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Pump-and-Treat Ground-Water Remediation System Optimization
Abstract
A ground-water management model using a nonlinear programming algorithm was developed to find the minimum cost design of the combined pumping and treatment components of a pump-and-treat remediation system and includes the fixed costs of system construction and installation as well as operation and maintenance. The fixed-cost terms of the objective function are incorporated into the nonlinear programming formulation using a penalty coefficient method. Results of applying the model to an aquifer with homogeneous hydraulic conductivity show that a combined well field and treatment process model that includes fixed costs has a significant impact on the design and cost of these systems, reducing the cost by using fewer, larger-flow-rate wells. Previous pump-and-treat design formulations have resulted in systems with numerous, low-flow-rate wells due to the use of simplified cost functions that do not exhibit economies of scale or fixed costs. Two example aquifers with heterogeneous conductivity fields were also investigated, and system costs similar to the homogeneous case were obtained. However, the introduction of aquifer heterogeneity did affect the remediation design, for example, well locations and pumping rates. Trade-offs between total remediation system cost and cleanup standard, remediation period, and typical design parameters of air-stripping towers are examined through sensitivity analysis. Generally, the optimal injection concentration is found to be approximately 70–80% of the cleanup standard. Designs with remediation periods around 5–6 yr have the minimum cost for the case study presented in this paper. Remediation periods beyond 6 yr are not economically justified, since the tower operating costs become dominant and offset the reduction of capital cost.
... The pump and treat (PAT) method is widely-accepted practice for groundwater remediation (Matott et al., 2006). In the PAT method, the polluted water is extracted out of the ground by means of pumping wells and after purifying process, the water is again injected back into the ground or used for any other purposes (McKinney and Lin, 1996). The use of simulation-optimization (S-O) models are very common to remediate polluted groundwater by PAT method (Ahlfeld et al., 1986(Ahlfeld et al., , 1988Kuo et al., 1992;Rogers and Dowla, 1994;Bear and Sun, 1998;Erickson et al., 2002;Maskey et al., 2002;Ko et al., 2005;Matott et al., 2006;He et al., 2009;Mategaonkar and Eldho, 2012;Yang et al., 2013;Kazemzadeh-Parsi et al., 2014;Luo et al., 2014;Sreekanth et al., 2016). ...
... The pump and treat (PAT) method is mostly used for groundwater remediation. In the PAT system, polluted groundwater is extracted from the aquifer by means of pumping wells; the water is then biologically or chemically treated to eliminate harmful contaminants (McKinney and Lin 1996;Espinoza et al. 2005). In the past few decades, many coupled simulation-optimization models (S-O) have been developed for groundwater remediation by using the PAT method (Ahlfeld et al., 1988(Ahlfeld et al., , 1986Bear and Sun, 1998;Erickson et al., 2002;He et al., 2009;Kazemzadeh-Parsi et al., 2014;Ko et al., 2005;Kuo et al., 1992;Liao et al., 2015;Luo et al., 2014;Maskey et al., 2002;Matott et al., 2006;McKinney and Lin, 1996;Mategaonkar and Eldho, 2012;Rogers and Dowla, 1994;Wang and Zheng, 1997;Wang and Ahlfeld, 1994;Yang et al., 2013). ...
... In the PAT system, polluted groundwater is extracted from the aquifer by means of pumping wells; the water is then biologically or chemically treated to eliminate harmful contaminants (McKinney and Lin 1996;Espinoza et al. 2005). In the past few decades, many coupled simulation-optimization models (S-O) have been developed for groundwater remediation by using the PAT method (Ahlfeld et al., 1988(Ahlfeld et al., , 1986Bear and Sun, 1998;Erickson et al., 2002;He et al., 2009;Kazemzadeh-Parsi et al., 2014;Ko et al., 2005;Kuo et al., 1992;Liao et al., 2015;Luo et al., 2014;Maskey et al., 2002;Matott et al., 2006;McKinney and Lin, 1996;Mategaonkar and Eldho, 2012;Rogers and Dowla, 1994;Wang and Zheng, 1997;Wang and Ahlfeld, 1994;Yang et al., 2013). In many previous works, the steady state flow assumption seems to be very common and logical in the PAT studies (Erickson et al., 2002;Kazemzadeh-Parsi et al., 2014;Kuo et al., 1992;Matott et al., 2006;McKinney and Lin, 1996;Singh and Chakrabarty, 2010;Wang and Ahlfeld, 1994). ...
Groundwater is the most significant source of fresh water for a variety of uses, including industrial, irrigation, drinking and domestic purposes. Nowadays, excessive usage of groundwater resource is taking place, to meet water demand for industrial, agricultural and domestic uses. Nevertheless, excessive use of groundwater has resulted in the depletion of this natural resource. A gradual decline in groundwater quality is also taking place, as industrial, farming and domestic effluents entering into the hydrological cycle. To counteract groundwater resource depletion and deterioration, it is pertinent to understand the physics of groundwater flow and contaminant transport processes and to develop strategies for groundwater resources management and groundwater remediation.
Numerical techniques such as finite difference method (FDM) and finite element method (FEM) are commonly used for groundwater flow and transport simulation. However, the analytic element method (AEM) has certain capabilities which overcome the difficulties associated with grid-based algorithms. In AEM, only the hydrogeologic features in the domain are broken up into sections and entered into the model as input data. AEM eliminates the compromise between model resolution and size of the model area. Also, AEM generates very accurate hydraulic head at pumping well location, which in turn improves the quality of the groundwater management model. On the contrary, in FDM/FEM, the hydraulic head at the pumping well location is the averaged hydraulic head over the grid. In the particle tracking method to track particles at each time step, it is necessary to know the position of a particle as well as its velocity. AEM-based flow models compute continuous velocities over the entire aquifer domain, and hence for the reverse particle tracking (RPT) and random walk particle tracking (RWPT) simulation, there is no need to use any velocity interpolation schemes as generally required in FDM or FEM based models. Further, the Eulerian transport models, such as FDM/FEM based models are often plagued by numerical dispersion and artificial oscillations if spatial and temporal discretization criteria do not meet properly. As an alternative, the random walk particle tracking (RWPT) simulates the advection-dispersion equation in a different manner and it is completely free from the numerical dispersion. The analytic element method is amenable for reverse particle tracking or random walk particle tracking and they have various advantages as mentioned above.
In this context, the main scope of the present study is to develop groundwater flow and contaminant transport simulation models using analytic element method, reverse particle tracking, and random walk particle tracking and to couple the simulation models with efficient optimization algorithm such as cat swarm optimization to get the effective simulation-optimization model for groundwater management and remediation. In this study, an AEM-RPT model is developed by combining analytic element method with reverse particle tracking. The AEM-RPT model is used to delineate the time-related capture zone of well-field. Further, the AEM-RWPT model is developed by combining analytic element method with random walk particle tracking. The AEM-RWPT model is applied to simulate groundwater flow and contaminant transport processes (advection and hydrodynamic dispersion) of heterogeneous hypothetical and field aquifer. Furthermore, the accuracy and computational efficiency of the AEM-RWPT model is enhanced by combining it with kernel density estimator (KDE). Additional features are included in the AEM-RWPT-KDE model to simulate radioactive decay and linear adsorption isotherm. The AEM-RWPT-KDE model is effectively used to solve the advection-dispersion-reaction equation (ADRE). The effectiveness of the developed model is verified with MODFLOW-MT3DMS and found to be satisfactory.
Heuristic optimization technique, such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Harmony Search (HS), Tabu Search (TS) and Differential Evolution (DE) are most commonly used for various groundwater management and groundwater remediation studies. All these optimization algorithms have advantages as well as limitations. Among these optimization algorithms, the particle swarm optimization is very popular and relatively easy to implement. However, the particle swarm optimization (PSO) can be influenced by stagnation point problem and parameter convergence error. Recently, a relatively new swarm optimization technique, namely Cat Swarm Optimization (CSO) is gaining considerable attention in various engineering fields. In Cat swarm optimization (CSO), search operation takes place via two modes (Seeking and Tracking mode). So, in case the solution is trapped in stagnation point, then there are greater chances of escaping from stagnation point, via inertia term and seeking mode process. Considering, the recent popularity of CSO, in the present study the cat swarm optimization is considered to develop the optimization model for groundwater resources management and groundwater remediation design.
In this study, two new simulation-optimization (S-O) models for groundwater management (AEM-CSO and AEM-RPT-CSO) are developed by coupling analytic element method with reverse particle tracking and cat swarm optimization. The AEM-CSO model is applied for groundwater management in a hypothetical unconfined aquifer considering two objectives separately: maximization of the total pumping and minimization of the total pumping cost. Also, an attempt is made to minimize groundwater contamination risk through capture zone management of pumping wells by AEM-RPT-CSO model. Further, a coupled AEM-MOCSO model is also developed by coupling analytic element method with multiobjective cat swarm optimization (MOSCO). The AEM-MOCSO model is applied to a hypothetical unconfined aquifer by considering two objectives together: maximization of the total pumping and minimization of the total pumping cost.
There are significant challenges, to directly incorporate analytic element method and random walk particle tracking in an optimization model for groundwater remediation, as both of them are computationally expensive. To deal this issue, in this study, an artificial neural network (ANN) and cat swarm based surrogate simulation-optimization model are developed for groundwater remediation. The ANN mimics the behavior of AEM-RWPT-KDE model. The ANN-CSO model is applied to remediate a hypothetical and field case study. Further, a simulation-optimization model is developed for multiobjective groundwater remediation by coupling artificial neural network with multiobjective cat swarm optimization (MOCSO). Here also, the ANN model acts as a proxy simulator for AEM-RWPT-KDE model. The ANN-MOCSO model is applied to a hypothetical and field case study for multiobjective groundwater remediation showing the effectiveness of the developed model.
The present study shows that AEM, RPT, and RWPT based models are very effective in groundwater flow and transport simulation. When these models are coupled with an efficient optimization tool such as CSO, we get robust simulation-optimization models, which can be effectively used in groundwater management and remediation designs.
... Historically, for the past three decades, the coupled simulation-optimization approach were generally adopted for efficient groundwater remediation processes and applied successfully to a variety of management problems ( [3], [4], [5], [6]). ...
... A wide variety of optimization algorithms have been used to address the groundwater remediation system. It includes linear programming [8], nonlinear programming [3], Simulated annealing [9], Genetic algorithm ( [3], [10], [4]) and Tabu search [6] etc. Relatively new evolutionary algorithm, particle swarm optimization (PSO) proposed by Kennedy and Eberhart [11] is found to be very efficient and effective optimization technique in various engineering applications. Mattot et al. [12] did a comparative study by coupling different optimization techniques (GA, SA, Conjugate gradient (CG) and PSO) with analytic element method (AEM) based groundwater flow simulation method. ...
... A wide variety of optimization algorithms have been used to address the groundwater remediation system. It includes linear programming [8], nonlinear programming [3], Simulated annealing [9], Genetic algorithm ( [3], [10], [4]) and Tabu search [6] etc. Relatively new evolutionary algorithm, particle swarm optimization (PSO) proposed by Kennedy and Eberhart [11] is found to be very efficient and effective optimization technique in various engineering applications. Mattot et al. [12] did a comparative study by coupling different optimization techniques (GA, SA, Conjugate gradient (CG) and PSO) with analytic element method (AEM) based groundwater flow simulation method. ...
