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NSF2026: EAGER: Spatio-Temporal Design of Techno-Ecological Synergies for a World without Waste and Resilient Landscapes

NSF2026: EAGER: Spatio-Temporal Design of Techno-Ecological Synergies for a World without Waste and Resilient Landscapes
NSF2026:EAGER:技术生态协同效应的时空设计,打造一个没有废物和有弹性景观的世界
批准号:
2036982
负责人:
Bhavik Bakshi
金额:
$29.99万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-01-01 至 2023-12-31

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中文摘要
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英文摘要
With support from the CBET/ENG Environmental Sustainability program and the NSF 2026 Fund Program in the Office of Integrated Activities, the investigators are researching the ecological capacity to provide goods and services in the face of demands imposed by a technological society. To meet sustainability goals, most engineers design and operate manufacturing processes to minimize resource use and emissions, but they may not account, for example, for the capacity of a watershed to provide fresh water to all users (including non-human users) or of the atmosphere to absorb emitted CO2. Similarly, economists may exclude consideration of the impact on ecosystems. The vision of this research is that through appropriate design, human activities can explicitly account for the provisions supplied by ecosystems, and can be designed to respect ecosystem limits. The research seeks to provide a framework for designing industries and ecosystems simultaneously to operate in a mutually beneficial or synergistic manner. The resulting Techno-Ecological Synergies (TES) will rely on designing ecosystems of the future, that in fundamental concept include the built environment, to enrich the NSF2026 Idea Machine winning entries of a "World without Waste," and "Large Landscape Resilience by Design."TES design will be formulated as an optimization problem. Novel and innovative approaches for developing designs at relevant spatial and temporal scales are proposed for solving the optimization problem. One such innovation to be realized is the the development of physics-based surrogate models with deep neural networks to capture the spatio-temporal variation of pollutant concentration in a selected region for a point source under various geographical, land cover, and meteorological conditions, embracing uncertainty issues. Advanced and innovative stochastic and/or dynamic programming methods will be employed to obtain TES designs. As a test case, the developed TES approach will be applied to a power plant near Cincinnati and vegetation on the surrounding landscape. For this case study, conventional and TES designs will be compared in terms of their contribution to reducing waste at the landscape and life cycle scales. To assess large landscape resilience for conventional and TES designs, future climate change scenarios will be simulated and landscape resilience compared for conventional and TES designs in terms of regional water availability and air quality. This approach will assess the benefits of seeking synergies with nature through the TES framework. The results of this work are targeted to lay the foundation for further work toward the convergence of disciplines including ecology, sociology, economics, public policy, statistics, environmental science, and engineering. The aspiration of the TES approach is to see, with time, practical implementation of TES on the scale of industrial, urban, and agro-ecological landscape networks.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.
期刊论文(7)
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会议论文
DOI: 10.1016/j.ifacol.2022.07.558
发表时间: 2022
期刊: IFAC-PapersOnLine
影响因子: --
作者: [Congwen Lu;J. Paulson]
通讯作者: Congwen Lu;J. Paulson
DOI: 10.1021/acssuschemeng.1c05617
发表时间: 2021-12-13
期刊: ACS SUSTAINABLE CHEMISTRY & ENGINEERING
影响因子: 8.4
作者: [Shah, Utkarsh, Bakshi, Bhavik R.]
通讯作者: Bakshi, Bhavik R.
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers*
使用贝叶斯方法进行乘子交替方向法的多智能体黑盒优化*
DOI: 10.1016/j.ifacol.2023.10.1155
发表时间: 2023
期刊: IFAC-PapersOnLine
影响因子: --
作者: [Krishnamoorthy, Dinesh, Paulson, Joel A.]
通讯作者: Paulson, Joel A.
Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning
使用主动深度学习对混合整数非线性系统的不变集进行可扩展估计
DOI: 10.1109/cdc51059.2022.9993131
发表时间: 2022
期刊: IEEE Conference on Decision and Control
影响因子: --
作者: [Bonzanini, Angelo D., Paulson, Joel A., Makrygiorgos, Georgios, Mesbah, Ali]
通讯作者: Mesbah, Ali
7
    NSF2026: Convergence Around a Sustainable World Without Waste
    • 批准号:
      2404686
    • 项目类别:
      Standard Grant
    • 资助金额:
      $10.0万
    • 财政年份:
      2023
    • 负责人:
      Bhavik Bakshi
    • 依托单位:
    NSF2026: Convergence Around a Sustainable World Without Waste
    • 批准号:
      2027185
    • 项目类别:
      Standard Grant
    • 资助金额:
      $10.0万
    • 财政年份:
      2020
    • 负责人:
      Bhavik Bakshi
    • 依托单位:
    EFRI E3P: Sustainable and Circular Engineering for the Elimination of End-of-life Plastics: A Framework for Assessment, Design, and Innovation
    • 批准号:
      2029397
    • 项目类别:
      Standard Grant
    • 资助金额:
      $200.0万
    • 财政年份:
      2020
    • 负责人:
      Bhavik Bakshi
    • 依托单位:
    Including Ecosystems in Process Design and Life Cycle Assessment for Environmental Sustainability and Innovation
    • 批准号:
      1804943
    • 项目类别:
      Standard Grant
    • 资助金额:
      $36.0万
    • 财政年份:
      2018
    • 负责人:
      Bhavik Bakshi
    • 依托单位:
    海外基金