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.
