A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

IPCS: an integrated process control system for enhanced in-situ bioremediation

To date, there has been little or no research related to process control of subsurface remediation systems. In this study, a framework to develop an integrated process control system for improving remediation efficiencies and reducing operating costs was proposed based on physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks. Process control for enhanced in-situ bioremediation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. Application of the proposed approach to a bioremediation process in a pilot-scale system indicated that it was effective in dynamic optimization and real-time process control of the sophisticated bioremediation systems.     

An intelligent emissions controller for fuel lean gas reburn in coal-fired power plants

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NOx) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NOx reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach. Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NOx emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework. In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.

Implications:A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-off between coal purchase cost and health risk.     

Graphical estimation of the dual-enzyme kinetic parameters for Cr(VI) reduction

Chromium(VI) (Cr(VI)) contamination of soil and groundwater is a major environmental concern. Bioreduction of Cr(VI) by Shewanella oneidensis MR-1 (MR-1) can be considered a feasible option to reduce the toxic and mobile Cr(VI) to the less toxic and less mobile chromium(III) (Cr(III)). The reaction rate expression for Cr(VI) reduction is nonlinear and the rate constants are evaluated by employing nonlinear optimization techniques. The outcome of the optimization techniques, in general, depends on the initial estimate of the kinetic parameters which is not always available. A graphical approach based on sound mathematical reasoning has been developed which is accurate, simpler to use, and can provide the best initial estimate for nonlinear optimization.     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Modeling hydrocarbon biodegradation in tidal aquifers with water-saturation and heat inhibition effects.

A model is developed for hydrocarbon biodegradation, which includes saturated and unsaturated flow, multi-species transport, heat transport, and bacterial growth processes. Numerical accuracy of the model was tested against analytical solutions. The model was also verified against laboratory results for a saturated-flow problem and reasonable match was obtained. Expressions are proposed for inhibition due to water content and temperature fluctuations. Bioactivities under cyclic water content variation were studied under no-flow conditions. A quantitative approach was used to reconcile some of the apparent contradictory conclusions regarding the efficiency of biodegradation of soils under wetting and drying conditions. The efficiency depends on the nature of the oxygenation process. For cases involving the presence of dissolved oxygen and the absence of O2 vapor, subjecting the soil to constant water content close to its optimal value for degradation is most efficient. However, wetting and drying can enhance degradation if O2 is only provided through aeration or direct contact between air and the medium. Also presented are the results of a typical field application of the model and a discussion of the effects of tides, saturation inhibition, and heat inhibition. Other inhibition factors, such as pH or salinity, can be easily incorporated in the formulation. The quantitative approach developed here can be used in assessing bioremediation not only in tidal aquifers but also in areas where water-table or temperature effects are of significance. The approach can be useful in the design of remediation strategies under water-flow or no-flow conditions involving water content and temperature fluctuations.     

污染土壤修复的技术再造与展望

污染土壤的面积在迅速扩大,迫切需要修复、治理;随着土壤污染组分的日益复杂化,等待着全面、高效的修复技术的研制。对污染土壤修复相关技术现状进行剖析表明,现有的各种污染土壤修复技术由于存在着许多技术上难以克服的问题,需要从技术的现有进展和技术构想进行整体意义上的创新,即如何把现有的技术进行参数优化、改造后进行最佳组合与综合,才能取得该技术领域的重大突破。污染土壤的生态化学修复,其实质在于技术的再造,代表了21世纪污染土壤修复技术的发展方向。     

A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.     

Biosurfactant-enhanced solubilization of NAPL mixtures

Remediation of nonaqueous phase liquids (NAPLs) by conventional pump-and-treat methods (i.e., water flushing) is generally considered to be ineffective due to low water solubilities of NAPLs and to mass-transfer constraints. Chemical flushing techniques, such as surfactant flushing, can greatly improve NAPL remediation primarily by increasing the apparent solubility of NAPL contaminants. NAPLs at hazardous waste sites are often complex mixtures. However, the equilibrium and nonequilibrium mass-transfer characteristics between NAPL mixtures and aqueous surfactant solutions are not well understood. This research investigates the equilibrium solubilization behavior of two- and three-component NAPL mixtures (containing akylbenzenes) in biosurfactant solutions. NAPL solubilization is found to be ideal in water (i.e., obeys Raoult's Law), while solubilization in biosurfactant solutions was observed to be nonideal. Specifically, the relatively hydrophobic compounds in the mixture experienced solubility enhancements that were greater than those predicted by ideal enhanced solubilization theory, while the solubility enhancements for the relatively hydrophilic compounds were less than predicted. The degree of nonideality is shown to be a nonlinear function of the NAPL-phase mole fraction. Empirical relationships based on the NAPL-phase mole fraction and/or micelle-aqueous partition coefficients measured in single-component NAPL systems are developed to estimate values for the multicomponent partition coefficients. Empirical relationships that incorporate both the NAPL-phase mole fraction and single-component partition coefficients yield much improved estimates for the multicomponent partition coefficient.     