Groundwater contamination is a major problem in many parts of the world. Once contaminated, remediation of groundwater becomes a tedious, time consuming and expensive process. For the groundwater remediation, pump and treat (PAT) is one of the commonly used technique, in which the contaminated groundwater is pumped, treated and put back into the aquifer system or other sources. Simulation-Optimization (S/O) models are very useful in the appropriate design of an effective PAT system. In the S/O models, while simulation model helps to predict the spatial and temporal variation of the contamination plume, optimization models are used to minimize the cost of pumping. Due to non-linear, non-analytical nature of the groundwater flow and transport processes, the numerical methods are a must to solve the problem. In this study, a groundwater management model has been developed for groundwater remediation using PAT by coupling user friendly commercial software Groundwater Modeling System (GMS) and Particle swarm optimization (PSO). Groundwater flow and transport processes has been simulated by using MODFLOW and MT3DMS package available in GMS. Thereafter a MATLAB function has been developed to execute MODFLOW and MT3DMS in MATLAB environment. Further, MODFLOW and MT3DMS is coupled with particle swarm optimization (PSO) to develop an optimal groundwater remediation strategy. The proposed methodology is applied to a hypothetical homogeneous confined aquifer for the determination of minimum remediation cost. MODFLOW and MT3DMS were also coupled with Genetic Algorithm (GA) and results are compared with the result of PSO based model. It has been observed that the PSO based model gives much better result than GA based model with less computational time. Although, the problem considered is hypothetical, the proposed methodology can be very easily implemented for real field conditions.
Index terms - Groundwater remediation, Pump and treat (PAT), Simulation – optimization, Groundwater Modeling System (GMS), MODFLOW, MT3DMS, Particle Swarm Optimization (PSO),
... Physical heterogeneities, in terms of spatially variable hydraulic conductivity and porosity, have been incorporated into aquifer remediation design studies (Gailey and Gorelick 1993;Lee and Kitanidis 1996;McKinney and Lin 1996;Wang and Zheng 1997). On a whole Monitored natural attenuation of contaminated groundwater is increasingly being considered as a possible remedial solution, particularly since the alternative remedial options can be very expensive. ...
... In the field, these non-ideal conditions may locally hinder or enhance the transport of contaminants. Physical heterogeneities, in terms of spatially variable hydraulic conductivity and porosity, have been incorporated into aquifer remediation design studies (Wang and Zheng 1997;Lee and Kitanidis, 1996;McKinney and Lin 1996;Gailey and Gorelick 1993). Heterogeneity in chemical properties, such as the distribution coefficient, is also observed at the field scale (Mackay et al. 1986;MacIntyre et al. 1991); yet chemical variability is widely neglected during remediation design. ...
... A variety of optimization techniques have been applied to optimal groundwater remediation design problems. These include linear programming (Atwood and Gorelick 1985), non-linear programming (McKinney 1996), dynamic programming (Culver 1993), simulated annealing (Marryott 1993), and genetic algorithms. However the majority of these works relies on single objective formulations and assumes deterministic conditions. ...
Natural attenuation (NA) has recently emerged as a viable groundwater remediation technology at various petroleum contaminated sites in the United States. NA is a passive remedial approach that depends upon natural processes to degrade and dissipate petroleum constituents in soil and groundwater. Such natural processes include advection, sorption, diffusion, dispersion and biodegradation. Due to heterogeneous nature of most contaminated groundwater (GW) sites there exists uncertainty in subsurface system parameters. This study evaluates sensitivities of parameter uncertainty on the performance and design of remediation plans that use natural attenuation with active remediation. This analysis is completed by using an optimization tool combined a GW flow and contaminant transport simulation model. The Enhanced multi-objective Robust Genetic Algorithm (EMRGA) is the optimization tool used here for the simultaneous optimization of multiple conflicting objectives under parameter uncertainty.
The multi-objective optimization problem is to minimize the cost of the natural attenuation-active remediation system and minimize the maximum contaminant concentration at the end of the five-year remediation period under parameter uncertainty and heterogeneity. The optimization model is applied to a problem based on a field site, contaminated with benzene located in Eglin Air Force Base, Florida. The uncertain parameters considered in this study are hydraulic conductivity (K), hydraulic gradient (dH/dx) and first-order benzene decay rate (k) benzene degradation. The optimization problem is solved using fifteen cases with different combinations of uncertain parameters and degrees of uncertainty. In addition, selected designs from the evolved Pareto-optimal sets (trade-off curves) were further evaluated by Monte Carlo analysis.
Results show that as uncertainty in hydraulic conductivity increased there was increased difficulty in lowering contamination levels as fewer wells were used at lower pumping rates. For uncertain parameters hydraulic gradient and decay rate the highly uncertain scenarios produced designs employing more wells at higher pumping rates, thus achieving minimum concentration values. Cases with less uncertainty in hydraulic conductivity produced high performing remedial designs with higher remediation reliability and higher clean up levels. On the other hand, the designs evolved by the EMRGA had lower reliabilities and lower clean up levels for cases having low variations of hydraulic gradient and first-order decay rate. Also, active remediation in the initial stages of the total remediation period emerged as a feasible and most cost-effective solution for the multi-objective optimization problem. Overall, uncertainty in hydraulic conductivity had the most significant impact on remediation reliability of the designs. Results indicate a threshold pumping index value of 430 m3/day/well over which a remediation design was almost certain in achieving 100% reliability. Effects of multiple parameter uncertainty were highly pronounced for cases involving a wider range of hydraulic conductivity values. For these cases the remediation costs dropped to 5.7% and 31.2% with increasing range of hydraulic gradient and heterogeneous decay rate respectively while the Cmax values increased by 217% and 307% for increasing range of hydraulic gradient and heterogeneous decay rate respectively. In general the EMRGA successfully identified Pareto-optimal remedial designs having a wide range of objective values, which satisfied both the conflicting objectives focused in this study. Based on these conflicting objectives (Remediation cost and Maximum residual concentration) seven highly reliable remedial options are identified. In general these designs used just two extraction wells (located just down-gradient from the contaminant plume) at pumping indices between 350 to 450 m3/day/well. These seven remediation plans are embedded in a decision tree to aid the remediation designer in getting an overview of possible groundwater remediation design requirements at the OU-1 site.
... The primary outcome from the management model is a set of optimally designed variables, such as external stresses and the corresponding value of the objective function. Practical engineering applications of mathematical groundwater management models include a pump-and-treat (P&T) systems design [5][6][7][8][9][10][11], sustainable coastal aquifer management [12][13][14], planning of surface and groundwater supply systems [15,16], the identification of unknown pollution sources [17,18], geothermal reservoir engineering [19,20] and pressure buildup management via brine extraction in geological CO 2 storage [21], among others. ...
... It is noted that the S/O formulation given by Equations (4)-(7) differs from traditional single objective P&T management models formulated in the literature [5][6][7][8][9][10][11]22]. In previous works, the objective function was formulated to provide a solution that minimizes the cost function to clean up the aquifer within the remediation design timeframe. ...
A new surrogate-assisted optimization formulation for groundwater remediation design was developed. A stationary Eulerian travel time model was used in lieu of a conservative solute transport model. The decision variables of the management model are well locations and their flow rates. The objective function adjusts the residence time distribution between all pairs of injection-production wells in the remediation system. This goal is achieved by using the Lorenz coefficient as an effective metric to rank the relative efficiency of many remediation policies. A discrete adjoint solver was developed to provide the sensitivity of the objective function with respect to changes in decision variables. The quality management model was checked with simple solutions and then applied to hypothetical two-and three-dimensional test problems. The performance of the simulation-optimization approach was evaluated by comparing the initial and optimal remediation designs using an advective-dispersive solute transport simulator. This study shows that optimal designs simultaneously delay solute transport breakthrough at pumping wells and improve the sweep efficiency leading to smaller cleanup times. Well placement optimization in heterogeneous porous media was found to be more important than well rate optimization. Additionally, optimal designs based on two-dimensional models were found to be more optimistic suggesting a direct use of three-dimensional models in a simulation-optimization framework. The computational budget was drastically reduced because the proposed surrogate-based quality management model is generally cheaper than one single solute transport simulation. The introduced model could be used as a fast, but first-order, approximation method to estimate pump-and-treat capital remediation costs. The results show that physically based low-fidelity surrogate models are promising computational approaches to harness the power of quality management models for complex applications with practical relevance.
... Consequently, the release of these organic chemicals into groundwater from industrial activities will usually require appropriate restoration strategies. Pump and treatment (PAT) is a long-established and widely-used engineered restoration technique for contaminated groundwater (McKinney, 1992;US EPA, 1996;Cohen et al., 1997;Matott et al., 2006;Champagne et al., 2012), in which contaminated groundwater is pumped from an aquifer for ex situ treatment by chemical and biological processes (Suthersan, 1999;Simon et al., 2002;Champagne et al., 2012). Other applications of PAT include the injection of chemical reagents to enhance removal of adsorbed and free-phase organic contaminants (Palmer and Fish, 1992;Suthersan, 1999). ...
... Other applications of PAT include the injection of chemical reagents to enhance removal of adsorbed and free-phase organic contaminants (Palmer and Fish, 1992;Suthersan, 1999). PAT is also implemented for hydraulic containment to control the migration of contaminated groundwater, preventing continued expansion of the contaminant zone or plume (McKinney, 1992;US EPA, 1996;Cohen et al., 1997;Suthersan, 1999;Matott et al., 2006). In this respect, PAT can be very effective for the management of contaminant plumes which are present at a scale or depth in aquifers that extend beyond the technical feasibililty or range of other engineered interventions. ...
This study demonstrates the value of targeted pump and treatment (PAT) to enhance the in situ biodegradation of organic contaminants in groundwater for improved restoration. The approach is illustrated for a plume of phenolic compounds in a sandstone aquifer, where PAT is used for hydraulic containment and removal of dissolved phase contaminants from specific depth intervals. Time-series analysis of the plume hydrochemistry and stable isotope composition of dissolved species (δ34S-SO4, δ13C-CH4, δ13C-TDIC (TDIC = Total Dissolved Inorganic Carbon)) in groundwater samples from high-resolution multilevel samplers were used to deduce changes in the relative significance of biodegradation processes and microbial activity in the plume, induced by the PAT system over 3 years. The PAT system has reduced the maximum contaminant concentrations (up to 6800 mg L-1 total phenols) in the plume by 50 to ∼70% at different locations. This intervention has (i) stimulated in situ biodegradation in general, with an approximate doubling of contaminant turnover based on TDIC concentration, which has increased from <200 mg L-1 to >350 mg L-1, (ii) enhanced the activity of SO4-reducing microorganisms (marked by a declining SO4 concentration with corresponding increase in SO4-δ34S to values >7 to 14‰V-CDT relative to background values of 1.9-6.5 ‰V-CDT), and (iii) where the TDIC increase is greatest, has changed TDIC-δ13C from values of -10 to -15‰V-PDB to ∼-20‰V-PDB. This indicates an increase in the relative importance of respiration processes (including denitrification and anaerobic methane oxidation, AMO) that yield 13C-depleted TDIC over fermentation and acetoclastic methanogenesis that yield 13C-enriched TDIC in the plume, leading to higher contaminant turnover. The plume fringe was found to be a zone of enhanced biodegradation by SO4-reduction and methanogenesis. Isotopically heavy methane compositions (up to -47.8‰V-PDB) and trends between δ13C-TDIC and δ13C-CH4 suggest that AMO occurs at the plume fringe where the contaminant concentrations have been reduced by the PAT system. Mass and isotope balances for inorganic carbon in the plume confirm the shift in spatial dominance of different biodegradation processes and significant increase in contribution of anaerobic respiration for contaminant biodegradation in zones targeted by the PAT system. The enhanced in situ biodegradation results from a reduction in organic contaminant concentrations in the plume to levels below those that formerly suppressed microbial activity, combined with increased supply of soluble electron acceptors (e.g. nitrate) into the plume by dispersion. An interruption of the PAT system and recovery of the dissolved organic contaminant concentrations towards former values highlights the dynamic nature of this enhancement on restoration and relatively rapid response of the aquifer microorganisms to changing conditions induced by the PAT system. In situ restoration using this combined engineered and passive approach has the potential to manage plumes of biodegradable contaminants over shorter timescales than would be possible using these methods independently. The application of PAT in this way strongly depends on the ability to ensure an adequate flux of dissolved electron acceptors into the plume by advection and dispersion, particularly in heterogeneous aquifers.
