| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | August 6, 2014 |
| Latest Amendment Date: | August 6, 2014 |
| Award Number: | 1442773 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Hamilton
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2014 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $1,199,600.00 |
| Total Awarded Amount to Date: | $1,199,600.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1109 GEDDES AVE STE 3300 ANN ARBOR MI US 48109-1015 (734)763-6438 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2350 Hayward Street Ann Arbor MI US 48109-2125 |
| 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): | CyberSEES |
| 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): | 47.070 |
ABSTRACT
ABSTRACT
Our nation's practices in managing the growing amounts of Municipal Solid Waste (MSW) that are generated every year are unsustainable. The majority of MSW generated every year is still disposed of in landfills despite national and international efforts aimed to increase recycling. In modern landfills, MSW is treated as a material to be isolated and contained. Current MSW management strategies cause sub-optimal degradation of landfill waste resulting in the generation of biogases (primarily methane and carbon dioxide) that are mostly flared, vented or leaked to the atmosphere where they remain as greenhouse gases (GHG). As a result, landfills represent the second largest anthropogenic source of methane in the US. Fortunately, MSW has high energy potential that remains virtually untapped as a national energy resource. The overarching goal of this research is to revolutionize how MSW is managed to provide a transformative means of extracting utility-scale energy from waste using next-generation facilities to be termed Sustainable Energy Reactor Facilities (SERFs). This paradigm-shift is only recently possible through the adoption of innovative computing technologies such as high-performance computing for multi-domain process modeling, low-cost autonomous sensor networks, and unmanned autonomous vehicles (UAVs), all synergistically integrated within a customized cyber-environment. This integration of in-situ SERF observation with high-performance computing allows the energy generation capacity of SERF to be maximized resulting in lower cost energy production with a dramatic reduction in GHG and carbon footprint compared to traditional dry-tomb landfills.
SERFs will be designed with two objectives: maximize energy recovery and minimize environmental impact. The explicit objective of maximizing energy generation will necessitate a significant deviation from modern MSW management practices which are based on empirical methods. SERFs are only possible through environmental sensing and modeling of physical-chemical-biological processes occurring within a landfill. At the core of the SERF technology will be complex, multi-domain computational performance models (CPMs) that require execution in near real-time and consider these processes over varying spatial and temporal scales. CPM is enabled by high-performance computing platforms that can update and execute the CPMs using in-situ observations of MSW processes collected by field deployed wireless sensor networks. Model uncertainty can be further reduced through the introduction of ground-based and aerial mobile sensing platforms whose paths are optimally planned using CPM model uncertainty and platform constraints (e.g., energy) within the same minimizing objective function. With CPM models updated, energy generation can be predicted by SERF owners with energy extraction maximized by the injection of septage and leachate into the SERF. A multidisciplinary team of researchers with expertise in landfill design and modeling and researchers from computer science will work in close collaboration with an Industrial Advisory Board (IAB) of major waste industry stakeholders (i.e., waste management companies, industry consultants, and government regulators). Research, educational and outreach activities are integrated through a virtual "hub". The IAB will provide guidance on decisions pertaining to the project's research and education activities. Activities are planned to promote education of the society-at large, integrate undergraduate and graduate education and research, and nurture a well-equipped future domestic workforce to manage and advance SERF technology. An award-winning journalist will also be engaged in training engineering students in efficiently communicating with broad audiences complex engineering matters, and in evaluating the proposed web-based resources (videos and animations). In addition, the research team will partner with a team of Chinese researchers leading to international technology, education and cultural exchanges.
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.
With the promulgation of Subtitle D RCRA regulations in the late 1980s-early 1990s, landfills have transitioned from uncontrolled waste dumps to sophisticated infrastructure systems designed with the objective to contain Municipal Solid Waste (MSW) and protect the environment from, primarily, groundwater contamination. This research project promotes a re-think of MSW from a hazard to be contained, to a resource to be harvested. More specifically, this research project leveraged recent advances in our understanding of the anaerobic degradation processes that are ongoing in all MSW landfills at variable rates, with the objective to optimize these processes, efficiently generate biogas, (which consists primarily of methane and carbon dioxide and is a sustainable energy source), and minimize undesirable biogas leaks from landfills.
A computational performance model was developed that captures the coupling of the complex hydraulic, biochemical, physical and mechanical processes that occur at a landfill site during anaerobic biodegradation. The model builds on previous efforts in the scientific community, and is also calibrated against comprehensive large-size, controlled and heavily instrumented laboratory experiments, where the key parameters of the coupled processes were measured. The model can be used as the ?brain? of the optimization process, and in its field implementation is informed by field measurements to provide an updated assessment of the biodegradation process.
Key to this monitoring operation is an understanding of undesired biogas leaks that may be occurring at a landfill site, despite the operation of a biogas collection system. These leaks not only contribute to climate change as methane is a particularly potent greenhouse gas, but also represent unnecessary energy losses. Presently, landfill emission monitoring is not conducted often and is focused on regulatory compliance, as opposed to being an integral part of landfill operation optimization and monitoring. Monitoring is achieved through an integrated surface monitoring system that consists of solar-powered, autonomous flux chambers that measure continuously methane fluxes, temperature, moisture content at selected locations of the landfill; and are complemented by regular monitoring of the entire landfill surface through robotic platforms (unmanned aerial vehicles and ground-based robots) that are equipped with multiple sensors, including methane sensors, optical cameras and infrared cameras. The robotic platforms provide spatially distributed data at high resolution. The fusion of the monitoring data from the different monitoring platforms and sensors provides an unprecedented and holistic understanding of biogas leakages across the entire landfill. The field data collected provides important insights about the methane leakages through landfills, which are found to be significantly affected by climate and weather conditions.
The computational performance model and the autonomous platforms provide important capabilities to improve landfill operations and re-envision the way we handle municipal solid waste that can enhance environmental stewardship and can expand the energy portfolio of our country.
Last Modified: 12/24/2019
Modified by: Dimitrios Zekkos
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