| NSF Org: |
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems |
| Recipient: |
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| Initial Amendment Date: | July 1, 2016 |
| Latest Amendment Date: | July 1, 2016 |
| Award Number: | 1643244 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Raymond Adomaitis
CBET Division of Chemical, Bioengineering, Environmental, and Transport Systems ENG Directorate for Engineering |
| Start Date: | June 30, 2016 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $512,000.00 |
| Total Awarded Amount to Date: | $512,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
318 Olin Hall Ithaca NY US 14850-2820 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Proc Sys, Reac Eng & Mol Therm |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Abstract - You, 1554558
Public interest in carbon and water footprints, ecosystem degradation and resource depletion, are just some of the environmental issues that drive the process industries to exercise greater responsibility for the environment in the design and operations of their manufacturing systems. This increasing emphasis on sustainability has driven a shift in process systems design and optimization from the traditional, economics-oriented methods towards methods that balance environmental protection in addition to economic profitability. This project is aimed at incorporating sustainability principles systematically and effectively into the design and optimization of process systems. Previously, such efforts were limited to the process boundary, while now, the boundary has expanded to include the life cycle. This broader perspective reduces the chance of unintended externalities by shifting the problem outside a narrow process boundary. Research challenges arise from the large size and scope of the problem, the multiscale nature of models and data, the potential presence of multiple non-cooperative decision makers, and the high degrees of uncertainty across temporal scales.
The objective of this project is to address the fundamental issues associated with multi-scale sustainability optimization of process systems design and operations decisions. This work will augment process systems engineering tools on supply chain optimization, sustainable engineering and the treatment of uncertainties. The research activities are to develop: (1) a systematic study and a novel multi-scale life cycle process systems optimization (LCPSO) framework that accounts for and optimizes the direct and indirect environmental impacts from the foreground process systems scale and from the background economy scale through the integration of process systems optimization with process-based and input-output-based models; (2) a novel and transformative LCPSO framework based on game theoretical modeling for non-cooperative supply chains with multiple individual decision-makers to account for the life cycle environmental sustainability and economic objectives of individual decision makers; and (3) a new approach for quantifying the role of uncertainties at multiple temporal scales for LCPSO.
The research has the potential to improve the economic competitiveness and environmental sustainability of the process industries. The results will offer insights to identify sustainable strategies for (re-)designing process systems and improving operational practices. The proposed optimization algorithms could result in benefits beyond the chemical engineering community and find applications in other arenas where systematic decision-making is pursued. The results will be integrated into education and outreach activities in the following areas: (1) incorporation of research findings into "Chemical Engineering Design Projects" and "Process Optimization" courses; (2) enhancing undergraduate students' participation in research; (3) working with a local, predominantly minority high school for an interactive STEM Saturdays outreach program; and (4) development of a "Distinguished Junior Researcher Seminar Series" by inviting junior research scholars in related fields to deliver seminars at the PI's institution.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Throughout this research project, we systematically investigated and addressed three major research tasks, namely, (a) a systematic study and a novel multi-scale life cycle process systems optimization framework that accounts for and optimizes the direct and indirect environmental impacts from the foreground process systems scale and from the background economy scale through the integration of process systems optimization with process-based and input-output-based models; (b) a novel life cycle optimization framework based on game theoretical modeling for non-cooperative supply chains with multiple individual decision-makers to account for the life cycle environmental sustainability and economic objectives of individual decision-makers; and (c) a new approach for quantifying the role of uncertainties at multiple temporal scales for life cycle optimization. At the fundamental level, we developed novel systems methodologies on life cycle optimization, including the state-of-the-art integrated hybrid life cycle optimization framework, the consequential life cycle optimization concept, and the game-theory-based life cycle optimization approach. We also pioneered the fundamental study on data-driven adaptive robust optimization for tackling uncertainty in various process operations and control problems. At the application level, we explored and extended the applications of the proposed lifecycle-based methodologies and data-driven optimization under uncertainty approaches for various applications, including chemical manufacturing, shale gas supply chain, biorefinery and biofuel systems, plastics recycling, bitcoin mining, solar cell recycling, and carbon-neutral hybrid energy systems (for Cornell campus and New York State, among others).
The proposed research methodologies and their innovative applications potentially improved the economic competitiveness and environmental sustainability of the process and energy industries. The results offer insights to identify sustainable strategies for (re-)designing process and energy systems and improving operational practices and control systems. The proposed optimization algorithms for tackling uncertainty using machine learning and robust optimization led to benefits beyond the chemical engineering community and found applications in other arenas where systematic decision-making is pursued (e.g., electric power systems, transportation, finance, etc.). The results were integrated into education and outreach activities in the following areas: (1) incorporation of research findings into undergraduate senior Process Design courses and graduate-level "Computational Optimization" and "Machine Learning and Data Analytics" courses; (2) enhancing undergraduate students' participation in research by engaging/funding around a dozen of undergraduate students, most of who had co-authored publications; and (3) successful development of a "Distinguished Junior Researcher Seminar Series" by inviting junior research scholars in related fields to deliver seminars at Northwestern University and Cornell University; most selected speakers of this program are now taking faculty positions in leading chemical engineering departments in the U.S.
Last Modified: 10/01/2022
Modified by: Fengqi You
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