Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithmsMulti-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-202013enfrA nonlinear, multi-objective optimization methodology is presented that seeks to maximize free product recovery of light non-aqueous phase liquids (LNAPLs) while minimizing operation cost, by introducing the novel concept of optimal alternating pumping and resting periods. This process allows more oil to flow towards the extraction wells, ensuring maximum free product removal at the end of the remediation period with minimum groundwater extraction. The methodology presented here combines FEHM (Finite Element Heat and Mass transfer code), a multiphase groundwater model that simulates LNAPL transport, with three evolutionary algorithms: the genetic algorithm (GA), the differential evolution (DE) algorithm and the particle swarm optimization (PSO) algorithm. The proposed optimal free-phase recovery strategy was tested using data from a field site, located near Athens, Greece. The PSO and DE solutions were very similar, while that provided by the GA was inferior, although the computation time was roughly the same for all algorithms. One of the most efficient algorithms (PSO) was chosen to approximate the optimal Pareto front, a method that provides multiple options to decision makers. When the optimal strategy is implemented, although a significant amount of LNAPL free product is captured, a spreading of the LNAPL plume occurs.Δημοσίευση σε επιστημονικό περιοδικό http://creativecommons.org/licenses/by/4.0/Hydrological Sciences Journal358671-685 Dokou Zoi Δοκου Ζωη Karatzas Giorgos Καρατζας Γιωργος Taylor & Francis free product LNAPL recovery multi-objective optimization Pareto front genetic algorithm differential evolution particle swarm optimization