Cost Optimization of DNAPL Remediation at Dover Air Force Base Site

This study investigates stochastic optimization of dense nonaqueous phase liquid (DNAPL) remediation design at Dover Air Force Base Area 5 using emulsified vegetable oil (EVO) injection. The Stochastic Cost Optimization Toolkit (SCOToolkit) is used for the study, which couples semianalytical DNAPL source depletion and transport models with parameter estimation, error propagation, and stochastic optimization modules that can consider multiple sources and remediation strategies. Model parameters are calibrated to field data conditions on prior estimates of parameters and their uncertainty. Monte Carlo simulations are then performed to identify optimal remediation decisions that minimize the expected net present value (NPV) cleanup cost while maintaining concentrations at compliance wells under the maximum contaminant level (MCL). The results show that annual operating costs could be reduced by approximately 50% by implementing the identified optimal remediation strategy. We also show that recalibration and reoptimization after 50 years using additional monitoring data could lead to a further 60% reduction in annual operating cost increases the reliability of the proposed remediation actions.     

A bilevel groundwater management model with minimization of stochastic health risks at the leader level and remediation cost at the follower level

Traditional single-objective programs cannot deal with the tradeoffs between the decision makers who represent different perspectives and have inconsistent decision goals. Multi-objective ones can hardly represent a complex dominant-subordinate relationship between the leader and the follower. This study presents a new bilevel programming model with considering leader–follower-related health-risk and economic goals for optimal groundwater remediation management. The bilevel model is formulated by integrating health-risk assessment and environmental standards (the leader or the environmental concern) and remediation cost (the follower or the economic concern) into a general framework. In addition, stochastic uncertainty in health risk assessment is considered into the decision-making process. The developed bilevel model is then applied to a petroleum-contaminated aquifer in Canada. Results indicate that the performance of bilevel programming can not only meet the low remediation cost as the expectation from the follower but also simultaneously conform to the low contamination level as the expectation from the leader. Furthermore, comparative analyses show that the bilevel model with two-level concerns has the advantage of maximizing the interests and satisfaction degrees of decision makers, which can avoid the extreme results generated from the single-level models.     

A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty

In this study a simulation-based fuzzy chance-constrained programming (SFCCP) model is developed based on possibility theory. The model is solved through an indirect search approach which integrates fuzzy simulation, artificial neural network and simulated annealing techniques. This approach has the advantages of: (1) handling simulation and optimization problems under uncertainty associated with fuzzy parameters, (2) providing additional information (i.e. possibility of constraint satisfaction) indicating that how likely one can believe the decision results, (3) alleviating computational burdens in the optimization process, and (4) reducing the chances of being trapped in local optima. The model is applied to a petroleum-contaminated aquifer located in western Canada for supporting the optimal design of groundwater remediation systems. The model solutions provide optimal groundwater pumping rates for the 3, 5 and 10 years of pumping schemes. It is observed that the uncertainty significantly affects the remediation strategies. To mitigate such impacts, additional cost is required either for increased pumping rate or for reinforced site characterization.     

Characterization of groundwater contaminant source using Bayesian method

Contaminant source identification in groundwater system is critical for remediation strategy implementation, including gathering further samples and analysis, as well as implementing and evaluating different remediation plans. Such problem is usually solved with the aid of groundwater modeling with lots of uncertainty, e.g. existing uncertainty in hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This paper presents an application of a dynamic framework coupling with a three dimensional groundwater modeling scheme in contamination source identification of groundwater. Markov Chain Monte Carlo (MCMC) is being applied to infer the possible location and magnitude of contamination source. Uncertainty existing in heterogonous hydraulic conductivity field is explicitly considered in evaluating the likelihood function. Unlike other inverse-problem approaches to provide single but maybe untrue solution, the MCMC algorithm provides probability distributions over estimated parameters. Results from this algorithm offer a probabilistic inference of the location and concentration of released contamination. The convergence analysis of MCMC reveals the effectiveness of the proposed algorithm. Further investigation to extend this study is also discussed.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance

