Optimal Decision Making Algorithm for Managed Aquifer Recharge and Recovery Operation Using Near Real-Time Data: Benchtop Scale Laboratory Demonstration

Aquifers show troubling signs of irreversible depletion as climate change, population growth, and urbanization lead to reduced natural recharge rates and overuse. One strategy to sustain the groundwater supply is to recharge aquifers artificially with reclaimed water or stormwater via managed aquifer recharge and recovery (MAR) systems. 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 paper presents a laboratory scale proof-of-concept study that demonstrates the capability of a real-time, simulation-based control optimization algorithm to ease the operational challenges of MAR systems. Central to the algorithm is a model that simulates water flow and transport of dissolved chemical constituents in the aquifer. The algorithm compensates for model parameter uncertainty by continually collecting data from a network of sensors embedded within the aquifer. At regular intervals the sensor data is fed into an inversion algorithm, which calibrates the uncertain parameters and generates the initial conditions required to model the system behavior. The calibrated model is then incorporated into a genetic algorithm that executes simulations and determines the best management action, for example, the optimal pumping policy for current aquifer management goals. Experiments to calibrate and validate the simulation-optimization algorithm were conducted in a small two-dimensional synthetic aquifer under both homogeneous and heterogeneous packing configurations. Results from initial experiments validated the feasibility of the approach and suggested that our system could improve the operation of full-scale MAR facilities.