H23G-1649
Development of a Control Optimization System for Real Time Monitoring of Managed Aquifer Recharge and Recovery Systems Using Intelligent Sensors
Tuesday, 15 December 2015
Poster Hall (Moscone South)
Kathleen M Smits1, Zachary William Drumheller1, Jonghyun Harry Lee2, Tissa H Illangasekare3, Julia Regnery4 and Peter K Kitanidis2, (1)Colorado School of Mines, Golden, CO, United States, (2)Stanford University, Stanford, CA, United States, (3)Colorado School of Mines, Department of Civil and & Environmental Engineering, Golden, CO, United States, (4)Colorado School of Mines, Civil and Environmental Engineering, Golden, CO, United States
Abstract:
Aquifers around the world show troubling signs of irreversible depletion and seawater intrusion as climate change, population growth, and urbanization lead to reduced natural recharge rates and overuse. Scientists and engineers have begun to revisit the technology of managed aquifer recharge and recovery (MAR) as a means to increase the reliability of the diminishing and increasingly variable groundwater supply. Unfortunately, MAR systems remain wrought with operational challenges related to the quality and quantity of recharged and recovered water stemming from a lack of data-driven, real-time control. This research seeks to develop and validate a general simulation-based control optimization algorithm that relies on real-time data collected though embedded sensors that can be used to ease the operational challenges of MAR facilities. Experiments to validate the control algorithm were conducted at the laboratory scale in a two-dimensional synthetic aquifer under both homogeneous and heterogeneous packing configurations. The synthetic aquifer used well characterized technical sands and the electrical conductivity signal of an inorganic conservative tracer as a surrogate measure for water quality. The synthetic aquifer was outfitted with an array of sensors and an autonomous pumping system. Experimental results verified the feasibility of the approach and suggested that the system can improve the operation of MAR facilities. The dynamic parameter inversion reduced the average error between the simulated and observed pressures between 12.5 and 71.4%. The control optimization algorithm ran smoothly and generated optimal control decisions. Overall, results suggest that with some improvements to the inversion and interpolation algorithms, which can be further advanced through testing with laboratory experiments using sensors, the concept can successfully improve the operation of MAR facilities.
