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.     

Cyclodextrin-Enhanced Solubilization of Organic Contaminants with Implications for Aquifer Remediation

Reagents that enhance the aqueous solubility of non-aqueous phase organic liquid (NAPL) contaminants are under investigation for use in enhanced subsurface remediation technologies. Cyclodextrin, a glucose-based molecule, is such a reagent. In this paper, laboratory experiments and numerical model simulations are used to evaluate and understand the potential remediation performance of cyclodextrin. Physical properties of cyclodextrin solutions such as density, viscosity, and NAPL-aqueous inter-facial tension are measured. Our analysis indicates that no serious obstacles exist related to fluid properties that would prevent the use of cyclodextrin solutions for subsurface NAPL remediation. Cyclodextrin-enhanced solubilization for a large suite of typical ground water contaminants is measured in the laboratory, and the results are related to the physicochemical properties of the organic compounds. The most-hydrophobic contaminants experience a larger relative solubility enhancement than the less-hydrophobic contaminants but have lower aqueous-phase apparent solubilities. Numerical model simulations of enhanced-solubilization flushing of NAPL-contaminated soil demonstrate that the more-hydrophilic compounds exhibit the greatest mass-removal rates due to their greater apparent solubilities, and thus are initially more effectively removed from soil by enhanced-solubilization-flushing reagents. However, the relatively more hydrophobic contaminants exhibit a greater improvement in contaminant mass-removal (compared with water flushing) than that exhibited for the relatively hydrophilic contaminants.     

Chlorinated Decreasing Solvents: Physical-Chemical Properties Affecting Aquifer Contamination and Remediation

Chlorinated degreasing solvents are multicomponent liquids containing not only the chlorinated hydrocarbons with which their name is associated (e.g., trichloroethylene or |TCE]. perchloroethylene or [PCE], 1,1,1-trichlorocihane [TCA]) but also a number of organic additives included as corrosion inhibitors and antioxidants. The additives, such as 1,4-dioxane, are likely to be of significant public-health importance as ground water contaminants due to their toxicity, solubility, and mobility. Following their use in vapor degreasing systems by industry, chlorinated degreasing solvents will also contain about 25% solubilized oil and grease.
A number of physical-chemical properties become especially important in the light of the multicomponent nature of these solvents. First, the higher aqueous solubility and lower sorption of the additives makes it is reasonable to expect that faster moving plumes of these solvent additives will precede plumes of the chlorinated hydrocarbons. Second, due to high losses of chlorinated hydrocarbons by volatilization from vapor degreasers during years in the middle of the century, it is probable that background concentrations of these hydrocarbons are present in ground water flow systems due to their downwind washout. Finally, the solubilized oil and grease may cause profound changes to the wettability of aquifer materials contacted by the solvents during their subsurface migration. It is argued, therefore, that the wettability of aquifer materials contaminated by chlorinated degreasing solvents needs to be experimentally determined before remediation of DNAPL at each site, rather than being simply assumed as water wet.     

Prediction of Remediation of a Heterogeneous Aquifer: A Case Study

Contaminant plumes whose characteristic length is smaller than the horizontal integral scale of the hydraulic conductivity, K, are abundant in shallow, phreatic aquifers. In such cases, the aquifer can be regarded as layered, with K being only a function of the vertical coordinate. The heterogeneity of K has a critical role upon the efficiency of remediation of such sites, for example, by Pump and Treat schemes. The expected efficiency is a random variable, with uncertainty. Quantifying this uncertainty can be of great importance to decision making. In this study, we focus on a case study in the coastal aquifer of Israel and compare two different approaches for constructing realizations of K: continuous and indicator. We observe a significant difference between the constructed realizations, which results in a considerable difference in the predicted remediation efficiency and its uncertainty. Furthermore, we study the effect of conditioning the realizations by a rather limited number of K data points. We find that the conditioning results in a major reduction of the uncertainty. In addition, we compare the results of the transport model to a simplified semi‐analytical solution that is based on assuming radial flow. We find a good agreement with the three‐dimensional numerical model. This result illustrates that the simplified solution can be used for prediction of the remediation efficiency when the flow at the plume vicinity can be regarded as radial.     

Design and Optimization of a Ground Water Monitoring System Using GIS and Multicriteria Decision Analysis

A GIS-based methodology has been developed to design a ground water monitoring system and implemented for a selected area in Mae-Klong River Basin, Thailand. A multicriteria decision-making analysis has been performed to optimize the network system based on major criteria which govern the monitoring network design such as minimization of cost of construction, reduction of kriging standard deviations, etc. The methodology developed in this study is a new approach to designing monitoring networks which can be used for any site considering site-specific aspects. It makes it possible to choose the best monitoring network from various alternatives based on the prioritization of decision factors.     

Multi-Objective Optimization of Managed Aquifer Recharge

This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady state and transient scenarios. The steady state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global (the non-dominated sorting genetic algorithm [NSGA-2]) and local (the Nelder-Mead downhill simplex search algorithms). The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties, and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared.     

Decision Analysis for Pump-and-Treat Design

The use of decision analysis (DA) has been proposed as a technique for selecting from among alternative designs for subsurface remediation. To assess the ability of DA to generate consistent decisions for the widely practiced pump-and-treat (PAT) strategy, 27 candidate PAT designs were compared for a case study site. The sensitivity of the alternative selection to various modeling assumptions was examined, including the complexity of the site-specific numerical models, the assumed degree of aquifer heterogeneity, the manner of defining failure, and the assumed cost of failure. The initial net-present-worth analysis resulted in the selection of one of two designs that included injection wells for effluent disposal and hydraulic control. However, when the injection wells were excluded from consideration, the selection from a diverse set of alternative PAT designs was highly sensitive to the particular modeling assumptions. In general, the practical usefulness of the DA approach is dependent on the ability to characterize the nature and probability of system failure.     

Application of Multi-Criteria Decision Analysis to Geoelectric and Geologic Parameters for Spatial Prediction of Groundwater Resources Potential and Aquifer Evaluation

Prediction of groundwater resources potential is a spatial decision problem that involves a set of multiple evaluation parameters. In order to produce a groundwater resources potential prediction model of higher reliability and precision in a given study area, the effects of all the important parameters that can contribute to the groundwater occurrence in the area must be integrated. However, the methodology of integrating these parameters such that the relative importance of each is reflected is still a challenge that has not been efficiently handled. In this study, the principle of multi-criteria decision analysis in the context of the analytical hierarchy process is proposed as a technique that can yield a prediction model of higher reliability and precision. The proposed technique was applied to geoelectric and geologic parameters, derived from the results of the interpretation of 2D resistivity imaging data acquired from the study area. The advantage of the proposed technique is that it reduces bias in decision making. The main objective of the study is to produce groundwater potential map for the area. Furthermore, an attempt was also made in the study to characterize the aquifer of the area by estimating the Dar-Zarrouk parameters, using the integration of borehole and 2D resistivity data. The success rate (accuracy) of the prediction was established to be 80 %. Furthermore, the regression line fitted to the aquifer transmissivity and transverse resistance data shows linear relationship with a high regression coefficient of 0.79. The prediction success rate obtained showed that the method proposed in this study is reliable, accurate, and an improved technique of integrating multiple parameters for holistic evaluation of groundwater resources.