Mass transfer correlations for nonaqueous phase liquid dissolution from regions with high initial saturations
TL;DR: In this paper, a new mass transfer correlation was developed using NAPL dissolution data from a small 2D experimental cell that contained a well-characterized heterogeneous distribution of grain sizes.
Abstract: [1] The application of existing correlations for nonaqueous phase liquid (NAPL) dissolution, which were developed in small, one-dimensional columns, to larger-scale, heterogeneous or multidimensional systems has shown the predicted dissolution behavior depends greatly on the correlation used. Variation among existing correlations is due to the system scale, NAPL-water interfacial area, and the nature of mass transfer or hydrodynamic mechanisms that are lumped in the correlation. In this paper, new mass transfer correlation is developed using NAPL dissolution data from a small 2-D experimental cell that contained a well-characterized heterogeneous distribution of grain sizes. The new correlation can be used for quantifying NAPL dissolution rates over a wide range of NAPL saturations and aqueous phase velocities within the NAPL source zone. When incorporated in a finite difference transport model, the correlation provides reasonably good predictions for systems with initially high NAPL saturations that are then reduced through the dissolution process. It is shown that NAPL dissolution is slower in this case due to the larger amorphous blobs that result from preferential flow and dissolution pathways. These large blobs have significantly less surface area in comparison with small discrete blobs that result from capillary entrapment. In comparison with other published dissolution correlations, the slower mass transfer rate is characterized with a significantly higher exponent on the NAPL saturation term.
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TL;DR: In this article, a percolation model is employed to simulate the distribution of TCE within 10 × 10×10 × 10 m source zones with spatially heterogeneous aquifer properties following a release event.
Abstract: [1] This study investigates field-scale DNAPL dissolution kinetics using high-resolution numerical simulations of DNAPL releases and dissolved phase transport. A percolation model is employed to simulate the distribution of TCE within 10 × 10 × 10 m source zones with spatially heterogeneous aquifer properties following a release event. Distributed aquifer properties and DNAPL saturations are utilized to simulate coupled groundwater flow and long-term dissolved phase transport. Grid-scale dissolution rates are computed based on published bench-scale relationships. Effective field-scale mass transfer coefficients are computed from simulated TCE fluxes at the downstream source zone boundary. Heterogeneity in groundwater velocity and DNAPL distributions leads to field-scale mass transfer coefficients that are much lower than laboratory-scale values. Field-scale mass transfer coefficients are observed to vary in direct proportion to the mean groundwater velocity, in contrast to laboratory studies that indicate proportionality with velocity to a power of ∼0.7. Computed field-scale mass transfer coefficients vary approximately in proportion to relative DNAPL mass raised to an empirical depletion exponent, which is 1 for more randomly oriented residual DNAPL regions. The former DNAPL geometries exhibit slow reductions in source concentration and contaminant flux with time as mass depletion proceeds. The latter DNAPL geometries exhibit significant and steady declines in source concentration and contaminant flux with time as depletion occurs.
190 citations
Cites background from "Mass transfer correlations for nona..."
...Several authors [Miller et al., 1990; Powers et al., 1992; Geller and Hunt, 1993; Imhoff et al., 1994; Powers et al., 1994; Nambi and Powers, 2003] have proposed empirical models relating laboratory-scale mass transfer coefficients to aqueous velocity and average nonaqueous phase liquid (NAPL)…...
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...Several authors [Miller et al., 1990; Powers et al., 1992; Geller and Hunt, 1993; Imhoff et al., 1994; Powers et al., 1994; Nambi and Powers, 2003 ] have proposed empirical models relating laboratory-scale mass transfer coefficients to aqueous velocity and average nonaqueous phase liquid (NAPL) saturation....
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TL;DR: In this article, the authors compare upscaled model predictions of flux-weighted downstream concentrations and source longevity to predictions derived from three-dimensional multiphase numerical simulation of tetrachloroethene (PCE)-NAPL dissolution for realizations of a statistically homogeneous, nonuniform aquifer.
Abstract: [1] Difficulties associated with identifying the dense nonaqueous phase liquid (DNAPL) source zone architecture at the field scale, combined with the computational costs of field-scale DNAPL dissolution simulations, have motivated the development of a number of simplified models that rely upon upscaled (i.e., domain-averaged) mass transfer coefficients to approximate field-scale dissolution processes. While conceptually attractive, these upscaled models have yet to be fully evaluated for prediction of mass recovery from a range of nonuniform, three-dimensional DNAPL source zones. This study compares upscaled model predictions of flux-weighted downstream concentrations and source longevity to predictions derived from three-dimensional multiphase numerical simulation of tetrachloroethene (PCE)-NAPL dissolution for realizations of a statistically homogeneous, nonuniform aquifer. Although the functional forms of the upscaled models are generally shown to be mathematically equivalent, upscaled model flux-weighted concentration predictions varied by over one order of magnitude, with variations attributed to the dependence of the upscaled model parameters on the specific source zone scenario used for model calibration. Replacement of upscaled model calibration parameters with source zone parameters that can be obtained from site characterization information (specifically, the initial flux-weighted concentration and source zone ganglia-to-pool (GTP) mass ratio) reduced the root-mean-square error between upscaled and numerical model predictions by approximately 80%. Application of this modified model to a range of source zone scenarios (0.4 < GTP < ∞) demonstrates the efficacy of the model for use as a screening tool to relate DNAPL mass removal and flux-weighted concentrations when mass removal is less than 80%.