... Despite the fact that only a small number of cities have made these metrics publicly available, the data in this study is gathered by hand and then used to estimate overall energy use. (McKinney and Lin, 1996).provide further information on this strategy. ...
Mediating the nexus between economic development, energy poverty, and energy efficiency has become a major issue for governments. Evidence from China shows that both sectors have an important role in determining economic development policies and alleviating poverty. Economic development in China is examined experimentally in this research. This study makes use of the Data Envelopment Analysis and the entropy approach between 2007/08 and 2010/11 on the 17 provinces of china. For every unit increase in economic growth pressure, the development indicator of energy efficiency falls by 3.4 percent. Energy poverty and energy efficiency in China are strongly linked to economic development, according to the model's economic development findings. Economic inequalities in China have increased as a result of greater economic development or China's economic development to be of high quality, we believe that our results will be useful in understanding the function of national economic growth management and coordination in wealth distribution and energy use. For rural and male-headed families, the economic development is more consistent. Employees are most likely to be economic developmentt from an increase in economic development, which is expected to reduce energy poverty the most and improve energy efficiency the most.
... Various methods have been reported to control and remediate (clean up) petroleum hydrocarbons from groundwater. Mckinney and Lin (1996) developed a nonlinear programming algorithm (NLP) and applied it to design a low-cost pump-and-treat (PAT) remediation system. Hilton and Culver (2000) developed a genetic algorithm (GA)-based method to consider the uncertainty due to hydraulic conductivity in the optimal design of a groundwater remediation system based on the PAT method. ...
In-situ bioremediation of groundwater is relatively low cost and has high efficiency in remediating groundwater contaminated with petroleum hydrocarbons under suitable hydrogeologic settings. This work develops a multiobjective simulation-optimization (S-O) model for the design of an in-situ bioremediation system for petroleum-hydrocarbon contaminated groundwater. Minimizing the cost of the remediation system (installation and operation) and maximizing its reliability are the two objectives of the developed S-O model. The BIO PLUME II software simulates the remediation process and the non-dominated sorting genetic algorithm (NSGA) II optimizes remediation. The reliability objective measures the effect of uncertainty in the estimate of the initial contaminant concentration on the performance of bioremediation design, and is evaluated under five scenarios of initial contaminant concentration in an example case study illustrating this paper’s methodology. The S-O model for optimal remedation calculates Pareto fronts reflecting the best tradeoff between cost and system reliability that can be obtained. Remediation managers choose remediation strategies from the calculated Pareto front that best serve their cost preferences and remediation requirements. The calculated remediation demonstrates the effectiveness of the remediation system is sensitive to the magnitude of the initial contaminant concentration.
... In PAT, polluted groundwater is taken out from the aquifer and after treating the water, it is either injected back to the aquifer or exported to some other place for practical use (McKinney and Lin 1996;Yan and Minsker 2006). The effective and economic design of the PAT remediation system depends on various factors such as the number of pumping wells, locations of the pumping wells and pumping rates. ...
We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.
... These studies also emphasized the importance of site-specific heterogeneity. In particular, McKinney and Lin (1996) reported that aquifer heterogeneity dictates the design of pumping locations and rates. Huang and Mayer (1997) suggested that pumping wells should be located along the centerline of the contaminant plumes and high-K zones. ...
... Optimal design of groundwater remediation systems is one of the major technical and environmental challenges in the field of water resources because groundwater remediation, often undergoing time horizons of up to 30 years or more, requires numerous operating expenditures (Culver and Shoemaker 1992;Mckinney and Lin 1996;Chang et al. 2007;Yang et al. 2013a). Along with the deepening of research on groundwater remediation design, its application is gradually becoming complex, inherently uncertain, and having multiple conflicting objectives (Kourakos and Mantoglou 2008;Singh and Minsker 2008;Hadka and Reed 2013;Luo et al. 2014;Ojha et al. 2015). ...
The first step in probabilistic multiobjective groundwater optimization is the conceptualization of the uncertain conductivity (KK) field. The KK distribution can be considered as a lognormal random variable. Two geostatistical approaches for conditional field generation, sequential Gaussian simulation (SGSIM) and sequential indicator simulation (SISIM), are used to generate sets of multiple conditional realizations of the KK field. The SGSIM- and SISIM-generated KK fields are found to have similar statistical properties, but SGSIM results in a smoother KK field. The solutions to a probabilistic multiobjective optimization of groundwater remediation design case study are addressed by combining the probabilistic improved niched Pareto genetic algorithm (PINPGA) with the stochastic groundwater flow and transport model. The multiobjective optimization of groundwater remediation design for the removal of trichloroethylene (TCE) plume at Massachusetts Military Reservation (MMR) in Cape Cod is used as a test case. The impact of the sets of lnKlnK realizations are evaluated by comparing the first and second moments of the resulting three-dimensional (3D) contaminant plume and by comparing the resulting Pareto frontiers. The resulting contaminant plumes of the two techniques have similar centroids, but the standard deviation and maximum concentrations are higher with the SISIM conductivity fields. Although PINPGA is able to identify valuable Pareto-optimal strategies about uncertain remediation options given either approach to generating KK-fields, the choice of conductivity field model impacted the Pareto frontiers. The optimization results demonstrate that with the smoothed SGSIM-based lnKlnK realizations, the PINPGA methodology obtains a smaller range between the upper and lower confidence intervals of the Pareto frontiers
... In recent years, computer codes have been developed to describe groundwater and contaminant transport in order to determine the optimal remediation strategy. Traditionally, several optimization strategies applied to Pump and Treat technology (PT) have been developed in order to minimize operational costs, using either time-invariant [Wang and Ahlfeld, 1994;Guan and Aral, 1996;McKinney and Lin, 1996] or time-varying optimization techniques [Minsker and Shoemaker, 1998]. They are based on linear and non-linear programming techniques and are mainly aimed at finding the best strategies to identify wells number and location, and to define optimal pumping rates at the wells. ...
The accidental discharge of leachate from solid waste landfills often leads to groundwater contamination both by inorganic and organic compounds. A major contribution to groundwater pollution at solid waste landfills comes from chlorinated organic compounds such as Tetrachloroethylene (PCE) and Trichloroethylene (TCE). They are among the most dangerous pollutants, due to their high toxicity and persistence grade mainly originating from plastic material degradation due to rainfall water leaching. The environmental fate of these species strictly depends on their main physical and chemical properties; they are denser and less viscous than water, thus migrating from superficial water and contaminated soils to groundwater. Consequently, the remediation of polluted sites must be designed accordingly. Several remediation technologies have been developed for aquifers contaminated by organic and inorganic compounds; they range from ex-situ treatments, coupled with the pump and treat technique, to in situ treatments, performed with Permeable Reactive Barriers (PRB). In a PRB-based treatment, the barrier is commonly built with reactive materials whose hydraulic conductivity is higher than that of the surrounding soils, so that the contaminated groundwater, moving under natural hydraulic gradient, is forced to pass through the barrier without any external energy input. Nowadays, Pump and Treat (PT) technologyis widely used for groundwater remediation. This technology has good
... In recent years, computer codes have been developed to describe groundwater and contaminant transport in order to determine the optimal remediation strategy. Traditionally, several optimization strategies applied to Pump and Treat technology (PT) have been developed in order to minimize operational costs, using either time-invariant [Wang and Ahlfeld, 1994;Guan and Aral, 1996;McKinney and Lin, 1996] or time-varying optimization techniques [Minsker and Shoemaker, 1998]. They are based on linear and non-linear programming techniques and are mainly aimed at finding the best strategies to identify wells number and location, and to define optimal pumping rates at the wells. ...
The accidental discharge of leachate from solid waste landfills often leads to groundwater contamination both by inorganic and organic compounds. A major contribution to groundwater pollution at solid waste landfills comes from chlorinated organic compounds such as Tetrachloroethylene (PCE) and Trichloroethylene (TCE). They are among the most dangerous pollutants, due to their high toxicity and persistence grade mainly originating from plastic material degradation due to rainfall water leaching. The environmental fate of these species strictly depends on their main physical and chemical properties; they are denser and less viscous than water, thus migrating from superficial water and contaminated soils to groundwater. Consequently, the remediation of polluted sites must be designed accordingly. Several remediation technologies have been developed for aquifers contaminated by organic and inorganic compounds; they range from ex-situ treatments, coupled with the pump and treat technique, to in situ treatments, performed with Permeable Reactive Barriers (PRB). In a PRB-based treatment, the barrier is commonly built with reactive materials whose hydraulic conductivity is higher than that of the surrounding soils, so that the contaminated groundwater, moving under natural hydraulic gradient, is forced to pass through the barrier without any external energy input. Nowadays, Pump and Treat (PT) technology is widely used for groundwater remediation. This technology has good remediation efficiency but has high operational costs. For this reason, there is a growing interest for PRB installations as an effective alternative to Pump and Treat remediation methods, thanks to its low operating and maintenance costs. The mechanism of action of a PRB depends on the reactive material chosen to build the barrier. Chlorinated organic compounds can be effectively removed from polluted water and wastewater also by adsorption, a process that combines good efficiency with a very simple process configuration. The pollutant is trapped into the barrier made of adsorbing material and does not precipitate. This particular PRB made of adsorptive material is named Permeable Adsorbing Barrier (PAB). The chapter, after a short description of the main remediation technologies, describes a specific procedure for the design of a PAB for the remediation of an aquifer contaminated by PCE and TCE. As case study, the design of a PAB at a solid waste landfill site in Giugliano in Campania (area North of Napoli, Italy) is presented. In this site, there are many solid waste landfills where, over the past 20 years, about eight million tons of urban and special wastes have been deposited and contamination by different compounds has been detected.
... The resulting evolution of methods and continued engagement with practice transformed WRSA. Three primary research themes emerged: (1) continued application to water policy and planning problems but focusing on stakeholder engagement, collaborative model development, characterization of the consequences of alternative options rather than specifying preferred options [e.g., Palmer et al., 1999], and better treatment of uncertainty and incorporation of robustness concepts [e.g., Watkins and McKinney, 1997;Ray et al., 2013], (2) application of prescriptive modeling (e.g., optimization) where well suited to support decisions that were less subjective in nature (e.g., reservoir operations, groundwater pumping, levee design, financial mechanisms) [Mays and Tung, 1981;Gorelick et al., 1983;Yeh, 1992;McKinney and Lin, 1996;Savic and Walters, 1997], and (3) the use of integrated models to create mathematical representations of coupled human and hydrologic systems, such as river basin models, groundwater coupled systems, and even global agricultural production and trade models, to make projections of the future evolution of these systems [Rosegrant et al., 2002;Molle, 2006;Schl€ uter and Pahl-Wostl, 2007;Wainwright, 2008;Kanta and Zechman, 2013]. ...
This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices.
... A variety of optimization techniques have been applied to optimal groundwater remediation or pumping design problems. These include linear programming (Atwood and Gorelick 1985), non-linear programming (McKinney 1996), dynamic programming (Culver 1993), simulated annealing (Marryott 1993), and genetic algorithms. However the majority of these works relies on traditional non linear algorithms. ...
