Pump-and-treat optimization using analytic element method flow models

• Published in: Advances in water resources Vol. 29; no. 5; pp. 760 - 775
• Main Authors: Matott, L. Shawn, Rabideau, Alan J., Craig, James R.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2006; Elsevier Science
• Subjects: algorithms; Analytic element; analytic element method; contaminant plumes; contaminants; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Groundwater; groundwater contamination; Hydrogeology; hydrologic models; Hydrology. Hydrogeology; mathematical models; Optimization; optimization algorithms; Particle swarm; particle swarm optimization; pollutants; Pollution, environment geology; Pump-and-treat; pump-and-treat optimization; remediation; Analytic element; Pump-and-treat; Groundwater; Particle swarm; Optimization; experimental studies; algorithms; models; cost; software; finite difference analysis; plumes; ground water; finite element analysis; pollution; contamination; pumping; remediation; hydraulic conductivity; aquifers; optimization; particles
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2005.07.009

Plume containment using pump-and-treat (PAT) technology continues to be a popular remediation technique for sites with extensive groundwater contamination. As such, optimization of PAT systems, where cost is minimized subject to various remediation constraints, is the focus of an important and growing body of research. While previous pump-and-treat optimization (PATO) studies have used discretized (finite element or finite difference) flow models, the present study examines the use of analytic element method (AEM) flow models. In a series of numerical experiments, two PATO problems adapted from the literature are optimized using a multi-algorithmic optimization software package coupled with an AEM flow model. The experiments apply several different optimization algorithms and explore the use of various pump-and-treat cost and constraint formulations. The results demonstrate that AEM models can be used to optimize the number, locations and pumping rates of wells in a pump-and-treat containment system. Furthermore, the results illustrate that a total outflux constraint placed along the plume boundary can be used to enforce plume containment. Such constraints are shown to be efficient and reliable alternatives to conventional particle tracking and gradient control techniques. Finally, the particle swarm optimization (PSO) technique is identified as an effective algorithm for solving pump-and-treat optimization problems. A parallel version of the PSO algorithm is shown to have linear speedup, suggesting that the algorithm is suitable for application to problems that are computationally demanding and involve large numbers of wells.