126 citations
Cites background or result from "Mass transfer correlations for nona..."
...…et al., 1990; Geller and Hunt, 1993; Powers et al., 1994; Imhoff et al., 1994], dissolution from high initial saturation ganglia regions [e.g., Nambi and Powers, 2000, 2003], interphase mass exchange from DNAPL pools [e.g., Kim and Chrysikopoulos, 1999; Chrysikopoulos and Kim, 2000], and…...
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...These upscaled mass transfer correlations are similar in form to correlations that have been derived in other studies to describe local-scale dissolution under a variety of conditions, including dissolution of residual NAPL ganglia [e.g., Miller et al., 1990; Geller and Hunt, 1993; Powers et al., 1994; Imhoff et al., 1994], dissolution from high initial saturation ganglia regions [e.g., Nambi and Powers, 2000, 2003 ], interphase mass ......
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TL;DR: A large body of research published in Water Resources Research has been devoted to characterizing and understanding processes controlling the transport and fate of these organic contaminants and the effectiveness of natural attenuation, bioremediation, and other remedial technologies as mentioned in this paper.
Abstract: Toxic organic contaminants may enter the subsurface as slightly soluble and volatile nonaqueous phase liquids (NAPLs) or as dissolved solutes resulting in contaminant plumes emanating from the source zone. A large body of research published in Water Resources Research has been devoted to characterizing and understanding processes controlling the transport and fate of these organic contaminants and the effectiveness of natural attenuation, bioremediation, and other remedial technologies. These contributions include studies of NAPL flow, entrapment, and interphase mass transfer that have advanced from the analysis of simple systems with uniform properties and equilibrium contaminant phase partitioning to complex systems with pore-scale and macroscale heterogeneity and rate-limited interphase mass transfer. Understanding of the fate of dissolved organic plumes has advanced from when biodegradation was thought to require oxygen to recognition of the importance of anaerobic biodegradation, multiple redox zones, microbial enzyme kinetics, and mixing of organic contaminants and electron acceptors at plume fringes. Challenges remain in understanding the impacts of physical, chemical, biological, and hydrogeological heterogeneity, pore-scale interactions, and mixing on the fate of organic contaminants. Further effort is needed to successfully incorporate these processes into field-scale predictions of transport and fate. Regulations have greatly reduced the frequency of new point-source contamination problems; however, remediation at many legacy plumes remains challenging. A number of fields of current relevance are benefiting from research advances from point-source contaminant research. These include geologic carbon sequestration, nonpoint-source contamination, aquifer storage and recovery, the fate of contaminants from oil and gas development, and enhanced bioremediation.
117 citations
TL;DR: It is found that the proposed simple NAPL dissolution models are very good for quantifying non-equilibrium dissolution, which is characterized by tailing of breakthrough curves, and are especially useful for situations of small residual N APL saturation, which are typical for many field applications.
108 citations
TL;DR: A thermodynamically based model for predicting two-fluid interfacial area (IFA) within a porous medium as a function of wetting phase saturation (S W ) and saturation history is presented in this article.
Abstract: [1] A thermodynamically based model for predicting two-fluid interfacial area (IFA) within a porous medium as a function of wetting phase saturation (S W ) and saturation history is presented The model considers consistency with multiphase flow constitutive relationships, the conversion of total to effective specific interfacial area, energy losses, and the change of interfacial area as residual nonwetting phase dissolves The model requires as input only the capillary pressure-saturation relationships and porosity Published, high-resolution interfacial area-saturation data sets were adequately reproduced when independent measures of these parameters were employed by the model In particular, the model is found to reproduce key IFA(S W ) features including the IFA magnitude and S W value corresponding to the function's maximum, negligible IFA at residual S W and the observed hysteresis of IFA(S W ) Varying key model parameters reveals that the magnitude of the IFA(S W ) relationship is predicted to be linearly related to the porosity and entry pressure of the porous medium and is unaffected by interfacial tension Interfacial area is a parameter in the single boundary layer expression of mass transfer between two immiscible liquids in porous media The model's ability to predict local-scale IFA for a wide variety of fluid-fluid-porous media systems while accounting for saturation and saturation history thus provides an avenue for simulating the dissolution of complex source zones containing both pooled and residual dense nonaqueous phase liquids
66 citations
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Book•
25 May 1984TL;DR: An overview of diffusion and separation processes brings unsurpassed, engaging clarity to this complex topic as mentioned in this paper, which is a key part of the undergraduate chemical engineering curriculum and at the core of understanding chemical purification and reaction engineering.