Ground water flow modeling and optimization techniques are coupled to determine the optimal cost of pumping to reduce the groundwater table of a confined aquifer at steady state condition. An optimal aquifer pumping model employing a non-linear programming was developed to find the minimum cost of pumping in a confined aquifer at steady state. The non-linear model include two objective functions: minimizing the cost of pumping and maximizing the discharge from the aquifer. Bacterial foraging techniques (BFT) is used to optimize the cost function The cost of pumping includes the fixed cost of system construction and installation as well as operation and maintenance. Results show that fixed costs has significant impact on the cost of aquifer pumping system. The sensitivities of different parameters such as cost of pumping and pumping discharge to variations in the permeability of soil and drawdown are studied by varying K and over wide range.
... A packed column air stripper differs from other types of stripper due to the presence of the packing materials, which increase the interfacial surface area between the liquid (water) and gas (air) phase and enhance the interphase mass transfer of contaminant from water to air (McKinney and Lin 1996). There are a number of design equations that can be used to predict the performance of packed column air strippers. ...
Addressing environmental degradation and ensuring environmental sustainability are inextricably linked to all methods of reducing volatile organic compounds (VOCs) from the environment. A packed column air stripper is a typical example of such technologies for the removal of VOCs from polluted water. The present review is devoted to the applications of a packed column air stripper and, in comparison with previous reviews, presents further elaborations and new information on topics such as modeling and simulation of the dynamic behavior of the air stripping process in a packed column air stripper. The paper observed that a knowledge gap still exists in the synthesis of this knowledge to formulate practically applicable mathematical relationships to describe the process generally. Therefore, further researches are still required in the area of air stripper performance optimization, particularly in the development of a mathematical model and the optimization of an air stripper using a statistical experimental design method. Such a determination is critical to the understanding of the interactive effect of process variables such as temperature, air-to-water (A/W) ratio, and height of packing on air stripper performance.
... Most commonly, researchers have applied sensitivity analysis to explore the performance and/ or reliability of various pump-and-treat scenarios (Hamed et al. 1996;Russell and Rabideau 2000;Ko et al. 2005;Lee et al. 2008;Singh and Chakrabarty 2010), but with the chosen model assumed a priori to be suitable for predictive purposes. Less conventional research has utilized sensitivity analysis to evaluate the influence of optimization search algorithm parameters (Erickson et al. 2002), remediation goals (Ahlfeld and Hill 1996;McKinney and Lin 1996), and the locations of remediation wells (Johnson and Rogers 1995). Other related work has examined the use of sensitivity analysis in developing predictive models for source water protection (Bakr and Butler 2005;Esling et al. 2008) and permeable reactive barriers (Painter and Milke 2007;Lee et al. 2009). ...
Pump‐and‐treat systems can prevent the migration of groundwater contaminants and candidate systems are typically evaluated with groundwater models. Such models should be rigorously assessed to determine predictive capabilities and numerous tools and techniques for model assessment are available. While various assessment methodologies (e.g., model calibration, uncertainty analysis, and Bayesian inference) are well‐established for groundwater modeling, this paper calls attention to an alternative assessment technique known as screening‐level sensitivity analysis (SLSA). SLSA can quickly quantify first‐order (i.e., main effects) measures of parameter influence in connection with various model outputs. Subsequent comparisons of parameter influence with respect to calibration vs. prediction outputs can suggest gaps in model structure and/or data. Thus, while SLSA has received little attention in the context of groundwater modeling and remedial system design, it can nonetheless serve as a useful and computationally efficient tool for preliminary model assessment. To illustrate the use of SLSA in the context of designing groundwater remediation systems, four SLSA techniques were applied to a hypothetical, yet realistic, pump‐and‐treat case study to determine the relative influence of six hydraulic conductivity parameters. Considered methods were: Taguchi design‐of‐experiments (TDOE); Monte Carlo statistical independence (MCSI) tests; average composite scaled sensitivities (ACSS); and elementary effects sensitivity analysis (EESA). In terms of performance, the various methods identified the same parameters as being the most influential for a given simulation output. Furthermore, results indicate that the background hydraulic conductivity is important for predicting system performance, but calibration outputs are insensitive to this parameter (K BK). The observed insensitivity is attributed to a nonphysical specified‐head boundary condition used in the model formulation which effectively “staples” head values located within the conductivity zone. Thus, potential strategies for improving model predictive capabilities include additional data collection targeting the K BK parameter and/or revision of model structure to reduce the influence of the specified head boundary.
... When faced with groundwate r remediati on design problems , the coupled simulation-optimization model is an effective and commonly used tool for managing the groundwater systems (Cai et al., 2003;Culver and Shoemaker, 1992;Gorelick, 1983;Mayer et al., 2002;Minsker and Shoemaker , 1998;Tiedema n and Gorelick, 1993;Wagner, 1995;Wagner and Gorelick, 1989;Wu et al., 2005;Zheng and Wang, 1999a ). Until recently, pump and treat (PAT) technolo gy, which relies on extracting the contaminated groundwate r to the surface using extraction wells or drains and treating it at the surface to remove the contaminan ts, was the most widely used method of remediating contaminated aquifers (Guan and Aral, 1999;Kou et al., 1992;Kourakos and Mantoglou, 2008;Marryott et al., 1993;Mckinney and Lin, 1996 ). The basic objective of optimal design of groundwate r PAT systems is to optimize the remedial pumping wells (including well location and pumping rate) so as to identify the minimum operating cost while meeting all specified hydraulic head and concentration constraints. ...
... In recent years, computer codes have been developed to describe groundwater and contaminant transport in order to determine the optimal remediation strategy. Traditionally, several optimization strategies applied to Pump and Treat technology (PT) have been developed in order to minimize operational costs, using either time-invariant [Wang and Ahlfeld, 1994;Guan and Aral, 1996;McKinney and Lin, 1996] or time-varying optimization techniques [Minsker and Shoemaker, 1998]. They are based on linear and non-linear programming techniques and are mainly aimed at finding the best strategies to identify wells number and location, and to define optimal pumping rates at the wells. ...
... Several remedial technologies have been developed to treat contaminated soils and groundwater, such as Pump-and-treat systems (BTS) (McKinney and Lin 1996), Bioremediation (BR) (Al-Awadhi et al. 1996), Chemical treatment (CT) (Chen et al. 2000), soil vapor extraction (SVE) and air sparging (AS) (Rahbeh and Mohtar 2007). BTS have been extensively used for groundwater remediation, but this method requires pumping of relatively large volumes of water with relatively low contaminant concentrations. ...
Air sparging is an effective technique for the remediation of soil and groundwater polluted by volatile organic compounds. In this paper, this technique was investigated by conducting air-sparging test in the laboratory on the Shanghai sandy silt that was artificially contaminated with p-xylene. A test tank was designed for this purpose. During the air-sparging process, aqueous p-xylene solutions were extracted from the observation holes, and their concentrations were quantified by the spectrophotometric detection method. The mechanism of mass transfer process of p-xylene in the vicinity of sparging well and the remediation of the contaminated groundwater by air sparging were explored. The results showed that the removal zone of the p-xylene was mainly located within a radius of about 20 cm around the air injection well, with 90 % p-xylene removed after 20-day air sparging. Within the initial 5-day sparging, the concentration of p-xylene decreased rapidly in the mass transfer zone. By contrast, in the area far from the injection well, the p-xylene concentration decreased evenly and slowly. Thus, the remediation of contaminated soil and groundwater by air sparging is space–time dependent. For further analysis, the adsorption of silt was taken into account, and the distribution coefficient, K
d
, was introduced to the modified Shackleford’s mass transfer model. The comparison between the simulated and measured results indicates that the modified model can satisfactorily describe the p-xylene mass transfer observed in this study.
... Example applications of simulation-based optimization in a geoscience context include the design of pump-and-treat systems (McKinney and Lin, 1996), groundwater supply systems (Chu and Chang, 2010), landfill liners (Bartelt-Hunt et al., 2006), agricultural operations (Gitau et al., 2006), waste load allocation strategies (Burn and Yulianti, 2001), and geothermal reservoirs (Akin et al., 2010). In addition, calibration of geoscience models is typically formulated as a simulation-based optimization problem-where uncertain model parameters are adjusted to minimize differences Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cageo ...
A common approach for utilizing environmental models in a management or policy-analysis context is to incorporate them into a simulation-optimization framework - where an underlying process-based environmental model is linked with an optimization search algorithm. The optimization search algorithm iteratively adjusts various model inputs (i.e. parameters or design variables) in order to minimize an application-specific objective function computed on the basis of model outputs (i.e. response variables). Numerous optimization algorithms have been applied to the simulation-optimization of environmental systems and this research investigated the use of optimization libraries and toolboxes that are readily available in MATLAB and Python - two popular high-level programming languages. Inspired by model-independent calibration codes (e.g. PEST and UCODE), a small piece of interface software (known as PIGEON) was developed. PIGEON allows users to interface Python and MATLAB optimizers with arbitrary black-box environmental models without writing any additional interface code. An initial set of benchmark tests (involving more than 20 MATLAB and Python optimization algorithms) were performed to validate the interface software - results highlight the need to carefully consider such issues as numerical precision in output files and enforcement (or not) of parameter limits. Additional benchmark testing considered the problem of fitting isotherm expressions to laboratory data - with an emphasis on dual-mode expressions combining non-linear isotherms with a linear partitioning component. With respect to the selected isotherm fitting problems, derivative-free search algorithms significantly outperformed gradient-based algorithms. Attempts to improve gradient-based performance, via parameter tuning and also via several alternative multi-start approaches, were largely unsuccessful.
Pump‐and‐treat technologies are widely used in groundwater remediation and site clean‐up. Such technologies involve pumping contaminated groundwater to the surface for treatment. Following treatment, the water is often reinjected back into the aquifer (referred to as pump‐treat‐inject or PTI) for potential reuse. The treatment system is often designed to remove dissolved‐phase contaminants in groundwater such that water meets applicable cleanup standards (herein referred to as “full treatment”). However, in some cases, the treatment system may not effectively reduce the dissolved‐phase concentrations effectively (herein referred to as “partial treatment”) for some of the contaminants present in groundwater. Modeling PTI under partial treatment conditions is challenging because contaminant concentrations in injected water depend on the pumped water concentrations and the system treatment efficiency. Essentially, the injected water concentration (a transport model input) is unknown prior to transport simulation. This study presents a novel iterative approach to modeling PTI under partial treatment scenarios, where the injected water concentration is linked to the modeled pumped water concentration. The method was developed for a complicated three‐dimensional (3‐D) flow and transport modeling study conducted for a confidential remediation site where PTI with partial treatment was applied. However, due to the complexity of the 3‐D model and the confidential information of the site, a simple two‐dimensional (2‐D) numerical model is presented to demonstrate the iterative method. The 2‐D model test runs and the 3‐D model application in a remediation site showed that the iterative simulation results quickly converged to a viable final solution.
We here propose a two-step approach-based simulation-optimization model for multi-objective groundwater remediation using enhanced random vector functional link (ERVFL) and evolutionary marine predator algorithm (EMPA). In this study, groundwater flow and solute transport models are developed using MODFLOW and MT3DMS. The ERVFL network is used to approximate the flow and transport models, enhancing the computational performance. This study also improves the robustness of the ERVFL network using a kernel density estimator (KDE) based weighted least square approach. We further develop the EMPA by modifying the marine predator algorithm (MPA) using elite opposition-based learning, biological evolution operators, and elimination mechanisms. In the multi-objective version of EMPA, the non-dominated/Pareto-optimal solutions are stored in an external repository using an archive controller and adaptive grid mechanism to promote better convergence and diversity of the Pareto front. The proposed methodologies are applied for multi-objective groundwater remediation of a hypothetical unconfined aquifer based on the two-step method. The first step directly integrates flow and transport models with EMPA and finds the optimal locations of pumping wells by minimizing the percent of contaminant mass remaining in the aquifer. In the second step, the ERVL-based proxy model is integrated with EMPA and used for multi-objective optimization while explicitly using the pumping well locations obtained in the first step. The multi-objective optimization generates a Pareto-optimal solution representing the relationship between the rate of pumping and the amount of contaminant mass in the aquifer. Further analyses show a significant advantage of the two-step approach over a traditional method for multi-objective groundwater remediation.