The objective of in situ thermal treatment is typically to reduce the contaminant mass or average soil concentration below a specified value. Evaluation of whether the objective has been met is usually made by averaging soil concentrations from a limited number of soil samples. Results from several field sites indicate large performance uncertainty using this approach, even when the number of samples is large. We propose a method to estimate average soil concentration by fitting a log normal probability model to thermal mass recovery data. A statistical approach is presented for making termination decisions from mass recovery data, soil sample data, or both for an entire treatment volume or for subregions that explicitly considers estimation uncertainty which is coupled to a stochastic optimization algorithm to identify monitoring strategies to meet objectives with minimum expected cost. Early termination of heating in regions that reach cleanup targets sooner enables operating costs to be reduced while ensuring a high likelihood of meeting remediation objectives. Results for an example problem demonstrate that significant performance improvement and cost reductions can be achieved using this approach.     

The New Potential for Understanding Groundwater Contaminant Transport

The groundwater remediation field has been changing constantly since it first emerged in the 1970s. The remediation field has evolved from a dissolved‐phase centric conceptual model to a DNAPL‐dominated one, which is now being questioned due to a renewed appreciation of matrix diffusion effects on remediation. Detailed observations about contaminant transport have emerged from the remediation field, and challenge the validity of one of the mainstays of the groundwater solute transport modeling world: the concept of mechanical dispersion (Payne et al. 2008). We review and discuss how a new conceptual model of contaminant transport based on diffusion (the usurper) may topple the well‐established position of mechanical dispersion (the status quo) that is commonly used in almost every groundwater contaminant transport model, and evaluate the status of existing models and modeling studies that were conducted using advection‐dispersion models.     

Stochastic Cost Optimization of Multistrategy DNAPL Site Remediation

This paper investigates numerical optimization of dense nonaqueous phase liquid (DNAPL) site remediation design considering effects of prediction and measurement uncertainty. Results are presented for a hypothetical problem involving remediation using thermal source reduction (TSR) and bioremediation with electron donor (ED) injection. Pump-and-treat is utilized as a backup measure if compliance criteria are not met. Remediation system design variables are optimized to minimize expected net present value (ENPV) cost. Adaptive criteria are assumed for real-time control of TSR and ED duration. Source zone dissolved concentration data enabled more reliable and lower cost operation of TSR than soil concentration data, but using both soil and dissolved data improved results sufficiently to more than offset the additional cost. Decisions to terminate remediation and monitoring or to initiate pump-and-treat are complicated by measurement noise. Simultaneous optimization of monitoring frequency, averaging period, and lookback periods to confirm decisions, in addition to remediation design variables, reduced ENPV cost. Results indicate that remediation design under conditions of uncertainty is affected by subtle interactions and tradeoffs between design variables, compliance rules, site characteristics, and uncertainty in model predictions and monitoring data. Optimized designs yielded cost savings of up to approximately 50% compared with a nonoptimized design based on common engineering practices. Significant improvements in accuracy and reductions in cost were achieved by recalibrating the model to data collected during remediation and re-optimizing design variables. Repeating this process periodically is advisable to minimize total costs and maximize reliability.     

Development and application of a stochastic optimization model for groundwater management: crop pattern and conjunctive use consideration

The need for irrigation water in arid and semi-arid regions is mostly supplied by groundwater. Furthermore, the agricultural development in these areas is not generally based on a comprehensive plan, which can cause aquifers depletion. On the other hand, to properly manage an aquifer and to have an optimal crop plan, the stochastic nature of the different parameters of a groundwater system such as groundwater recharge and water demands should be taken into consideration. In this paper, we develop an explicit stochastic optimization model for Firouzabad aquifer in Iran. This formulation is based on the first and second moment analysis for groundwater head which has been initially proposed for surface water resources management by Fletcher and Ponnambalam. We extend the model to create a new random withdrawal policy for conjunctive use setting in which the randomness in available precipitation is taken into account. The interesting point is that the model provides the respective probabilities of shortage and surplus without imposing the extra decision variables into the optimization model. A genetic-based algorithm is used to solve the stochastic nonlinear and non-convex formulation. The outcome results indicate that the current crop pattern should be changed, that is, the allocated areas of some crops have to be meaningfully reduced. Finally, to validate our model efficiency, we demonstrate that how much close the statistical characteristics obtained from the optimization model are to those estimated from the Monte Carlo simulation. Furthermore, the optimal benefits obtained using the proposed optimization model are as suitable as the benefits achieved using the corresponding Monte Carlo-based optimization model.     

