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

我国在管理每年产生的越来越多的城市固体废物(MSW)方面的做法是不可持续的。每年产生的大部分生活垃圾仍然被丢弃在垃圾填埋场，尽管国家和国际努力旨在增加回收利用。在现代垃圾填埋场中，生活垃圾被视为一种被隔离和容纳的材料。目前的城市生活垃圾管理战略导致垃圾的次优降解，导致沼气(主要是甲烷和二氧化碳)的产生，这些沼气大多被燃烧、排放或泄漏到大气中，在那里它们仍然是温室气体(GHG)。因此，垃圾填埋场是美国第二大甲烷人为来源。幸运的是，城市生活垃圾具有很高的能源潜力，几乎仍然是一种未开发的国家能源资源。这项研究的总体目标是彻底改变城市生活垃圾的管理方式，利用被称为可持续能源反应堆设施(SERF)的下一代设施，提供从废物中提取公用事业规模的能源的变革性手段。这种范式转变直到最近才有可能通过采用创新的计算技术，如用于多领域流程建模的高性能计算、低成本自主传感器网络和无人驾驶车辆(UAV)，所有这些都协同集成在一个定制的网络环境中。这种将现场观测与高性能计算相结合的方式，可以最大限度地提高SERF的发电能力，从而降低能源生产成本，与传统的干坟墓垃圾填埋场相比，大大减少了温室气体和碳足迹。农奴系统的设计有两个目标：最大限度地回收能源和最大限度地减少对环境的影响。最大限度地提高能源产量的明确目标将需要与基于经验方法的现代生活垃圾管理做法发生重大偏离。农奴只能通过环境监测和对垃圾填埋场内发生的物理-化学-生物过程进行建模来实现。SERF技术的核心将是复杂的多域计算性能模型(CPM)，这些模型需要近实时执行，并在不同的空间和时间尺度上考虑这些过程。CPM是由高性能计算平台实现的，这些平台可以使用现场部署的无线传感器网络收集的城市垃圾过程的现场观测来更新和执行CPM。通过引入陆基和空中移动传感平台，可以进一步减少模型不确定性，这些平台的路径是使用CPM模型不确定性和平台约束(例如，能量)在相同的最小化目标函数内进行优化规划的。随着CPM模型的更新，农奴主可以通过向农奴中注入粪便和渗滤液来最大限度地提取能量，从而预测能量的产生。一个由具有垃圾填埋场设计和建模专长的研究人员和来自计算机科学的研究人员组成的多学科团队将与废物行业主要利益攸关方(即废物管理公司、行业顾问和政府监管机构)的工业咨询委员会(IAB)密切合作。研究、教育和外联活动通过一个虚拟的“枢纽”整合在一起。IAB将就与该项目的研究和教育活动有关的决策提供指导。计划开展的活动旨在促进社会的教育--将本科生和研究生的教育和研究结合起来，培养一支装备精良的未来国内劳动力队伍，以管理和提高农奴技术。一名获奖记者还将培训工程学学生与广大受众有效地交流复杂的工程问题，并评估拟议的网络资源(视频和动画)。此外，研究团队将与一个中国研究人员团队合作，领导国际技术、教育和文化交流。

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.