Groundwater contamination causes significant groundwater supply risks that severely affect humans and livestock, especially in the era of climate change. Consequently, the need to develop and optimize remediation strategies such as pump and treat is indispensable. An effective and efficient strategy can reduce the spread of contaminants and cost of clean-up. However, developing such a strategy is challenging due to the inherent uncertainty and sequential nature of the problem. For example, taking actions such as drilling wells is interwoven with information gathering over time. In this paper, we formulate the groundwater remediation planning and decision problem as a partially observable Markov decision process (POMDP). A POMDP formulation allows planning by optimizing the trade-off between information gathering and the performance of possible future scenarios. We use a state-of-the-art POMDP solver called DESPOT for planning remediation strategies. The results show that DESPOT can yield much better remediation strategies than hand crafted heuristics and optimization methods. The superior performance of DESPOT is due to the incorporation of both previous information and future reward into the current decision. We also demonstrate the robustness of the POMDP formulation to measurement error.
We propose a simulation-optimization (SO) model based on a novel two-step strategy for the optimal design of groundwater remediation systems. The SO models are developed by coupling simulation models directly or through the extreme learning machine (ELM) with evolutionary hunting strategy based metaheuristics (EHSMs). In the first step, EHSMs with a combinatorial optimization technique are used to obtain optimal pumping locations by minimizing the percentage of contaminant mass that remained in the aquifer while keeping the pumping strategy as constant. In the second step, the optimal pumping locations are directly used as input, and a composite function is employed to minimize the sum of the water extraction rates and the percentage of extracted contaminant mass by constraining hydraulic heads and contaminant concentrations. The performance of the two-step strategy is found to be slightly better and computationally more efficient than the alternate approach. Moreover, various statistical measures suggest the superiority of EHSMs over other metaheuristics for groundwater remediation.
A two-dimensional mathematical, model is developed to simulate the flow regime, of the upper part of Dibdibba Formation. The proposed, conceptual model, which is advocated to simulate the flow regime of aquifer is fixed for one layer, i.e. the activity of the deeper aquifer is negligible. The model is calibrated using, trial and error method. According to the calibration process, the hydraulic characteristics of the upper aquifer has been identified the hydraulic conductivity in the study area ranged (60-200) m/day while the specific, yield ranges, between, (0.08- 0.45).In this research, the obtaining of the optimum management of groundwater flow by linked simulation-optimization model. MODFLOW packages are used to simulate the flow in the system of groundwater. This model is completed with an optimization model which is depending on the Genetic Algorithm (GA) and Tabu Search (TS). Two management cases (fixed well location and flexible well location with the moving, well option) were considered by executing the model with adopting calibratedparameters. In the, first case the objective function is converged to a maximum value of (3.35E+5 m³/day) by using GA, while this function is closed to 4.00E+5 m³/day by using TS. The objective function in second case converges to the maximum value (7.64E+05m³/day) and (8.25E+05m³/day) when using GA and TS respectively. The choice option for the optimal location of the wells in the second case leads to an increase of 106% of the total, pumping rates, compared to the first, case. The results of the first and second cases shown that the total value of pumping rate from all pumping wells by using TS is better than the total value of pumping rate by using GA.
Persulfate (S2O82-) based in situ chemical oxidation (ISCO) has gained more attention in recent years due to the generation of highly reactive and selective sulfate radical (SO4•-). This study examined the effects of important groundwater chemical parameters, i.e., alkalinity, pH and chloride on benzene degradation via heterogeneous persulfate activation by three Fe(III)- and Mn(IV)-containing aquifer minerals: ferrihydrite, goethite and pyrolusite. A comprehensive kinetic model was established to elucidate the mechanisms of radical generation and mineral surface complexation. Results showed that an increase of alkalinity up to 10 meq/L decreased the rates of persulfate decomposition and benzene degradation, which was associated with the formation of unreactive surface carbonato complexes. An increase in pH generally accelerated persulfate decomposition due to enhanced formation of reactive surface hydroxo complexation. A change in the chloride level up to 5 mM had a negligibly effect on the reaction kinetics. Kinetics modeling also suggested that SO4•- was transformed to hydroxyl radical (HO•) and carbonate radical (CO3•-) at higher pHs. Furthermore, the yields of two major products of benzene oxidation, i.e., phenol and aldehyde, were positively correlated with the branching ratio of SO4•- reacting with benzene, but inversely correlated with that of HO• or CO3•-, indicating that SO4•- preferentially oxidized benzene via pathways involving fewer hydroxylation steps compared to HO• or CO3•-.
An optimization model is developed to locate pumping wells in a river bank filtration system to minimize the overall cost of pumping and treatment. The model combines the numerical solution of water flow and solute transport from the river to the pumping wells with a genetic-algorithm-based optimization technique. The water quality parameters considered in the study are the concentration of (1) suspended solids, (2) endosulfan, which is a widely used pesticide in agriculture, and (3) E. coli. It was observed that the fitness function increases at a faster rate near the river before attaining the optimum. However, the rate of decrease in the fitness function is gradual after the optimum. It is concluded that the genetic-algorithm-based optimization technique is an efficient and robust in the estimation of optimum well distance. Sensitivity analysis of the optimal distance and total cost due to variation in aquifer characteristics and decay rate is also presented.
Facility location problems (FLPs) are commonly encountered in water infrastructure planning. They pertain to the optimal siting of facilities, e.g. water and wastewater treatment plants, groundwater pumps, sensors to detect water contamination, chlorine booster stations along water distribution pipes, etc. In this paper, an improved Tabu Search (TS) algorithm, the Expanding Neighborhood Tabu Search (ENTS) is proposed for solving large FLPs. ENTS incorporates elements of Variable Neighborhood Search where the search neighborhood is systematically varied; specifically, the search neighborhood is expanded as the search progresses according to a ranking of the potential locations to site the facilities. Solutions involving just top-ranked locations are searched first, then those involving lower-ranked ones as well, then even lower-ranked ones in addition, and so on, until finally, all locations are included in the search. The ranking of potential locations is carried out using a simple TS but with the stopping criterion set such that it terminates prematurely. Preliminary results comparing ENTS and a simple TS for four cases of the uncapacitated FLP shows ENTS to be the more efficient algorithm for large problems. More research is needed to prove the feasibility of ENTS for solving actual water infrastructure FLPs.
The optimal groundwater bioremediation design problem is complex, nonlinear, and computationally expensive. In this paper, an improved Ant Colony Optimization (ACO) algorithm is employed for optimizing a groundwater bioremediation problem, and the BIOPLUMEII model is used to simulate aquifer hydraulics and the bioremediation process. Injection and extraction pumping rates and well locations are treated as decision variables. Optimization results show that the proposed approach performs better than the Genetic Algorithm (GA), Simulated Annealing (SA) and the hybrid SA-GA algorithm, called Parallel Recombinative Simulated Annealing (PRSA), and reduces the computational time of a number of function evaluations compared with the mentioned algorithms. Applying the optimal dynamic pumping strategy in the second stage reduces bioremediation costs by 13:3%.
The purpose of this research is to investigate the capability of a real genetic algorithm (GA) to remove a heavy-metal pollutant plume from an aquifer. The real genetic algorithm has been used in conjunction with proven and accepted finite-difference analysis to reach an efficient solution. It also illustrates the interaction between the real GA optimizer and the finite-difference technique. Two scenarios regarding the well positions, number of wells, pumping rate at each well, and total cost were determined for the purpose. A new system has been developed for removing heavy metals [mercury (Hg) and cadmium (Cd)] from groundwater in a real problem and proved to be optimal. Real-coding GAs proved to be a practical means of optimizing engineering solutions to problems related to groundwater quality management, in particular those involving discontinuous functions. The real GA can be used with accredited finite-difference methods toward an optimal solution. The developed application of a combined simulation-optimization method for cleanup of contamination from a real landfill in a coastal area of Syria can also be used in other water-resource quality applications. (C) 2013 American Society of Civil Engineers.
Groundwater flow model MODFLOW 2000 and contaminant transport model MT3DMS 5.0 have been directly linked with nondominated sorting genetic algorithm (NSGA) II using a novel technique to solve a multiobjective pump-and-treat groundwater remediation problem. In this technique, the system simulators in their compiled forms are directly coupled with the optimization algorithm. The performance of the model has been evaluated on a multiobjective groundwater remediation problem based on a hypothetical confined multilayered aquifer under steady-state flow condition. Two conflicting objectives, viz, minimization of remediation cost and minimization of remediation time subject to upper and lower bounds on extraction rates, upper bounds on hydraulic heads, and contaminant concentration levels, are incorporated into the optimization formulation. Incorporation of discrete cost components into the objective cost function renders it to be discontinuous and nondifferentiable, and the resulting Pareto front exhibits disjoints at a few locations. The disjointed feature of the Pareto front is used to study the impact of remediation time on remediation cost. The main contribution of this study is the finding of the significance of remediation time on optimal groundwater remediation design problems. The results suggest that remediation time plays a crucial role in the optimal aquifer remediation design. Unless compulsory, incorporation of remediation time as a decision variable in the optimization model may yield better results in minimizing the remediation cost. Another contribution is the linking of computer programs written in different languages. The linking technique used in this study has the potential of coupling any two or more codes written in different languages without writing interface programs. DOI: 10.1061/(ASCE)HE.1943-5584.0000327. (C) 2011 American Society of Civil Engineers.
The in-situ bioremediation process is widely adopted for treating petroleum contaminated sites. The objective of this paper is to propose a hybrid fuzzy rule-based model predictive process control system, which supports automated and real-time control of the in-situ bioremediation process while optimizing its inherently complex physical, chemical, and biological processes. This control system can identify the optimal control action sequence and its corresponding trajectory of process outputs, while at the same time minimizing the total operation cost of the process. The proposed control system improves upon earlier versions of the process control system because it can handle in real-time the unavoidable imprecision of the bioremediation simulation model and the uncertainty implicit in the fuzzy control rules developed based on a survey questionnaire.
This study describes the development of a dynamic knowledge-based reasoning-enhanced model predictive control system (KBRECS) for in-situ bioremediation processes. The automated control system balances the complex physical, chemical, and biological processes involved in the remediation process while minimizing overall cost of the entire remediation process. The control system includes an optimization subsystem and a monitoring subsystem. The optimization subsystem consists of a simulation model supported by an optimization function which is designed to generate a series of optimal control actions. The monitoring subsystem is a knowledge-based system which is designed to monitor and adjust the online control actions. The numerical simulation model describes the fate and transport of the subsurface contaminants. The optimization function is a constrained, nonlinear function that has been implemented using a genetic algorithm (GA). Intermediate genetic algorithm individuals are indexed and stored in the knowledge base, thereby reducing search times for values to replace the unqualified schemes used by the monitoring subsystem. The system was applied to a lab experiment and compared with the control system presented in [9]. The results indicated that the knowledge based reasoning system enhanced the control system by generating an appropriate control strategy and adjusting control actions promptly. This helps to enhance efficiency in control of the in-situ bioremediation process at petroleum-contaminated groundwater systems.