Applying ANN emulators in uncertainty assessment of flood inundation modelling: a comparison of two surrogate schemes

Abstract

A generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic inundation maps, but with reduced computational requirements. The results also revealed that GAE-S1 possessed a slightly better performance in accuracy (referencing to GLUE) than GAE-S2, but a lower flexibility in application. This study shed some light on how to apply different surrogate schemes in using numerical models for uncertainty assessment, and could help decision makers in choosing cost-effective ways of conducting flood risk analysis.     

A stochastic optimization framework for the restoration of an over-exploited aquifer

ABSTRACT

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.

Editor Z.W. Kundzewicz; Guest editor S. Weijs     

Groundwater Modeling with Nonlinear Uncertainty Analyses to Enhance Remediation Design Confidence

Soil and groundwater contamination are often managed by establishing on‐site cleanup targets within the context of risk assessment or risk management measures. Decision‐makers rely on modeling tools to provide insight; however, it is recognized that all models are subject to uncertainty. This case study compares suggested remediation requirements using a site‐specific numerical model and a standardized analytical tool to evaluate risk to a downgradient wetland receptor posed by on‐site chloride impacts. The base case model, calibrated to observed non‐pumping and pumping conditions, predicts a peak concentration well above regulatory criteria. Remediation scenarios are iteratively evaluated to determine a remediation design that adheres to practical site constraints, while minimizing the potential for risk to the downgradient receptor. A nonlinear uncertainty analysis is applied to each remediation scenario to stochastically evaluate the risk and find a solution that meets the site‐owner risk tolerance, which in this case required a risk‐averse solution. This approach, which couples nonlinear uncertainty analysis with a site‐specific numerical model provides an enhanced level of knowledge to foster informed decision‐making (i.e., risk‐of‐success) and also increases stakeholder confidence in the remediation design.     

Multi-objective optimization for sustainable groundwater resource management in a semiarid catchment

Abstract

Groundwater is an important water resource and its management is vital for integrated water resources development in semiarid catchments. The River Shiyang catchment in the semiarid area of northwestern China was studied to determine a sustainable multi-objective management plan of water resources. A multi-objective optimization model was developed which incorporated water supplies, groundwater quality, ecology, environment and economics on spatial and temporal scales under various detailed constraints. A calibrated groundwater flow model was supplemented by grey simulation of groundwater quality, thus providing two lines of evidence to use in the multi-objective water management. The response matrix method was used to link the groundwater simulation models and the optimization model. Multi-phase linear programming was used to minimize and compromise the objectives for the multi-period, conjunctive water use optimization model. Based on current water demands, this water use optimization management plan was able to meet ecological, environmental and economic objectives, but did not find a final solution to reduce the overall water deficit within the catchment.     

A multistage simulation-based optimization model for water resources management in Tarim River Basin, China

In this study, a multistage simulation-based optimization model is developed for supporting water resources management under uncertainty. The system couples a lumped rainfall-runoff model with an inexact multistage stochastic program, where its random parameter is provided by the statistical analysis of simulation outcomes. Moreover, penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The developed model can also reflect dynamic features of the system conditions through transactions at discrete points in time over a multistage context. The developed model is applied to a real case of planning water resources management in Tarim River Basin, which is one of the most serious water-shortage regions of China. A variety of policies associated with different water-allocation targets are analyzed. The results are helpful for decision makers identifying optimal water-allocation plans for mitigating the conflict among ecological protection, economic development, and regional sustainability.     

