CyberSEES: TYPE 2: Sustainably Unlocking Energy from Municipal Solid Waste Using a Sensor-Driven Cyber-Infrastructure Framework

Cyber​​SEES：类型 2：使用传感器驱动的网络基础设施框架可持续地从城市固体废物中释放能源

• 批准号: 1442773
• 负责人: Dimitrios Zekkos
• 金额: $ 119.96万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1442773&HistoricalAwards=false
• 关键词: CyberSEES; TYPE; Sustainably; Unlocking; Energy

ABSTRACTOur 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.

Abstractour Nation在管理每年生成的市政固体废物（MSW）量不断增长的实践是不可持续的。尽管国家和国际努力旨在增加回收利用，但每年大多数MSW每年产生的大多数人仍在垃圾填埋场中处置。在现代垃圾填埋场中，MSW被视为要隔离和包含的材料。当前的MSW管理策略导致垃圾填埋废物的亚最佳降解，导致沼气酶（主要是甲烷和二氧化碳）的产生，这些降解主要是爆发，排气或泄漏到它们作为温室气体（GHG）的大气中。结果，垃圾填埋场代表了美国第二大的人为甲烷。幸运的是，MSW具有很高的能源潜力，实际上尚未被视为国家能源。这项研究的总体目标是彻底改变MSW的方式提供了一种变革性的方法，即使用下一代设施从废物中提取公用事业规模的能源，以称为可持续能源反应堆设施（SELFS）。直到最近，通过采用创新计算技术，例如用于多域工艺建模，低成本自主传感器网络和无人驾驶的自动驾驶汽车（UAV），所有这些范式迁移才是可能的，所有这些都可以在定制的Cyber​​-Encrironment中进行协同集成。 与传统的干炸弹垃圾填埋场相比，这种原位农奴观察与高性能计算的整合使农奴的能量产生能力可最大化，从而最大程度地减少GHG和碳足迹。农奴将采用两个目标设计：最大化能量回收并最大程度地减少环境影响。最大化能源产生的明确目标将需要与基于经验方法的现代MSW管理实践有重大偏差。 农奴仅通过对垃圾填埋场内发生的物理化学生物过程的环境感应和建模才有可能。农奴技术的核心将是复杂的多域计算绩效模型（CPM），这些模型（CPM）需要实时执行，并在不同的空间和时间尺度上考虑这些过程。 CPM是通过高性能计算平台启用的，可以使用现场部署的无线传感器网络收集的MSW流程进行现场观察，可以更新和执行CPMS。通过引入地面和空中移动传感平台，可以进一步降低模型不确定性，这些平台在相同的最小化目标函数中使用CPM模型不确定性和平台约束（例如能量）对路径进行最佳计划。随着CPM模型的更新，可以通过将分离和渗出物注入到农奴中最大化能量提取的农奴所有者可以预测能量。具有垃圾填埋设计和建模专业知识的研究人员的多学科团队以及计算机科学的研究人员将与主要废物行业利益相关者（即废物管理公司，行业顾问和政府监管机构）的工业顾问委员会（IAB）密切合作。研究，教育和外展活动是通过虚拟“中心”整合的。 IAB将为与项目的研究和教育活动有关的决策提供指导。计划促进社会的教育 - 大型，整合本科和研究生教育和研究，并培养设备齐全的未来家庭劳动力，以管理和推进农奴技术。屡获殊荣的记者还将参与培训工程专业的学生，​​以有效地与广泛的受众群体复杂的工程事务进行交流，并评估拟议的基于Web的资源（视频和动画）。此外，研究小组将与一组中国研究人员合作，导致国际技术，教育和文化交流。