Groundwater remediation works are expensive, complex in nature, and generally involve uncertain design parameters and multiple conflicting objectives. Reduction in remediation cost is, normally, the primary objective of optimal remediation designs. Remediation time has significant influence on remediation cost. The main contributions of this study are: development of a chance-constrained multi-objective programming (CCMOP) model, exploration of the significance of flexible remediation time on optimal remediation designs, and exploration of some basic characteristics of multi-objective optimization algorithms. The developed methodology excludes inferior yet optimal solutions from the set of possible solutions, identifies the ranges in which non-inferior solutions lie, and prevents one from making inferior decisions. It provides insightful information about the problem and simplifies the decision making process considerably. The results suggest that by avoiding the use of faulty remediation times, highly significant amounts of remediation cost could be saved. The CCMOP is an effective uncertainty and reliability prediction technique.
This paper deals with a procedure to optimize the design of permeable reactive barrier (PRB) built with activated carbon for the in situ remediation of a polluted aquifer. A simulation-based heuristic procedure is developed to define, with an iterative procedure, the optimal position and geometrical dimensions of the barrier itself, which simultaneously assure the respect of pollutant concentration limits and minimum barrier size. A computer code is used to describe the field motion of the aquifer, the contaminant transport and the adsorption phenomena occurring inside the barrier. A real aquifer polluted by tetrachloroethylene (PCE) situated in the area north of Naples (Italy) is presented as a case study. Simulation results show that a PRB formed by sections of different width, tuned to the different values of PCE concentration in the plume, can assure an effective remediation of the site.
This paper extends earlier work on derivative-based optimization for
cost-effective remediation to unconfined aquifers, which have more
complex, nonlinear flow dynamics than confined aquifers. Most previous
derivative-based optimization of contaminant removal has been limited to
consideration of confined aquifers; however, contamination is more
common in unconfined aquifers. Exact derivative equations are presented,
and two computationally efficient approximations, the quasi-confined
(QC) and head independent from previous (HIP) unconfined-aquifer finite
element equation derivative approximations, are presented and
demonstrated to be highly accurate. The derivative approximations can be
used with any nonlinear optimization method requiring derivatives for
computation of either time-invariant or time-varying pumping rates. The
QC and HIP approximations are combined with the nonlinear optimal
control algorithm SALQR into the unconfined-aquifer algorithm, which is
shown to compute solutions for unconfined aquifers in CPU times that
were not significantly longer than those required by the
confined-aquifer optimization model. Two of the three example
unconfined-aquifer cases considered obtained pumping policies with
substantially lower objective function values with the unconfined model
than were obtained with the confined-aquifer optimization, even though
the mean differences in hydraulic heads predicted by the unconfined- and
confined-aquifer models were small (less than 0.1%). We suggest a
possible geophysical index based on differences in drawdown predictions
between unconfined- and confined-aquifer models to estimate which
aquifers require unconfined-aquifer optimization and which can be
adequately approximated by the simpler confined-aquifer analysis.
A new multi-objective optimization methodology is developed, whereby a multi-objective fast harmony search (MOFHS) is coupled with a groundwater flow and transport model to search for optimal design of groundwater remediation systems under general hydrogeological conditions. The MOFHS incorporates the niche technique into the previously improved fast harmony search and is enhanced by adding the Pareto solution set filter and an elite individual preservation strategy to guarantee uniformity and integrity of the Pareto front of multi-objective optimization problems. Also, the operation library of individual fitness is introduced to improve calculation speed. Moreover, the MOFHS is coupled with the commonly used flow and transport codes MODFLOW and MT3DMS, to search for optimal design of pump-and-treat systems, aiming at minimization of the remediation cost and minimization of the mass remaining in aquifers. Compared with three existing multi-objective optimization methods, including the improved niched Pareto genetic algorithm (INPGA), the non-dominated sorting genetic algorithm II (NSGAII), and the multi-objective harmony search (MOHS), the proposed methodology then demonstrated its applicability and efficiency through a two-dimensional hypothetical test problem and a three-dimensional field problem in Indiana (USA).
This paper describes a method for quantifying the economic and environmental effects of uncertainty in biological parameter values on optimal in situ bioremediation design. The range of uncertainty in model results associated with a range of input parameter values is quantified for both individual parameter errors and errors in combinations of parameters. Three measures of sensitivity are presented that quantify different aspects of the effects of model error on an implemented optimal policy. Numerical results are presented for an example site contaminated with phenol, with parameter ranges derived from values reported in the literature. For the example site, Ks (the substrate half-velocity coefficient in the Monod kinetic equation for biodegradation) was found to be the most sensitive biological parameter and this sensitivity was asymmetric; i.e., reductions in the value of Ks have a much greater effect than increases in the value of Ks. The methodology applied in this paper could also be applied to other water resource management problems, allowing the user to quantify the effects of wide ranges of possible parameter values on model results. The method is particularly useful for computationally intensive optimization models, as it requires a manageable number of model runs, and for the many situations where insufficient data are available to permit accurate estimation of probability distributions.
Effective process control is crucial in implementing remediation actions for petroleum-contaminated sites. However, in dealing with in situ bioremediation practices, difficulties exist in incorporating numerical simulation models that are needed for process forecasting within real-time non-linear optimization frameworks that are critical for supporting the process control. With such difficulties, it is desired that a statistical relationship between remediation system performance and operating condition be established. Nevertheless, in the remediation systems, many variables can be either continuous or discrete, and the relations among them can be either linear or non-linear. These lead to complexities in the related multivaraite analyses. In this study, a forecasting system has been developed for supporting remediation design and process control based on techniques of NAPL-biodegradation (non-aqueous phase liquid biodegradation) simulation and stepwise-cluster analysis (SCA). The results indicate that the developed system is effective in forecasting the effects of multiple cleanup actions under various conditions. The predicted benzene concentrations have acceptable error levels compared with the outputs of numerical simulation. An optimization model for obtaining optimum operating conditions is then proposed to illustrate how the SCA method can be used for supporting optimization of bioremediation operations. A unique contribution of this research is the development of a multivariate inference system associated with simulation and optimization efforts for tackling the complex in situ bioremediation practices.
Given the inherent uncertainty in groundwater management problems uncertainty in determining aquifer parameter values, identifying an optimal remediation strategy based on a deterministic description of the system may not yield an optimal and feasible design. This work builds on the robust genetic algorithm (GA) developed by Chan Hilton and Culver. The robust GA is a simulation-optimization approach which combines a GA with a contaminant fate and transport simulation model and a spatially correlated random field generator to identify tradeoffs between design cost and reliability while considering uncertainty of hydraulic conductivity values. This work evaluates the application of the robust GA to two formulations of a groundwater remediation design problem. In this problem, the objectives are to minimize the cost of the remediation design while satisfying water quality constraints and indirectly maximizing the reliability of the designs. This is done by identifying the location and pumping rates of a set of extraction well used for pump-and-treat remediation. The results show that the robust GA can successfully identify cost-effective and reliable designs in a computationally efficient manner. Future work involving the robust GA and planned modifications also are discussed in this paper.
The impacts of physical and chemical aquifer heterogeneities on optimal remediation design, costs, and time to compliance are investigated by linking a genetic algorithm with a contaminant transport simulation model. Physical and chemical aquifer heterogeneities were grouped into three levels as follows: (1) hydraulic conductivity (K) heterogeneity only; (2) combined heterogeneity of K and the distribution coefficient (Kd); and (3) combined heterogeneity of K, Kd, and the mass transfer rate (α). Various degrees of heterogeneity were considered, ranging from slightly heterogeneous to strongly heterogeneous. Impacts were evaluated using two different optimization models: the optimal design model and the time-to-compliance model. The first model focused on finding optimal aquifer remediation designs and costs under various heterogeneity conditions, and the second model optimized the time needed to meet the water quality goals for a fixed pumping schedule. Results show that the variability in the remediation costs and time to compliance for different realizations of a heterogeneous K-field increases as K-heterogeneity increases. Consideration of Kd- and α-heterogeneities results in different policies and costs compared to cases where sorption heterogeneity is neglected. In general, time to compliance increases for systems with both chemical and physical heterogeneity as compared to systems with only physical heterogeneity. The impact of α-heterogeneity on remediation strategies is most apparent when Kd-heterogeneity is high. Although an increase in K-heterogeneity decreases the impact of Kd- and α-heterogeneities on remediation costs and time to compliance, sorption heterogeneity could still significantly impact the performance of a remediation system, especially when sorption heterogeneity is high.
There often is difficulty enforcing the given constraints when applying a genetic algorithm (a flexible stochastic search method) to optimal ground-water remediation design problems. This paper compares two methods for constraint handling within the genetic algorithm framework. The first method, the additive penalty method (APM), is a commonly used penalty function approach in which a penalty cost proportional to the total constraints violation is added to the objective function. The second method, the multiplicative penalty method (MPM), multiplies the objective function by a factor proportional to the total constraints violation. The APM and MPM, using constant and generation-varying constraint weights, are applied to two pump-and-treat design examples. Overall, the application of the APM resulted in infeasible solutions with small-to-moderate total constraints violations. With the MPM, a set of feasible and near-optimal policies was readily identified for both examples. Additionally, the MPM converges to the solution faster than the APM. These results demonstrate that the MPM is a robust method, capable of finding feasible and optimal or near-optimal solutions while using a range of weights.
This work explores the sensitivity of optimal remedial design policies and their associated costs to the residual constraint violation, which is the sum of any small violations in constraints that may occur over all points of interest. To evaluate the sensitivity, a genetic algorithm is used to solve two different groundwater remediation design problems: pump-and-treat using granular activated carbon and enhanced in situ bioremediation. The sensitivity to the residual violation is tested given a range of water quality goals and for static and dynamic cases. The range of residual constraint violations tested was small, so that in all cases greater than 98% of the remediation goal was reached. Nevertheless, it was found that the cost sensitivity to these small constraint relaxations was of the same magnitude as the cost sensitivity to changes in the ultimate water quality goal. The greatest sensitivity was seen for the lowest water quality goals. This work indicates that a remediation designer using optimization tools should consider the trade-offs in cost and performance that will occur depending upon one's approach to constraint enforcement.
An integrated simulation-optimization system was developed for
supporting decisions of the dual phase vacuum extraction (DPVE)
processes. The system coupled a DPVE process simulator, a multivariate
regression tool and a nonlinear optimization model into a general
framework. A stepwise-cluster-analysis technique was provided for
establishing a DPVE process forecasting system for describing the
relationships between remediation actions and system responses (i.e.,
total extracted volume of oil/water, elevation distribution of water
table, and specific volume of oil). The forecasting system was then
embedded into a multiobjective optimization framework, where the
objectives were to minimize the operation cost and maximize the
remediation efficiency. The constraints include environmental, hydraulic
and technical restrictions to the DPVE processes. A case study was
conducted for a petroleum-contaminated site in western Canada. The
results from the stepwise cluster analysis indicated that the generated
cluster trees could be used for predicting system responses of the DPVE
process, given inputs of the operating conditions. The prediction
accuracies of the generated cluster trees were verified using randomly
generated data sets. The optimum operating conditions could vary
significantly under different cost-efficiency targets. When a stricter
environmental target (i.e., the amount of pollutants in subsurface) was
concerned, a higher system cost had to be paid; when the cost became a
critical factor, the performance of contaminant removal would have to be
compromised. The developed system could be used to analyze tradeoffs
between system cost and process efficiency in the DPVE operations; it
could also support the formulation of an on-site process-control system
with vacuum levels and extraction rates being the main control
variables.