Extending the Capture Map Concept to Estimate Discrete and Risk-Based Streamflow Depletion Potential

A popular and contemporary use of numerical groundwater models is to estimate the discrete relation between groundwater extraction and surface-water/groundwater exchange. Previously, the concept of a “capture map” has been put forward as a means to effectively summarize this relation for decision-making consumption. While capture maps have enjoyed success in the environmental simulation industry, they are deterministic, ignoring uncertainty in the underlying model. Furthermore, capture maps are not typically calculated in a manner that facilitates analysis of varying combinations of extraction locations and/or reaches. That is, they are typically constructed with focus on a single reach or group of reaches. The former of these limitations is important for conveying risk to decision makers and stakeholders, while the latter is important for decision-making support related to surface-water management, where future foci may include reaches that were not the focus of the original capture analysis. Herein, we use the concept of a response matrix to generalize the theory of the capture-map approach to estimate spatially discrete streamflow depletion potential. We also use first-order, second-moment uncertainty estimation techniques with the concept of “risk shifting” to place capture maps and streamflow depletion potential in a stochastic, risk-based framework. Our approach is demonstrated for an integrated groundwater/surface-water model of the lower San Antonio River, Texas, USA.     

A multiphase flow and multispecies reactive transport model for DNAPL-involved Compound Specific Isotope Analysis

This study presents a multiphase flow and multispecies reactive transport model for the simultaneous simulation of NAPL and groundwater flow, dissolution, and reactive transport with isotope fractionation, which can be used for better interpretation of NAPL-involved Compound Specific Isotope Analysis in 3D heterogeneous hydrogeologic systems. The model was verified for NAPL-aqueous phase equilibrium partitioning, aqueous phase multi-chain and multi-component reactive transport, and aqueous phase multi-component transport with isotope fractionation. Several illustrative examples are presented to investigate the effect of DNAPL spill rates, degradation rate constants, and enrichment factors on the temporal and spatial distribution of the isotope signatures of chlorinated aliphatic hydrocarbon groundwater plumes. The results clearly indicate that isotope signatures can be significantly different when considering multiphase flow within the source zone. A series of simulations indicate that degradation and isotope enrichment compete with dissolution to determine the isotope signatures in the source zone: isotope ratios remain the same as those of the source if dissolution dominates the reaction, while heavy isotopes are enriched in reactants along groundwater plume flow paths when degradation becomes dominant. It is also shown that NAPL composition can change from that of the injected source due to the partitioning of components between the aqueous and NAPL phases even when degradation is not allowed in NAPL phase. The three-dimensional simulation is presented to mechanistically illustrate the complexities in determining and interpreting the isotopic signatures with evolving DNAPL source architecture.     

How Effective Is Thermal Remediation of DNAPL Source Zones in Reducing Groundwater Concentrations?

Dense nonaqueous phase liquid (DNAPL) source areas containing chlorinated volatile organic compounds (cVOCs) such as trichloroethene (TCE) and perchloroethene (PCE) often give rise to significant dissolved plumes in groundwater, leading to the closure of downgradient water supply wells and creating vapor intrusion issues in buildings located above the plume. Hydraulic containment via pump‐and‐treat has often been implemented to limit migration but must continue indefinitely. Removal of the DNAPL source area by means such as in situ thermal remediation (ISTR) offers the potential to diminish or end the need for hydraulic containment if the associated dissolved plume attenuates sufficiently following source removal. A question often raised is whether this occurs or whether the back diffusion of contaminants from secondary sources such as low‐permeability lenses in the dissolved plume precludes it. The authors conducted DNAPL source removal using ISTR at dozens of sites. This paper presents a compilation of cases—10 separate DNAPL source areas at five project sites—where data indicate that the implementation of a thorough ISTR in a DNAPL source area can result in the attenuation of the associated dissolved plume, such that in several cases, long‐standing pump‐and‐treat systems could be turned off. Our findings contrast with recent assertions that aggressive source remediation may not be justifiable because dissolved plume concentrations will not decline sufficiently. We show that the application of ISTR can result in the thorough removal of the DNAPL source, effective diminution of dissolved plume groundwater concentrations, and achievement of drinking water standards.     

