Abstract
A new surrogate-assisted optimization formulation for groundwater remediation design was developed. A stationary Eulerian travel time model was used in lieu of a conservative solute transport model. The decision variables of the management model are well locations and their flow rates. The objective function adjusts the residence time distribution between all pairs of injection-production wells in the remediation system. This goal is achieved by using the Lorenz coefficient as an effective metric to rank the relative efficiency of many remediation policies. A discrete adjoint solver was developed to provide the sensitivity of the objective function with respect to changes in decision variables. The quality management model was checked with simple solutions and then applied to hypothetical two- and three-dimensional test problems. The performance of the simulation-optimization approach was evaluated by comparing the initial and optimal remediation designs using an advective-dispersive solute transport simulator. This study shows that optimal designs simultaneously delay solute transport breakthrough at pumping wells and improve the sweep efficiency leading to smaller cleanup times. Well placement optimization in heterogeneous porous media was found to be more important than well rate optimization. Additionally, optimal designs based on two-dimensional models were found to be more optimistic suggesting a direct use of three-dimensional models in a simulation-optimization framework. The computational budget was drastically reduced because the proposed surrogate-based quality management model is generally cheaper than one single solute transport simulation. The introduced model could be used as a fast, but first-order, approximation method to estimate pump-and-treat capital remediation costs. The results show that physically based low-fidelity surrogate models are promising computational approaches to harness the power of quality management models for complex applications with practical relevance.
Highlights
Simulation-based optimization (S/O) approaches are increasingly used to support water resources management, policy analysis, economics and decision-making
While groundwater flow management models have advanced significantly, this is not the case for existing S/O approaches based on solute transport models
The available computational budget often falls short to accomplish the lengthy number of solute transport iterates before convergence of the S/O loop
Summary
Simulation-based optimization (S/O) approaches are increasingly used to support water resources management, policy analysis, economics and decision-making. A physically based surrogate model is a sound alternative for groundwater modeling practitioners because the underlying principles are easier to explain and understand This facilitates their acceptance by practicing engineers, water resources managers and stakeholders. The available computational budget often falls short to accomplish the lengthy number of solute transport iterates before convergence of the S/O loop To cope with this fundamental limitation, this paper introduces a physically based surrogate model to streamline the use of quality management models for complex aquifer systems. The performances of the optimal P&T system design were systematically validated This is achieved by a comparative analysis based on two solute transport simulations implemented with the initial and optimal decision variables, respectively.
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