Solving economic and environmental optimal control in dumping of sewage with a flexible and parallel evolutionary computation

Abstract

We solve a state constrained optimal control problem with parallel implementations of an intelligent optimization system based on evolutionary self-learning named Flexible Evolution Agent. The target of the problem to solve is to find the optimal locations of liquid waste dumping sites in offshore waters, so the concentration of a considered pollutant must not exceed a pre-established value, either at a specific distance from the shore or in different protected areas, as can be fish hatcheries. Simultaneously, we also minimize the investment costs such as hydraulic infrastructures. We consider a 3D finite volumes Taylor-Galerkin version for the pollutant concentration simulation. Numerical results with new parallel evolutionary implementations are shown.