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.     

Relationship between mass-flux reduction and source-zone mass removal: analysis of field data

The magnitude of contaminant mass-flux reduction associated with a specific amount of contaminant mass removed is a key consideration for evaluating the effectiveness of a source-zone remediation effort. Thus, there is great interest in characterizing, estimating, and predicting relationships between mass-flux reduction and mass removal. Published data collected for several field studies were examined to evaluate relationships between mass-flux reduction and source-zone mass removal. The studies analyzed herein represent a variety of source-zone architectures, immiscible-liquid compositions, and implemented remediation technologies. There are two general approaches to characterizing the mass-flux-reduction/mass-removal relationship, end-point analysis and time-continuous analysis. End-point analysis, based on comparing masses and mass fluxes measured before and after a source-zone remediation effort, was conducted for 21 remediation projects. Mass removals were greater than 60% for all but three of the studies. Mass-flux reductions ranging from slightly less than to slightly greater than one-to-one were observed for the majority of the sites. However, these single-snapshot characterizations are limited in that the antecedent behavior is indeterminate. Time-continuous analysis, based on continuous monitoring of mass removal and mass flux, was performed for two sites, both for which data were obtained under water-flushing conditions. The reductions in mass flux were significantly different for the two sites (90% vs. approximately 8%) for similar mass removals ( approximately 40%). These results illustrate the dependence of the mass-flux-reduction/mass-removal relationship on source-zone architecture and associated mass-transfer processes. Minimal mass-flux reduction was observed for a system wherein mass removal was relatively efficient (ideal mass-transfer and displacement). Conversely, a significant degree of mass-flux reduction was observed for a site wherein mass removal was inefficient (non-ideal mass-transfer and displacement). The mass-flux-reduction/mass-removal relationship for the latter site exhibited a multi-step behavior, which cannot be predicted using some of the available simple estimation functions.     

A coupled simulation-optimization approach for groundwater remediation design under uncertainty: An application to a petroleum-contaminated site

This study provides a coupled simulation–optimization approach for optimal design of petroleum-contaminated groundwater remediation under uncertainty. Compared to the previous approaches, it has the advantages of: (1) addressing the stochasticity of the modeling parameters in simulating the flow and transport of NAPLs in groundwater, (2) providing a direct and response-rapid bridge between remediation strategies (pumping rates) and remediation performance (contaminant concentrations) through the created proxy models, (3) alleviating the computational cost in searching for optimal solutions, and (4) giving confidence levels for the obtained optimal remediation strategies. The approach is applied to a practical site in Canada for demonstrating its performance. The results show that mitigating the effects of uncertainty on optimal remediation strategies (through enhancing the confidence level) would lead to the rise of remediation cost due to the increase in the total pumping rate.     

Establishing and Evaluating the Risk Implications of Uniform Soil Remediation Goals

Abstract

There is growing interest in the establishment of uniform, concentration-based soil remediation goals to accelerate the cleanup of contaminated sites and to provide consistency in the extent of cleanup required for similar sites. If uniform goals are used in a region, post-remediation risk will vary across sites due to regional variability in site properties. Thus far, of the national and state agencies that have established or proposed uniform concentration guidelines, none have quantified the regional variability in risk that is likely to result with their use as remediation goals.

This paper describes a methodology for evaluating regional cancer risk variability resulting from use of uniform, concentration-based soil remediation goals, presents an example application of the methodology, and examines its utility for establishing uniform goals for organic contaminants in soil. The methodology is based on the integrated transport, fate, exposure, and risk model SoilRisk. Also addressed is the issue of contaminants for which regional risk variability is expected to be large with use of uniform soil remediation goals. An approach is described for reducing such variability by development of alternative uniform contaminant concentration levels for categories of sites differentiated by site geographic location or physical-chemical properties.     

Improving petroleum contaminated land remediation decision-making through the MCA weighting process

Internationally petroleum contamination is widespread, posing serious environmental risks including surface and groundwater contamination, thus remediation is essential. The implementation of remediation options is becoming more complex with the increasing influence of stakeholders on the outcome of decision-making processes. Acceptance of remediation schemes during implementation can be increased by involving stakeholders and the public in the decision-making stage. In petroleum remediation involving multiple stakeholders, Multicriteria Analysis has been employed due to its ability to incorporate the preferences of each stakeholder through weighting. The research focused on investigating ways to improve the weighting process. The study demonstrated the utility of SWING, and determined which type of participant and how many participants to include in the decision process, through the application of ELECTRE III and Weighted Summation. It was recommended that a mixture of stakeholders, the public and experts be involved. The total number of participants will be limited by the choice of participatory and weighting methods. The careful selection of participants, as well as the choice of participatory and weighting methods, can minimize the subjectivity involved in MCA weighting, thereby lending decisions in petroleum remediation greater legitimacy.     

