Multiobjective Optimization of Pump-and-Treat-Based Optimal Multilayer Aquifer Remediation Design with Flexible Remediation Time

Singh, Thangjam Somchand; Chakrabarty, Dibakar

doi:10.1061/(asce)he.1943-5584.0000327

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…The problem can thus be formulated as determining the location and number of wells as well as the required pumping rates at these wells such that the cost is minimum and the desired quality levels are maintained. Several studies used EAs for solving the groundwater remediation problems via the simulation-optimization framework such as GA (Huang & Mayer 1997;Wang & Zheng 1997;Sun & Zheng 1999;Smalley et al 2000;Yoon & Shoemaker 2001;Zheng & Wang 2002;Babbar & Minsker 2006;Wu et al 2006;Park et al 2007;Bayer et al 2008;Seyedpour et al 2019), SA (Kobayashi et al 2008); MOGA (Erickson et al 2002;Mantoglou & Kourakos 2007;Singh et al 2008), NSGA-II (Singh & Chakrabarty 2011;Ouyang et al 2017), etc. More details of these EAs applications are also elaborated in Table 9.…”

Section: Applications In Reservoir Operation and Irrigation Systemsmentioning

confidence: 99%

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Reddy

Kumar

2020

H2Open Journal

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During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

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Section: Applications In Reservoir Operation and Irrigation Systemsmentioning

confidence: 99%

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Reddy

Kumar

2020

H2Open Journal

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“…To understand the response of the groundwater system to different remediation strategies, various numerical methods such as finite difference method (FDM) (He et al, 2017;Yang et al, 2018a), finite element method (FEM) (Ghafouri and Darabi, 2007;Esfahani and Datta, 2018), meshless method (Boddula and Eldho, 2017;Mategaonkar et al, 2018;Seyedpour et al, 2019) and software packages like MODFLOW (Singh and Chakrabarty, 2011;Joswig et al, 2017) have been used. Different methods are used to solve the groundwater remediation optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimizing pump-and-treat method by considering important remediation objectives

et al. 2022

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The efficiency of groundwater remediation by pump-and-treat (PAT) method is affected by several components. The most important of these components include the pumping wells location, pumping rate, and remediation period. In this research, hybrid optimization-simulation models were developed to find the appropriate groundwater remediation strategy by PAT method. The GA-FEM and NSGA-II-FEM models were used to solve four optimization problems for a hypothetical and real aquifer. These optimization problems were investigated from one objective problem to a four-objectives problem. In the multi-objective problems, in each step, one objective function is added to the previous set of objective functions. In the one-objective case, the objective function was defined as minimizing the contaminant concentration by pumping at a constant rate, while in the two-objectives problem, minimizing the drawdown of groundwater head by pumping at a constant rate was added. In the three-objectives problem, the pumping rate was variable and the mean pumping rate from all the wells is minimized. Finally, minimizing the remediation period is added in the four-objective case. The results indicated that locating the pumping wells in the path of the contaminant flow and close to the source improves the efficiency of the PAT system. The wells with higher pumping rates would be in the path of contamination flow and the wells with lower pumping rates should be located in nodes near the Dirichlet boundary. It is concluded that the remediation period in the hypothetical and real aquifer cannot be less than almost 3000 and 760 days, respectively. Finally, it can be said, the most important component in choosing the proper PAT strategy is the proper location of pumping wells.

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“…In literature, different formulations exist with different complexity, depending on the transport processes and the contaminant considered. The formulations range from the evaluation of the number of pore volumes that are necessary to flush in order to reach the clean-up target concentrations [16,25], numerical models set up for site-specific conditions or specific processes [26][27][28][29], and numerical models coupled with genetic algorithms [30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Analytical and Numerical Methods for a Preliminary Assessment of the Remediation Time of Pump and Treat Systems

Antelmi

Renoldi

Alberti

2020

Water

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Several remediation technologies are currently used to address groundwater pollution. “Pump and treat” (P&T) is probably one of the most widely applied, being a process where contaminated groundwater is extracted from the subsurface by pumping and then treated before it is discharged or reinjected into the aquifer. Despite being a very adaptable technology, groundwater remediation is often achieved in long and unsustainable times because of limitations due to the hydrogeological setting and contaminant properties. Therefore, the cost–benefit analysis over time results in an inefficient system and a preliminary evaluation of the clean-up time is crucial. The aim of the paper is to compare, in an integrated manner, the application of some models to estimate the time to compliance of a P&T system in relation to the specific hydrogeological condition. Analytical solutions are analyzed and applied to an industrial site and to a synthetic case. For both cases, batch flushing and the advection-dispersion-retardation (ADR) model underestimate remediation times comparing the results to real or simulated monitoring data, whereas the Square Root model provided more reliable remediation times. Finally, for the synthetic case, the reliability of analytical approaches and the effects of matrix diffusion are tested on the basis of a numerical groundwater transport model specifically implemented, which confirm the results of the analytical methods and the strong influence of the matrix diffusion on the results.

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Multiobjective Optimization of Pump-and-Treat-Based Optimal Multilayer Aquifer Remediation Design with Flexible Remediation Time

Cited by 14 publications

References 25 publications

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Optimizing pump-and-treat method by considering important remediation objectives

Analytical and Numerical Methods for a Preliminary Assessment of the Remediation Time of Pump and Treat Systems

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