Simulation and optimization technologies for petroleum waste management and remediation process control

Qin, Xiaosheng; Huang, Guohe; He, Li

doi:10.1016/j.jenvman.2008.07.002

Cited by 69 publications

(20 citation statements)

References 252 publications

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“…The stopping criterion can be either in the form of a maximum number of generations or a satisfactory fitness level. Even if the algorithm has terminated due to a maximum number of generations, a satisfactory solution cannot be always guaranteed (Qin et al 2009). …”

Section: Optimization Problemmentioning

confidence: 99%

“…When LNAPL reaches the capillary zone it accumulates, creating an oil table that rests directly above the water table, often causing a depression on the water table due to its weight. Finally, some of the LNAPL partitions into the groundwater through dissolution causing longterm groundwater contamination (Qin et al 2009). …”

Section: Introductionmentioning

confidence: 99%

“…The disadvantage of using linear optimization methods is that the linearity assumption is often unrealistic in field applications. While conventional nonlinear methods (classical, dynamic, mixedinteger) have a strong mathematical representation, they are not very attractive for real-world applications due to the large requirements in computational effort related to complex nonlinear manipulations (Qin et al 2009). Specific to the problems of pump-and-treat, or free product recovery, the functions of various groundwater system components (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…This renders evolutionary algorithms more attractive for solving this type of problems. The interested reader is referred to Mayer et al (2002) and Qin et al (2009) for an extensive literature review of the recent developments associated with optimization techniques applied to site remediation.…”

Section: Introductionmentioning

confidence: 99%

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

Dokou

Karatzas

2013

Hydrological Sciences Journal

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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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Section: Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dokou

Karatzas

2013

Hydrological Sciences Journal

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“…Giacobbo et al [16] have investigated the feasibility of using genetic algorithms for estimating the parameters of a groundwater contaminant transport model. Additionally, Qin et al [17] have given a comprehensive review on recent developments, advancements, challenges, and barriers associated with simulation and optimization techniques in supporting process control of petroleum waste management and site remediation.…”

Section: Introductionmentioning

confidence: 99%

A reliability engineering study for a near surface radioactive waste repository using genetic algorithm

Aguiar

Damaso

2010

WIT Transactions on Ecology and the Environment

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Infiltration of water into a waste disposal facility and into the waste region is the main factor inducing the release of radionuclides from a disposal facility. Since infiltrating water flow is dependent on the natural percolation at the site and the performance of engineered barriers, its prediction requires modelling of unsaturated water flow through intact or partially/completely failed components of engineered barriers. These include the cover systems, concrete vault, backfill, waste forms, and overpacks. This paper aims to carry out a performance study regarding a near surface repository in terms of reliability engineering. It is assumed that surface water infiltrates through the barriers reaching the matrix where radionuclides are contained, thus releasing them into the environment. The repository consists of a set of barriers which are considered saturated porous medium, except geosphere. A genetic algorithm is employed to establish an optimized configuration to the structure of the repository, by determining the thicknesses of barriers and waste packages arrangement, taking into account the engineering and radiological constraints. Such restrictions aim to keep the risks associated with the migration of radionuclides at a low level.

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Advantages of the analytic element method for the solution of groundwater management problems

2011

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Abstract:In the simulation-optimization approach, a coupled optimization and groundwater flow/transport model is used to solve groundwater management problems. The efficiency of the numerical method, which is used to simulate the groundwater flow, is one the major reason to obtain the best solution for a management problem. This study was carried out to examine the advantages of the analytic element method (AEM) in the simulation-optimization approach, for the solution of groundwater management problems. For this study, the AEM and finite difference method (FDM) based flow models were developed and coupled with the particle swarm optimization (PSO)-based optimization model. Furthermore, the AEM-PSO and FDM-PSO models developed were applied in hypothetical as well as real field conditions to address groundwater management problems and the results were compared. For the real field situation, the models developed were applied to the Dore River basin in France to minimize the installation and operational cost of new pumping wells taking the location and discharge of the pumping wells as decision variables. The constraints of the problem were identified with the help of stakeholders and water authority officials. The AEM flow model was developed to facilitate the management model particularly when at each iteration, the optimization model calls for a simulation model to calculate the values of groundwater heads. The results show that, at some points, the AEM-PSO model is efficient in identifying the optimal location of wells and consequently results in optimal costs, sometimes difficult when using the FDM.

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Simulation and optimization technologies for petroleum waste management and remediation process control

Cited by 69 publications

References 252 publications

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

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

A reliability engineering study for a near surface radioactive waste repository using genetic algorithm

Advantages of the analytic element method for the solution of groundwater management problems

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