and potential students at the program’s partner universities, creating pipelines to CEDAR. Focus on social, ethical and business implications will further help recruit a diverse cohort of students and train well-rounded leaders that are advocates for the sustainable circular economy. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

目前的“取废”经济模式依赖于将不可再生的原材料不可逆转地转化为产品。这一过程的有害副产品的健康、环境和经济负担不成比例地落在最脆弱的社区身上。为了推动变革走向可持续的未来并改善所有人的成果，有必要培训循环经济领域的未来领导者，最大限度地利用可再生材料，提高制造过程的效率，将废物升级为有价值的副产品，并最大限度地减少不可减少的废物流的危害。尽管付出了很多努力，但培养学生成为日益孤立的领域的专家，而不考虑与利益相关者的沟通的教育模式并没有取得预期的结果。伍斯特理工学院将通过建立CEDAR项目（循环经济和可持续发展数据分析工程研究）来解决这一需求，以教育一个面向外部的学者社区，他们精通数据驱动的可持续工程解决方案，以应对阻止向循环经济过渡的挑战。雪松培训计划预计将为120名研究生提供全面的培训机会，其中包括30名获得资助的博士学位学员，学科范围从化学科学（化学、生物学、物理学、工程学）到数据科学（计算、商业分析、统计学、数学），重点是推进循环经济。雪松培训项目将技术和社会因素结合在一起，关注我们社会的福祉。这种数据和化学科学的综合培训将培养领导者，通过解决三个关键领域的基本问题，准备将工业化学过程转变为绿色和循环经济：(1)原子和能源效率过程的发展，(2)理解和部署升级回收，(3)最大限度地减少不可避免的不可减少的废物流的危害。跨越传统学科界限的沟通和合作通过专业研讨会和融合的研究研讨会促进，由不同学科的教师团队授课。为了推动变革，受训者通过与公众、学术界、政府和工业界的听众进行多种方式的思想交流和倡导，学习领导技能。通过参加雪松社区务虚会、雪松外部顾问委员会会议和实习项目与行业建立联系，将加强学员的专业和职业发展，并为可持续经济注入人才。CEDAR项目将通过采用创新的以少数族裔为中心的招聘策略来解决女性和少数族裔在STEM学科中的代表性不足的问题，利用同伴大使来培养与该项目合作大学的教师和潜在学生的关系，建立通往CEDAR的渠道。关注社会、道德和商业影响将进一步帮助招收多样化的学生群体，并培养全面发展的领导者，倡导可持续循环经济。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。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。

项目成果

Dynamics of Photoexcitations in Ti 3 C 2 T z , Mo 2 Ti 2 C 3 T z , and Nb 2 CT z 2D MXenes

Ti 3 C 2 T z 、Mo 2 Ti 2 C 3 T z 和 Nb 2 CT z 2D MXene 中的光激发动力学

• DOI: 10.1109/irmmw-thz50927.2022.9895648
• 发表时间: 2022
• 期刊: Millimeter and Terahertz Waves
• 作者: Colin-Ulloa, Erika;Fitzgerald, Andrew M.;Mann, Javery;Montazeri, Kiana;Barsoum, Michel W.;Ngo, Ken A.;Uzarski, Joshua R.;Titova, Lyubov V.
• 通讯作者: Titova, Lyubov V.

Accuracy of Predictions Made by Machine Learned Models for Biocrude Yields Obtained from Hydrothermal Liquefaction of Organic Wastes

• DOI: 10.1016/j.cej.2022.136013
• 发表时间: 2022-03
• 期刊: Chemical Engineering Journal
• 影响因子: 15.1
• 作者: Feng Cheng;Elizabeth R. Belden;Wenjing Li;Muntasir Shahabuddin;R. Paffenroth;M. Timko

Measuring the Uncertainty of Environmental Good Preferences with Bayesian Deep Learning

• DOI: 10.1145/3524458.3547250
• 发表时间: 2022-09
• 期刊: Proceedings of the 2022 ACM Conference on Information Technology for Social Good
• 作者: Ricardo Flores;M. L. Tlachac;Elke A. Rundensteiner

Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System

• DOI: 10.1109/icdm51629.2021.00108
• 发表时间: 2021-12
• 期刊: 2021 IEEE International Conference on Data Mining (ICDM)
• 作者: Ziming Zhang;Guojun Wu;Yanhua Li;Yun Yue;Xun Zhou

Ultrafast dynamics of plasmons and free carriers in 2D MXenes

• DOI: 10.1364/cleo_fs.2023.ff1g.2
• 发表时间: 2023-05
• 期刊: 2023 Conference on Lasers and Electro-Optics (CLEO)
• 作者: L. Titova;Erika Colin-Ulloa;Andrew M. Fitzgerald;Kiana Montazeri;Javery A. Mann;Varun Natu;Ken Ngo;J. Uzarski;M. Barsoum