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

摘要我国每年产生的城市固体废物（MSW）越来越多，其管理方式是不可持续的。尽管国家和国际努力增加回收利用，但每年产生的大部分城市固体废物仍然在垃圾填埋场处理。在现代垃圾填埋场中，MSW被视为要隔离和容纳的材料。目前的城市固体废物管理策略导致填埋废物的次优降解，导致产生沼气（主要是甲烷和二氧化碳），这些沼气大多被燃烧、排放或泄漏到大气中，并作为温室气体（GHG）留在大气中。因此，垃圾填埋场是美国第二大甲烷人为来源。幸运的是，城市固体废物具有很高的能源潜力，作为一种国家能源资源几乎尚未开发。这项研究的总体目标是彻底改变城市固体废物的管理方式，以提供一种变革性的手段，利用被称为可持续能源反应堆设施（SERF）的下一代设施从废物中提取公用事业规模的能源。这种范式转变只是最近才有可能通过采用创新的计算技术，如多域过程建模的高性能计算，低成本自主传感器网络和无人驾驶自动驾驶汽车（UAV），所有这些都协同集成在定制的网络环境中。 现场SERF观测与高性能计算的这种集成允许SERF的能量产生能力最大化，从而导致较低成本的能量生产，与传统的干墓垃圾填埋场相比，温室气体和碳足迹显著减少。SERF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环境影响。明确的目标，最大限度地提高能源发电将需要一个显着偏离现代城市固体废物管理的做法是基于经验的方法。 SERF只有通过对垃圾填埋场内发生的物理-化学-生物过程进行环境传感和建模才有可能。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由高性能计算平台实现，该平台可以使用现场部署的无线传感器网络收集的MSW过程的原位观察来更新和执行CPM。通过引入地面和空中移动的感测平台，可以进一步降低模型不确定性，这些平台的路径是使用CPM模型不确定性和平台约束（例如，在同一个最小化目标函数中。随着CPM模型的更新，SERF业主可以预测能源发电量，通过向SERF注入化粪池和渗滤液来最大限度地提取能源。一个由具有垃圾填埋场设计和建模专业知识的研究人员和计算机科学研究人员组成的多学科团队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）。研究、教育和外联活动通过一个虚拟的“中心”整合在一起。IAB将为项目的研究和教育活动提供决策指导。活动计划，以促进整个社会的教育，整合本科和研究生教育和研究，并培养一个装备精良的未来国内劳动力管理和推进SERF技术。一名获奖记者还将参与培训工程专业的学生，使他们能够与广泛的受众有效地沟通复杂的工程问题，并评估拟议的基于网络的资源（视频和动画）。此外，该研究团队将与中国研究人员团队合作，促进国际技术，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.