Improving Seawater Barrier Operation with Simulation Optimization in Southern California

Abstract

A calibrated simulation model is linked with two optimization models to investigate alternatives for enhancing seawater intrusion barrier operations for the Alamitos Barrier Project in Los Angeles. Two types of management problems are analyzed, the optimal scheduling problem (OSP) and the optimal well location problem. The OSP objective is to minimize the total injected water subject to constraints on the state variables: Hydraulic head and chloride concentration at target locations. Two OSP formulations are considered, a pure hydraulic gradient formulation, and a combined hydraulic and transport formulation. When considering all 43 injection wells over a five-year planning horizon, the simulation-optimization model could not significantly improve upon the assigned initial injection rates. However, if a subset of the injection wells is exclusively considered, more favorable injection policies are obtained where less water is injected, compared with either the mean or annual mean derived from the historical record. Next, a genetic algorithm (GA) is linked with the calibrated simulation model to determine the locations of new injection wells that maximize one of two alternative fitness functions, which quantify barrier improvement. Parallel processing is implemented to accelerate the convergence of the GA.