Abstract
The present study implements a stochastic optimization technique to optimally manage freshwater pumping from coastal aquifers. Our simulations utilize the well-known sharp interface model for saltwater intrusion in coastal aquifers together with its known analytical solution. The objective is to maximize the total volume of freshwater pumped by the wells from the aquifer while, at the same time, protecting the aquifer from saltwater intrusion. In the direction of dealing with this problem in real time, the ALOPEX stochastic optimization method is used, to optimize the pumping rates of the wells, coupled with a penalty-based strategy that keeps the saltwater front at a safe distance from the wells. Several numerical optimization results, that simulate a known real aquifer case, are presented. The results explore the computational performance of the chosen stochastic optimization method as well as its abilities to manage freshwater pumping in real aquifer environments.
Highlights
The present studySaltwater intrusion in freshwater aquifers is a major concern in coastal areas around the globe
As we have already mentioned, the model we consider for the saltwater intrusion problem in coastal unconfined aquifers of finite size, is based on the sharp-interface simplification, which assumes that there is no mixing zone between freshwater and saltwater inside the aquifer, and on the Ghyben-Herzberg relation, which at steady state estimates the position of the interface assuming that horizontally floating freshwater floats over static saltwater
This work presents a new version of the ALOPEX stochastic optimization algorithm and studies its performance when applied on analytical models of saltwater intrusion of coastal aquifers
Summary
Saltwater intrusion in freshwater aquifers is a major concern in coastal areas around the globe. Stochastic, heuristic and adaptive search algorithms, like ALOPEX, SA or Genetic Algorithms, have been developed and suggested for problems with nonlinear objectives and constraints, mainly to overcome the difficulties gradient-based methods are facing with local extrema. Adding to this the ease of implementation and low computational cost of the ALOPEX stochastic algorithm, we believe that it might be potentially useful to the engineering community
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