Hybrid‐Optimization Algorithm for the Management of a Conjunctive‐Use Project and Well Field Design

Abstract

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.