Multi-objective optimization for free-phase LNAPL recovery using evolutionary computation algorithms

Abstract

A 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.Key words free product LNAPL recovery; multi-objective optimization; Pareto front; genetic algorithm; differential evolution; particle swarm optimization Optimisation multi-objectif utilisant des algorithmes d'évolution pour la récupération de LLPNA libres Résumé Nous présentons une méthode d'optimisation non-linéaire et multi-objectif visant à maximiser la récupération de liquides légers en phase non aqueuse (LLPNA) tout en minimisant les coûts d'exploitation, en introduisant une nouvelle stratégie de périodes optimales de pompage et de pause alternées. Ce procédé permet l'écoulement de davantage de pétrole vers les puits d'extraction, et assure une élimination maximum de produit à la fin de la période de dépollution tout en minimisant l'extraction d'eaux souterraines. La méthodologie présen-tée ici combine FEHM, un modèle d'écoulement souterrain multiphasique simulant le transport des LLPNA, avec trois algorithmes d'évolution: l'algorithme génétique (AG), l'algorithme à évolution différentielle (ED) et l'optimisation par essaims particulaires (OEP). Cette stratégie de récupération a été testée à l'aide des données d'un site situé près d'Athènes, en Grèce. Les solutions de l'OEP et l'ED étaient très similaires tandis que celle de l'AG était moins intéressante, alors que le temps d'exécution était à peu près le même pour tous les algorithmes. L'algorithme le plus performant (OEP) a été choisi pour se rapprocher du front de Pareto optimal, méthode qui offre plusieurs options aux décideurs. Lorsque la stratégie optimale est mise en oeuvre, même si une quantité importante de LLPNA est récupérée, on observe cependant la propagation d'un panache de LLPNA.Mots clefs récupération de LLPNA li...

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