2006 AGU Fall Meeting Poster No. H41B-0420

1. http://cesep.mines.edu 2006 AGU Fall Meeting Poster No. H41B-0420 Command Base Mote Data Output Excite sensor (apply voltage) Continuous Plume Monitoring and Model Calibration using a wireless sensor network: Proof of Concept in Intermediate-Scale Tank Test Lisa Porta (lporta@mines.edu) & Tissa H. Illangasekare (tissa@mines.edu) Center for Experimental Study of Subsurface Environmental Processes (CESEP), Division of Environmental Sciences and Engineering, Colorado School of Mines, Golden, CO Philip Loden & Qi Han , Department of Mathematical and Computer Sciences, Colorado School of Mines, Golden, CO Denney Liptak &Anura Jayasumana, Dept. of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO TEST BED ABSTRACT 2) SCOPE OF PROJECT 5) PRELIMINARY EXPERIMENTAL RESULTS Dimensions: L = 244 cm H = 40 cm W = 8 cm The current practice for accurate monitoring of subsurface plumes using samples collected at monitoring wells is often limited by cost and time constraints. This approach becomes impractical for continuous monitoring of plumes that grow with time. With the development of new sensor technologies and wireless sensor networks (WSNs), the potential exists to create innovative and efficient subsurface data collection for plume monitoring. The goal is to automatically collect data from sensors and wirelessly transmit the data to computer platforms where inversion codes and forward simulation models reside. This data can then be used to continuously monitor and update model parameters for the prediction of expected plume behavior. Many technological and operational challenges related to sensor placement and distribution, automation of real-time datacollection, wireless communication, and modeling have to be overcome before the implementation of complex plume monitoring systems. This preliminary proof of concept demonstration study assesses this technology using an aquifer test bed constructed in an intermediate scale tank. The test system includes a set of ten conductivity probes individually connected to wireless sensor boards (motes). The tank was packed using five well-characterized silica sands to represent a heterogeneous aquifer. Bromide tracer was injected into a steady flow field and tracer concentrations at different points in the tank were measured with conductivity sensors via the motes. Eventually, inverse modeling will be used to determine subsurface parameters needed for predictive modeling. This preliminary study is the starting step in the development of a more complex wireless sensing communication system to be used in field applications involving remediation design, performance assessment, risk analysis and exposure assessment. Conductivity sensors packed within the tank Initial flow model for tank packing, and sensor placement in different sands A 200 mg/L NaBr (sodium bromide) solution was injected into a well upstream (about 10 cm) from the first sensor for 40 min. The slug of tracer migrated through the tank with the water flow while the sensors reported the measured change in electrical conductivity. Data were wirelessly communicated to the computer via the base mote, which sends the reading commands at specified time intervals. Migration of dye tracer through tank The breakthrough curves illustrate the movement of tracer in the tank as captured by the sensors. AUTOMATED DATA COLLECTION SUMMARY 1) BACKGROUND • The motes were effectively used in combination with electrical conductivity sensors in a two-dimensional physical test bed • Real-time, quantitative data were obtained to create tracer breakthrough curves • Sensor calibration issues did not permit model calibration at this time • FUTURE WORK • Develop a different sand packing scheme with a random heterogeneous field and smaller sand lenses to get more valuable data for inverse modeling • Build a screen around the sensors to mimic a well • Extend wireless communication between the motes • Selective data sampling: the motes should report only the sensor values which are useful for model calibration. This can be done by setting a threshold value for the motes to report to the computer • Real-time inverse modeling: as new data are collected from the sensors over time, the model can be recalibrated more accurately 3) CURRENT FOCUS: PROOF OF CONCEPT • Existing sampling technologies • Expensive, involving laboratory analytical methods • Time consuming with respect to collection and analysis • Fail to capture real time plume migration when the concentrations change • Often not optimally designed with respect to best sampling locations • Development of a physical test bed for wireless sensing application in subsurface plume monitoring and evaluation of wireless sensing equipment efficiency • Sensor to mote connection • Sensor calibration and sensitivity analysis • Development of test bed (two-dimensional) with initial modeling • Tracer experiments for wireless sensor testing • Wireless automated data gathering, storage and analysis • Model calibration • Upscaling of methodology (three-dimensional) User interface • Wireless Sensor Networks (WSNs) • Consist of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions at different locations. Applications include military, health, traffic, ecosystem monitoring. Each node in a sensor network is equipped with a radiotransceiver, a small microcontroller, and an energy source, usually a battery (or solar power in the field). • Why use WSNs? • Reduce wiring for sensor distribution • over a large area • Real time plume monitoring • Intelligent data gathering The user types in commands (such as time interval of sampling and sensors to be excited) which the base mote will wirelessly communicate to the tank motes. These apply a voltage to the sensors which respond with an instantaneous reading of electrical conductivity in the sand. Sampling times are programmable for minimum user involvement. 4) EXPERIMENTAL METHODS • ACKNOWLEDGEMENTS • Project funded by Army Research Office: Terrestrial Sciences, Environmental Sciences Division. • Dr. Russell Harmon, Senior Program Manager The test bed was built out of plexiglass walls attached to a metal plate. A steady flow was maintained in the heterogeneous media formed by five well characterized sands. Ten sensors were distributed in the tank at different depths and in turn connected to five different motes attached on a rack above the tank.