Evaluating sources of indoor air pollution

Evaluation of indoor air pollution problems requires an understanding of the relationship between sources, air movement, and outdoor air exchange. Research is underway to investigate these relationships. A three-phase program is being implemented: 1) Environmental chambers are used to provide source emission factors for specific indoor pollutants; 2) An IAQ (Indoor Air Quality) model has been developed to calculate indoor pollutant concentrations based on chamber emissions data and the air exchange and air movement within the indoor environment; and 3) An IAQ test house is used to conduct experiments to evaluate the model results. Examples are provided to show how this coordinated approach can be used to evaluate specific sources of indoor air pollution. Two sources are examined: 1) para-dichlorobenzene emissions from solid moth repellant; and 2) particle emissions from unvented kerosene heaters. The evaluation process for both sources followed the three-phase approach discussed above. Para-dichlorobenzene emission factors were determined by small chamber testing at EPA's Air and Energy Engineering Research Laboratory. Particle emission factors for the kerosene heaters were developed in large chambers at the J. B. Pierce Foundation Laboratory. Both sources were subsequently evaluated in EPA's IAQ test house. The IAQ model predictions showed good agreement with the test house measurements when appropriate values were provided for source emissions, outside air exchange, in-house air movement, and deposition on "sink" surfaces.     

Development of a model to select plants with optimum metal phytoextraction potential

Purpose  

The aim of the present study is to propose a nonlinear model which provides an indicator for the maximum phytoextraction of metals to help in the decision-making process. Research into different species and strategies plays an important role in the application of phytoextraction techniques to the remediation of contaminated soil. Also, the convenience of species according to their biomass and pollutant accumulation capacities has gained important space in discussions regarding remediation strategies, whether to choose species with low accumulation capacities and high biomass or high accumulation capacities with low biomass.     

Characterization and optimization of long-term controlled release system for groundwater remediation: a generalized modeling approach

A well-based reactive barrier system using controlled-release KMnO4 has been recently developed as a long-term in situ treatment option for plumes of dense and non-aqueous phase liquids in groundwater. In order to take advantage of the merits of controlled release systems (CRS) in environmental remediation, the release behavior needs to be optimized for the hydrologic and environmental conditions of target treatment zone. Where release systems are expected to be operated over long times, like for the reactive barriers, it may only be practical to describe the long-term behavior numerically. We developed a numerical model capable of describing release characteristics associated with variable forms and structures of long-term CRS. Sensitivity analyses and illustrative simulations showed that the release kinetics and durations would be constrained by changes in agent solubility, bulk diffusion coefficients, or structures of the release devices. The generality of the numerical model was demonstrated through simulations for CRS with monolithic and double-layered matrices. The generalized model was then used for actual design and analyses of an encapsulated-matrix CRS, which can yield constant release kinetics for several years. A well-based reactive barrier system (4.05 x 10(3)m3) using the encapsulated-matrix CRS can release approximately 1.65 kg of active agent (here MnO4(-)) daily over the next 6.6 yr, creating prolonged reaction zone in the subsurface. The generalized model-based, target-specific approach using the long-term CRS could provide practical tool for improving the efficacy of advanced in situ remediation schemes such as in situ chemical oxidation, bioremediation, or in situ redox manipulation. Development of techniques for adjusting the bulk diffusion coefficients of the release matrices and facilitating the lateral spreading of the released agent is warranted.     

Evaluating Sources of Indoor Air Pollution

Evaluation of Indoor air pollution problems requires an understanding of the relationship between sources, air movement, and outdoor air exchange. Research is underway to investigate these relationships. A three-phase program is being implemented: 1) Environmental chambers are used to provide source emission factors for specific indoor pollutants; 2) An IAQ (Indoor Air Quality) model has been developed to calculate indoor pollutant concentrations based on chamber emissions data and the air exchange and air movement within the indoor environment; and 3) An IAQ test house is used to conduct experiments to evaluate the model results. Examples are provided to show how this coordinated approach can be used to evaluate specific sources of indoor air pollution. Two sources are examined: 1) para-dichlorobenzene emissions from solid moth repellant; and 2) particle emissions from unvented kerosene heaters.

The evaluation process for both sources followed the three-phase approach discussed above. Para-dichlorobenzene emission factors were determined by small chamber testing at EPA’s Air and Energy Engineering Research Laboratory. Particle emission factors for the kerosene heaters were developed In large chambers at the J. B. Pierce Foundation Laboratory. Both sources were subsequently evaluated in EPA’s IAQ test house. The IAQ model predictions showed good agreement with the test house measurements when appropriate values were provided for source emissions, outside air exchange, in-house air movement, and deposition on “sink” surfaces.