Multi‐objective risk‐informed design of floor isolation systems

The design of floor isolation systems (FISs) for the protection of acceleration sensitive contents is examined considering multiple objectives, all quantified in terms of the probabilistic system performance. The competing objectives considered correspond to (i) maximization of the level of protection offered to the sensitive content (acceleration reduction) and (ii) minimization of the demand for the isolator displacement capacity and, more importantly, for the appropriate clearance to avoid collisions with surrounding objects (floor displacement reduction). Both of these objectives are probabilistically characterized utilizing a versatile, simulation‐based framework for quantifying seismic risk, addressing all important uncertainties related to the seismic hazard and the structural model. FIS performance is assessed through time‐history analysis, allowing for all important sources of nonlinearity to be directly addressed in the design framework. The seismic hazard is described through a stochastic ground motion model. For efficiently performing the multi‐objective optimization, an augmented surrogate modeling methodology is established, considering development of a single metamodel with respect to both the uncertain model parameters and the design variables for the FIS system. This surrogate model is then utilized to simultaneously support the probabilistic risk assessment and the design optimization to provide the Pareto front of dominant designs. Each of these designs establishes a different compromise between the considered risk‐related objectives offering a variety of potential options to the designer. Within the illustrative example, the efficiency of the established framework is exploited to compare three different FIS implementations, whereas the impact of structural uncertainties on the optimal design is also evaluated. Copyright © 2016 John Wiley & Sons, Ltd.     

Check dam layout optimization on the stream network for flood mitigation: surrogate modelling with uncertainty handling

The configuration of check dams and their numbers throughout a basin are important factors for reducing floods in downstream reaches of rivers. In this paper, a stochastic model based on surrogate modelling and Monte Carlo simulation, linked to an evolutionary optimization tool, is developed to assign the optimal sites and number of check dams on a stream network. To handle uncertainty of rainfall variables and their correlation structures, the copula method is employed and an artificial neural network (ANN) is used to emulate the computationally expensive hydrological model, HEC-HMS, within the optimization routines. The prepared modelling framework is applied to a mountainous basin to determine the arrangement of check dams in its sub-basins. The experimental results show that optimal strategies can reduce the expected value of peak flood discharges by up to 50%, with significantly lower costs or number of check dams, relative to a traditional approach with a large number of check dams in sub-basins, presenting a maximum of 21% efficiency.     

Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling

A generalized likelihood uncertainty estimation (GLUE) method incorporating moving least squares (MLS) with entropy for stochastic sampling (denoted as GLUE‐MLS‐E) was proposed for uncertainty analysis of flood inundation modelling. The MLS with entropy (MLS‐E) was established according to the pairs of parameters/likelihoods generated from a limited number of direct model executions. It was then applied to approximate the model evaluation to facilitate the target sample acceptance of GLUE during the Monte‐Carlo‐based stochastic simulation process. The results from a case study showed that the proposed GLUE‐MLS‐E method had a comparable performance as GLUE in terms of posterior parameter estimation and predicted confidence intervals; however, it could significantly reduce the computational cost. A comparison to other surrogate models, including MLS, quadratic response surface and artificial neural networks (ANN), revealed that the MLS‐E outperformed others in light of both the predicted confidence interval and the most likely value of water depths. ANN was shown to be a viable alternative, which performed slightly poorer than MLS‐E. The proposed surrogate method in stochastic sampling is of practical significance in computationally expensive problems like flood risk analysis, real‐time forecasting, and simulation‐based engineering design, and has a general applicability in many other numerical simulation fields that requires extensive efforts in uncertainty assessment. Copyright © 2014 John Wiley & Sons, Ltd.