1. Multi-Objective Optimization of Managed Aquifer Recharge
- Author
-
Catalin Stefan and Aybulat Fatkhutdinov
- Subjects
geography ,Mathematical optimization ,geography.geographical_feature_category ,Groundwater flow ,Computer science ,Water injection (oil production) ,0208 environmental biotechnology ,02 engineering and technology ,Groundwater recharge ,Multi-objective optimization ,020801 environmental engineering ,Aquifer properties ,Pareto optimal ,Computers in Earth Sciences ,Groundwater ,Water Science and Technology ,Water well - Abstract
This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady state and transient scenarios. The steady state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global (the non-dominated sorting genetic algorithm [NSGA-2]) and local (the Nelder-Mead downhill simplex search algorithms). The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties, and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared.
- Published
- 2018