A method for optimal remediation of heterogeneous aquifers based on stochastic simulation with adaptive determination of critical realizations is developed. Multiple hydraulic conductivity realizations are generated using the turning bands method while Monte Carlo simulation is incorporated into a multiobjective genetic optimization algorithm. In order to reduce the computational burden of Monte Carlo simulations, an adaptive procedure is developed for identifying ``critical realizations'' which are the ones that have an effect on the optimal solution depending on desired reliability level. The adaptive procedure is embedded into the genetic optimization algorithm where noncritical realizations are eliminated step by step during the advancement of optimization. When the number of remaining realizations reaches a minimum, some new realizations are added. The specific realizations which are removed or added in the critical set are selected by an automatic procedure controlled by a similarity criterion based on rankings of realizations. This is a significant improvement over the nonadaptive methodology for identification of critical realizations and does not require using a number of initial designs and a safety threshold. The methodology is applied in a remediation problem with two objectives (reduction of contaminant mass and cleanup cost). The applications indicate significant savings in computer time without loss of performance.
Surfactant-enhanced remediation is an effective approach for dealing with subsurface-contaminated sites. However, in studying and controlling the related processes, difficulties exist in incorporating a complicated numerical simulation model that is needed for process forecasting within a real-time nonlinear optimization framework that is critical for supporting process control. In this study, an integrated simulation-optimization approach was developed for supporting real-time dynamic modeling and process control of surfactant-enhanced remediation at petroleum-contaminated sites. Subsurface modeling is combined with a dual-response surface method to develop a system for generating optimum operation conditions under various site conditions, through the support of a nonlinear optimization model. The developed methodology was applied to a real-world case study in western Canada. Using the developed three-dimensional multiphase and multicomponent model, the surfactant-enhanced remediation process that is being implemented on the study site was simulated. The results provide useful information for further dual-response surface analysis to support the development of an optimization model to determine optimum process operation conditions. Under each initial contaminant concentration, optimum operation conditions can then be identified through this combined dual-response surface method optimization approach. Thus, a decision support system can then be produced to guide decisions of remediation process control under various site conditions.
Models which solve the governing groundwater flow or solute transport
equations in conjunction with optimization techniques, such as linear
and quadratic programing, are powerful aquifer management tools.
Groundwater management models fall in two general categories: hydraulics
or policy evaluation and water allocation. Groundwater hydraulic
management models enable the determination of optimal locations and
pumping rates of numerous wells under a variety of restrictions placed
upon local drawdown, hydraulic gradients, and water production targets.
Groundwater policy evaluation and allocation models can be used to study
the influence upon regional groundwater use of institutional policies
such as taxes and quotas. Furthermore, fairly complex
groundwater-surface water allocation problems can be handled using
system decomposition and multilevel optimization. Experience from the
few real world applications of groundwater optimization-management
techniques is summarized. Classified separately are methods for
groundwater quality management aimed at optimal waste disposal in the
subsurface. This classification is composed of steady state and
transient management models that determine disposal patterns in such a
way that water quality is protected at supply locations. Classes of
research missing from the literature are groundwater quality management
models involving nonlinear constraints, models which join groundwater
hydraulic and quality simulations with political-economic management
considerations, and management models that include parameter
uncertainty.
A management model is presented for determining the maximum concentration of a transient pollutant source such that space-dependent groundwater quality requirements are met. The method is illustrated using a one-dimensional groundwater system with a single pollutant source at an adjacent stream. The chloride concentration of the source is to be managed to meet variable groundwater quality constraints. Management of the pollutant source is demonstrated for a single-period as well as for repeated-period pollutant discharges. The Crank-Nicolson numerical approximation of the convective-dispersive equation is used in the management model. The pollutant source concentration is treated as a parameter in the resulting system of linear equations. Taking advantage of the block structure of the matrix, the concentrations throughout the system are defined and manipulated as functions of this parameter for each time step. The parameter is maximized by comparison of groundwater quality limits with the groundwater solute concentrations at the corresponding nodes. The minimum value of the parameter over the entire time frame is the maximum concentration allowable in the source water over the management period or periods.
This work presents new results on a method for optimal aquifer remediation when available information is limited. The methodology combines computer simulation models of solute transport and fate, descriptions of spatial variability, probabilistic analysis of uncertainty, and optimization. The objective is to find the most cost-effective management policy for aquifer decontamination. Advantages of the method include the following: (1) it utilizes measurements in real time, (2) it simultaneously estimates aquifer parameters and makes decisions for remediation, and (3) it devises a more cost-effective and reliable aquifer remediation strategy than deterministic optimization, specifically, the method known as deterministic feedback control. Subject to constraints and for a given reliability of meeting water quality standards, this method minimizes the expected value of the cost in the remaining periods. That is, because of incomplete information about the site the cost of a decontamination strategy is not known a priori. The objective is to minimize the cost weighted by the probability that it will be incurred. The optimal aquifer management policy is expressed as the sum of a deterministic and a stochastic control term. The former is obtained by solving a deterministic optimization problem through constrained differential dynamic programming, and the latter is obtained by a perturbation approximation to the stochastic optimal control problem. Extended Kalman filtering is incorporated into the optimization method to improve the accuracy of the estimated state and parametric variables using available measurements. A hypothetical contamination case with two-dimensional unsteady flow and transport for a persistent solute is studied to illustrate the applicability of the methodology. The effectiveness in terms of cost and reliability of the proposed method is studied under various conditions and then compared with the cost and reliability of the deterministic feedback control method through Monte Carlo simulations. The proposed methodology is shown to be superior to deterministic feedback control.
A simulation-management methodology is demonstrated for the rehabilitation of aquifers that have been subjected to chemical contamination. Finite element groundwater flow and contaminant transport simulation are combined with nonlinear optimization. The model is capable of determining well lo-cations plus pumping and injection rates for groundwater quality control. Examples demonstrate linear or nonlinear objective functions subject to linear and nonlinear simulation and water management constraints. Restrictions can be placed on hydraulic heads, stresses, and gradients, in addition to con-taminant concentrations and fluxes. These restrictions can be distributed over space and time. Three design strategies are demonstrated for an aquifer that is polluted by a constant contaminant source: they are pumping for contaminant removal, water injection for in-ground dilution, and a pumping, treatment, and injection cycle. A transient model designs either contaminant plume interception or in-ground dilution so that water quality standards are met. The method is not limited to these cases. It is generally applicable to the optimization of many types of distributed parameter systems.
A method is presented for determining the optimal well locations and steady-state pumping scheme for dewatering a site. It uses the finite-difference approximations of the ground-water differential equation as constraints in a linear programming model. The method was used to determine optimal well distributions and steady-state pumping patterns for dewatering a dry dock excavation site. Because the currently available LP method is restricted to steady-state solutions, auxiliary use was made of the preceding transient numerical model to predict dewatering times.
This book is intended as an introduction to the various numerical approaches to the simulation of subsurface flow. Groundwater flow, unsaturated flow, flow in fractured media, solute and energy transport, geothermal flows, oil and gas reservoirs and land subsidence are all considered. The main emphasis is placed on the finite element and finite difference methods. Adequate background material on subsurface phenomena is provided for unfamiliar readers.
Countercurrent aeration in a packed tower for removal of volatile organic compounds (VOCs) may be more cost effective than granular activated carbon adsorption. Packed tower process design information for a hypothetical 200-mgd water treatment plant with an influent containing up to 60% nitrified wastewater was obtained from an 11.5-in. diameter pilot stripping tower. Simulated influent water was spiked with high concentrations of five VOCs. This paper demonstrates the techniques used to apply the results to full-scale design. Air stripping theory is applied to the pilot tower results to develop empirical mass transfer data. Correlations developed between mass transfer and liquid loading rate are applied to full-scale design. Alternative designs, full-scale cost estimates, and techniques for determining the optimal (least cost) design are discussed.
An optimal aquifer remediation design model employing a nonlinear programming algorithm was developed to find the minimum cost design of a pump-and-treat aquifer remediation system. The mixed-integer nonlinear programming model includes the discontinuous fixed costs of system construction and installation as well as operation and maintenance. The fixed cost terms in the objective function have been approximated by continuous functions of the decision variables using a polynomial penalty coefficient method resulting in a nonlinear programming formulation of an otherwise mixed-integer nonlinear programming model. Results of applying the new polynomial penalty coefficient method to an example design problem show that a combined well field and treatment process model that includes fixed costs has a significant impact on the design and cost of aquifer remediation systems, reducing system costs by using fewer, larger flow rate wells. Previous pump-and-treat design formulations have resulted in systems with numerous, low flow rate wells due to the use of simplified cost functions that do not exhibit economies of scale or fixed costs. The polynomial penalty coefficient method results were compared to two alternative approximate mixed-integer nonlinear programming methods for solving optimal aquifer remediation design problems, the pseudo-integer method and the exponential penalty coefficient method. The polynomial penalty coefficient method obtains the same solutions and performs as well as or better than the exponential penalty coefficient method. The polynomial penalty coefficient method almost always results in better, less expensive designs and requires significantly less computer time than the pseudo-integer method. 38 refs., 3 figs., 4 tabs.
Information is compiled on more than 135 compounds that may be groundwater pollutants. The compounds profiled include all the Priority Pollutants promulgated by the US EPA under the Clean Water Act (CWA) of 1977. Many of these priority pollutants were included among the Target Compounds promulgated by the EPA under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA) in 1980 and the Superfund Amendments and Reauthorization Act (SARA) of 1986. All chemicals described in the book are classified as priority pollutants and/or target compounds. For each chemical, the following information is given: (1) synonyms; (2) structural formula; (3) CAS Registry number; (4) DOT designation; (5) empirical formula; (6) formula weight; (7) RETCS number; (8) physical and chemical properties; (9) fire hazards; (10) health hazard data; and (11) manufacturing data and/or selected manufacturers.
This article focuses on the technical and economic feasibility of treating air stripper off-gas with granular activated carbon (GAC). For dichloroethene and trichloroethene, air stripping followed by off-gas GAC treatment was shown to be very effective and economical compared with aqueous-phase GAC treatment, with GAC usage rates for gas-phase adsorption being less than one half those for aqueous-phase adsorption. Also, because gas-phase adsorption kinetics are much faster than liquid-phase kinetics, the required bed depth and diameter are much smaller for gas-phase beds. Steam regeneration was found to be ineffective for regeneration gas-phase GAC at low concentrations. Este artículo enfoca sobre el uso de modelos para determinar el costo de tratar gas maio del estropeo de aire con carbón granulado activado (CGA), razones de uso de CGA, y el diseno de lechos y examina la viabilidad de regeneración de vapor del CGA. Para dicloroeteno y tricloroeteno, estropeo de aire seguido por tratamiento de CGA de gas maio se mostró ser muy efectivo y económico comparado con tratamiento de CGA en fase acuoso. Razones de uso de CGA para adsorción del fase de gas fueron menos del medio de esos para adsorción de fase acuoso. También, por causa que los cinéticos de adsorción del fase degas son mucho más rápido que los cinéticos del fase líquido, la profundidad y diámetro de lecho requisitos son muchos más pequeño para lechos de fase de gas. Los resultados de los estudios de la regeneración de vapor fueron inconcluyentes.
