Project Description
PFI:BIC – A Smart, Flexible, Large-Scale Sensing and Response Service System (LASSaRESS) for Monitoring and Management of Ground, Air and Waterborne Contaminants
This is a collaborative project with Duke’s Pratt School of Engineering also partnering with industry PFT Technology, LLC. Oil leakage from underground cable systems leads to environmental damage and economic loss. World-wide, the impact is estimated at $2 billion in direct economic losses. When environmental and productivity costs are considered, the total harm from underground oil leakage is estimated to be much higher. The goal of this project is to develop a cost-effective, scalable, smart underground oil leak location system that can be modified to serve a host of applications in leak detection and pollution measurement including applications in gas leak detection, water leak detection, and pollution monitoring. The techniques developed through this project have the potential to improve future generations of distributed networked sensors through application of cloud computing technologies. This new smart system, when implemented to detect underground leaks, and more generally, pollutants is expected to make significant, positive environmental impacts. Given the team’s past successful work in underground oil leak detection and mitigation, an immediate impact in scaling oil leak detection is expected. At the same time, the mini-mass spectrometers can in the future be configured to monitor many contaminants, thereby addressing a variety of environmental challenges.
Project objectives are: 1) build the core smart system components, 2) develop core algorithms and build the smart system test bed, and 3) validate the test bed functionality in the field. First, mini-mass spectrometers will be fabricated, and a dynamically configurable cloud computing network will be developed with the goal of connecting multiple mini-mass spectrometers into an analytical system to collect leak source data. Collected data will be analyzed and leakage locations will be identified based on distributed sensor readings using an algorithm developed to dynamically optimize sensor positioning and identify leak location. Finally, the smart system will be implemented in the field to monitor a controlled, low-level, perfluorocarbon tracer leak. The expected outcome of this program is a low cost, self-configurable, highly flexible, mobile system that can locate leaks and contaminants with minimal human intervention.
RENCI role is to design the cloud-based data collection, analysis and visualization pipeline for the project.