An air-stripping tower was designed to treat 1500 gpm (0.09464 mVs) of groundwater contaminated with trichloroethene, tetrachloroethene, os-l, 2-dichloroethene, toluene, ethyl benzene, and isomers of xylene. Á simplified procedure for designing a tower with the smallest volume and energy requirements was used. Data for the first four months of operation demonstrated excellent removals for the six volatile organic compounds. The cost of treatment was about $ 0.0586/1000 gal (3.785 m 3 ) of water treated. Operating problems, microbiological analyses, and data regarding total organic halogen (TOX) and TOX formation potential are summarized. Una torre que despeja aire fué diseñada para tratar 1500 gpm (0.09464 m³/s) de aguas subterráneas contaminadas con triclooeteno, tetracloroeteno, ds-1,2-dicloroeteno, tolueno, benceno etilo, y isómeros de xileno. Se usò un proceso simplificado para diseñar una torre conrequisitos menores de volumen y energía. Información de los primeros cuatro meses de operación demonstro excelente resultados para eliminar los seis compuestos volatiles orgánicos. El tratamiento costò cerca de $0.0586/1000 gal (3785 L) de agua tratada. Se resumen problemas de operación, análises de microbiologia, e información considerando el halógeno orgánico total y el potencial de formación dei halógeno orgânico total.
Chlorohydrocarbon solvents in concentrations up to 1 mg/L are being found with disturbing frequency in ground and surface water supplies across the US. In addition, trihalomethane levels in many water systems exceed the federal standard of 0.1 mg/L. Preliminary analyses suggest that aeration may be a cost-effective process for removal of these trace organic contaminants. The authors have developed a procedure for the process design of air stripping in packed towers for the removal of volatile organic contaminants, which follow Henry's law at low concentrations. The design methodology incorporates the effects on system design of water and air temperature, volatility of the contaminant, and type and size of packing. It provides an analytical method for estimating the relative cost of air stripping for removal of volatile organic compounds, as well as a quantitative framework for design and evaluation of pilot plant studies.
Simulated annealing is introduced and applied to the optimization of
groundwater management problems cast in combinatorial form. This
heuristic, probabilistic optimization method seeks minima in analogy
with the annealing of solids and is effective on large-scale problems.
No continuity requirements are imposed on objective (cost) functions.
Constraints may be added to the cost function via penalties, imposed by
designation of the solution domain, or imbedded in submodels (e.g., mass
balance in aquifer flow simulators) used to evaluate costs. The location
of global optima may be theoretically guaranteed, but computational
limitations lead to searches for nearly optimal solutions in practice.
Like other optimization methods, most of the computational effort is
expended in flow and transport simulators. Practical algorithmic
guidance that leads to enormous computational savings and sometimes
makes simulated annealing competitive with gradient-type optimization
methods is provided. The method is illustrated by example applications
to idealized problems of groundwater flow and selection of remediation
strategy, including optimization with multiple groundwater control
technologies. They demonstrate the flexibility of the method and
indicate its potential for solving groundwater management problems. The
application of simulated annealing to water resources problems is new
and its development is immature, so further performance improvements can
be expected.
The problem of designing contaminated groundwater remediation systems
using hydraulic control is addressed. Two nonlinear optimization
formulations are proposed which model the design process for the
location and pump rates of injection and extraction wells in an aquifer
cleanup system. The formulations are designed to find a pumping system
which (1) removes the most contaminant over a fixed time period and (2)
reduces contaminant concentration to specified levels by the end of a
fixed time period at least cost. The formulations employ a
two-dimensional Galerkin finite element simulation model of steady state
groundwater flow and transient convective-dispersive transport. To make
the optimization problems computationally tractable sensitivity theory
is used to derive a general relationship for computing the derivatives
of an arbitrary function of the simulation outputs with respect to model
inputs. This relationship is then applied to the convective-dispersive
transport equation.
A mathematical model for optimal conjunctive utilization of the
groundwater quality and quantity resources of unconfined aquifers is
presented. The saturated zone of the groundwater system is considered as
a component of a regional waste treatment system involving a waste water
treatment plant and external sources of dilution water. The model
minimizes the cost associated with surface waste treatment while it
maintains acceptable water quality levels throughout the aquifer. The
results indicate the feasibility of secondary treatment (trickling
filter) plus dilution water along with the assimilation capacity of the
aquifer for waste water degradation.
Groundwater simulation models have been incorporated into a genetic algorithm to solve three groundwater management problems: maximum pumping from an aquifer; minimum cost water supply development; and minimum cost aquifer remediation. The results show that genetic algorithms can effectively and efficiently be used to obtain globally (or, at least near globally) optimal solutions to these groundwater management problems. The formulation of the method is straightforward and provides solutions which are as good as or better than those obtained by linear and nonlinear programming. Constraints can be incorporated into the formulation and do not require derivatives with respect to decision variables as in nonlinear programming. More complicated problems, such as transient pumping and multiphase remediation, can be formulated and solved using this method. The computational time required for the solution of genetic algorithm groundwater management models increases with the complexity of the problem. The speedup attainable by solving genetic algorithm problems on massively parallel computers is significant for problems where the simulation time required to complete each generation is high.
A successive approximation linear quadratic regulator (SALQR) method with management periods is combined with a finite element groundwater flow and transport simulation model to determine optimal time-varying groundwater pump-and-treat reclamation policies. Management periods are groups of simulation time steps during which the pumping policy remains constant. In an example problem, management periods reduced the total computational demand, as measured by the CPU time, by as much as 85% compared to the time needed for the SALQR solution without management periods. Conversely, the optimal costs increased as the number of times that the control can change is reduced. With two simulation periods per management period, the optimal cost increased by less than 1% compared to the optimal cost with no management periods, yet the computational work was reduced by a third. The optimal policies, including the number and locations of wells, changed significantly with the number of management periods. Complexity analysis revealed that the SALQR algorithm with management periods can significantly reduce the computational requirements for nonsteady optimization of groundwater reclamation and other management applications.
Attempts to store fluids in confined aquifers will sometimes be frustrated by regional groundwater flow and/or by buoyancy drift due to density differences between the stored fluid and the native groundwater. Such effects can largely be overcome through the use of gradient control wells. A procedure based on linear programing can be used for the initial design of a well field that will create a zero gradient or a finite gradient in a given region. The finite difference form of the steady state groundwater equation provides one set of constraints, while the gradient condition in the storage region provides a second set. A standard linear programing solution routine is then used to provide the minimum pumping rates and head distribution consistent with the constraints and the chosen well array.
The contents of this book are: Groundwater resources; Groundwater flow equations; Groundwater quality--the mass transport problem; Numerical methods in groundwater management; Optimization methods for groundwater management; Groundwater supply management models; Groundwater quality management models; and The inverse problem in groundwater systems.
Accurate autocorrelation specification is important in stochastic represnetation of heterogeneous reservoirs. The author studies the bias and precision of autocorrelation estimates using both analytical and numerical methods in multiple dimensions. The author uses the Turning Bands Method in this study to generate stochastic fields to represent spatial heterogeneity. Extensions include transformation to non-normal distributions, geometric anisotropy in correlation, nested correlation structure, and addition of nonstationarity. The Matrix Decomposition Method is also discussed. He treats random fractals as special correlation models and uses them in the same generation methods. He extends the study of finite difference truncation error to heterogeneous anisotropic media. When approximating the pressure equation, a 9-point FD scheme with heterogeneous media or in the presence of a source term is no more accurate than a 5-point scheme. Discontinuity in heterogeneity changes the truncation error from second-order to first-order with uniform grid and from first-order to zero-order with non-uniform grid. He studies the effect of spatial variation of permeability on single-well tracer tests first within a single layer using numerical simulation and then with a layered model analytically. The shifting toward a one-dimensional flow patter, caused by anisotropy or high permeability channels, reduces the apparent dispersivity. Unequal depths of investigation among the layers reduces the apparent dispersivity. The separation of the tracer fronts when they arrive at the well from different layers, caused by flow irreversibility, increases the apparent dispersivity. Irreversible flow can result from compressibility and permeability variation among the layers.
The use of this material will give an estimate of the well and pump costs for projects requiring a given capacity. This is intended only as an instrument for establishing orders of magnitude as a basis for comparisons, and of course does not substitute for detailed engineering studies.
Well cost data were analyzed for three categories according to the aquifer tapped: sand and gravel, shallow bedrock, and deep sandstone. In the sand and gravel category, tubular and gravel packed wells finished in the glacial materials above bedrock were considered separately.
1. AbstractA hydraulic control optimization model is applied to the conceptual and implementation analysis of a ground-water remediation system in coastal New Jersey. The site is modeled using a distributed parameter finite-difference model containing 36,000 nodes within five layers. The conceptual problem is to determine the feasibility of producing a capture zone which encompasses the entire existing plume while recharging all extracted water within property boundaries in such a way that the recharged water satisfies criteria on its fate. The conceptual analysis problem is formulated as a linear program in which the total extraction pumping is minimized, and requirements are placed on hydraulic heads and gradients in both horizontal and vertical directions. A requirement is also made that all extracted water be recharged to the subsurface. The model is used for determination of the feasibility of the remediation concept. Details of constructing constraints for a large-scale formulation are presented. The concept of constraint calibration, using particle tracking to insure that constraints are producing desired results, is introduced and demonstrated. The optimization formulation is used for detailed implementation analysis of the remediation system. A number of techniques for modifying elements of the conceptual model results, such as unrealistically small pump rates, are described. The optimization approach is found to be useful for determining the feasibility of the remedial strategy at this site and for producing results which can be used as a starting point for detailed analysis of the remediation strategy.
Detailed two-dimensional flow simulation of a complex ground-water system is combined with quadratic and linear programming to evaluate design alternatives for rapid aquifer restoration. The design model ensures that a contaminant plume is removed and treated within four years at the least possible cost. Rapid restoration is accomplished by maintaining specified velocities around the plume perimeter toward a group of pumping wells located near the plume center. Ground-water velocities are adjusted to include the effects of solute retardation due to sorption. As a simplification, the model does not account for hydrodynamic dispersion. Results show how treatment and pumping costs depend dynamically on the type of treatment process, the capacity of pumping and injection wells, and the number of wells. The design for an inexpensive treatment process minimizes pumping costs, while an expensive process results in the minimization of treatment costs. Substantial reductions in pumping costs occur with increases in injection capacity or in the number of wells. Treatment costs are reduced by expansions in pumping capacity or injection capacity. The analysis identifies maximum pumping and injection capacities.
Maintenance of the high ground-water velocities required by rapid restoration strategies is difficult and expensive. In order to meet the four-year target date, about ten times the initial volume of contaminated water must be pumped and treated. Rapid restoration forces large pumping volumes and high costs.
Methods of combining numerical simulation models derived from partial differential equations and numerical optimization techniques are explored in the context of engineering design. Techniques for incorporating simulation models into an optimization framework are presented. An attempt is made to devise hydraulic control systems for contaminated groundwater aquifer remediation. A series of nonlinear optimization formulations are proposed which quantify design objectives and constraints for designing the location and pump rates at injection/extraction wells in an aquifer cleanup system. The different formulations are designed to find the pumping system which: removes the most contaminant over a fixed time period, reduces contaminant concentration to specified levels by the end of a fixed time period at least cost, and prevents the contaminant plume from migrating further than its current position at least cost. The nonlinear optimization formulations represent groundwater physics by incorporating a two dimensional Galerkin finite element model of the convective-dispersive transport equation.
Typescript (photocopy) Thesis (M.S.)--Cornell University, August, 1984. Bibliography: leaves 158-161.
Volatile organic scans: Implications for ground water monitoring
- R H Plumb
- A M Pitchford
Plumb, R. H. Jr., and Pitchford, A. M. (1985). "Volatile organic scans: Implications for ground water monitoring." Proc.@BULLET Pet. Hydrocarbons and Organic Chemicals in Ground Water: Prevention, Detection and Restoration, Nat'1. Water Well Assoc.-Am. Pet. Inst., Houston, Tex., 207-222.
Selecting among treatment options for removing synthetic organics from water
- B I Dvorak
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