Abstract: This overview of diffusion and separation processes brings unsurpassed, engaging clarity to this complex topic. Diffusion is a key part of the undergraduate chemical engineering curriculum and at the core of understanding chemical purification and reaction engineering. This spontaneous mixing process is also central to our daily lives, with importance in phenomena as diverse as the dispersal of pollutants to digestion in the small intestine. For students, Diffusion goes from the basics of mass transfer and diffusion itself, with strong support through worked examples and a range of student questions. It also takes the reader right through to the cutting edge of our understanding, and the new examples in this third edition will appeal to professional scientists and engineers. Retaining the trademark enthusiastic style, the broad coverage now extends to biology and medicine.
5,195 citations
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01 Jan 1984
TL;DR: In this article, the authors present a guide to statistical methods for the detection of contaminated fish in the Tennessee River and their application in the development of super-wars and other applications.
Abstract: CHAPTER 1: INTRODUCTION 1.1 Statistics: The Science of Data 1.2 Fundamental Elements of Statistics 1.3 Types of Data 1.4 The Role of Statistics in Critical Thinking 1.5 A Guide to Statistical Methods Presented in this Text Statistics in Action: Contamination of Fish in the Tennessee River Collecting theData CHAPTER 2: DESCRIPTIVE STATISTICS 2.1 Graphical and Numerical Methods for Describing Qualitative Data 2.2 Graphical Methods for Describing Quantitative Data 2.3 Numerical Methods for Describing Quantitative Data 2.4 Measures of Central Tendency 2.5 Measures of Variation 2.6 Measures of Relative Standing 2.7 Methods for Detecting Outliers 2.8 Distorting the Truth with Descriptive Statistics Statistics in Action: Characteristics of Contaminated Fish in the Tennessee River CHAPTER 3: PROBABILITY 3.1 The Role of Probability in Statistics 3.2 Events, Sample Spaces, and Probability 3.3 Compound Events 3.4 Complementary Events 3.5 Conditional Probability 3.6 Probability Rules for Unions and Intersections 3.7 Bayes' Rule (Optional) 3.8 Some Counting Rules 3.9 Probability and Statistics: An Example 3.10 Random Sampling Statistics in Action: Assessing Predictors of Software Defects CHAPTER 4: DISCRETE RANDOM VARIABLES 4.1 Discrete Random Variables 4.2 The Probability Distribution for a Discrete Random Variable 4.3 Expected Values for Random Variables 4.4 Some Useful Expectation Theorems 4.5 Bernoulli Trials 4.6 The Binomial Probability Distribution 4.7 The Multinomial Probability Distribution 4.8 The Negative Binomial and the Geometric Probability Distributions 4.9 The Hypergeometric Probability Distribution 4.10 The Poisson Probability Distribution 4.11 Moments and Moment Generating Functions (Optional) Statistics in Action: The Reliability of a "One-Shot" Device CHAPTER 5: CONTINUOUS RANDOM VARIABLES 5.1 Continuous Random Variables 5.2 The Density Function for a Continuous Random Variable 5.3 Expected Values for Continuous Random Variables 5.4 The Uniform Probability Distribution 5.5 The Normal Probability Distribution 5.6 Descriptive Methods for Assessing Normality 5.7 Gamma-Type Probability Distributions 5.8 The Weibull Probability Distriibution 5.9 Beta-Type Probability Distributions 5.10 Moments and Moment Generating Functions (Optional) Statistics in Action: Super Weapons Development: Optimizing the Hit Ratio CHAPTER 6: JOINT PROBABILITY DISTRIBUTIONS AND SAMPLING DISTRIBUTIONS 6.1 Bivariate Probability Distributions for Discrete Random Variables 6.2 Bivariate Probability Distributions for Continuous Random Variables 6.3 The Expected Value of Functions of Two Random Variables 6.4 Independence 6.5 The Covariance and Correlation of Two Random Variables 6.6 Probability Distributions and Expected Values of Functions of Random Variables (Optional) 6.7 Sampling Distributions 6.8 Approximating a Sampling Distribution by Monte Carlo Simulation 6.9 The Sampling Distributions of Means and Sums 6.10 Normal Approximation to the Binomial Distribution 6.11 Sampling Distributions Related to the Normal Distribution Statistics in Action: Availability of an Up/Down System CHAPTER 7: ESTIMATION USING CONFIDENCE INTERVALS 7.1 Point Estimators and their Properties 7.2 Finding Point Estimators: Classical Methods of Estimation 7.3 Finding Interval Estimators: The Pivotal Method 7.4 Estimation of Population Mean 7.5 Estimation of the Difference Between Two Population Means: Independent Samples 7.6 Estimation of the Difference Between Two Population Means: Matched Pairs 7.7 Estimation of a Poulation Proportion 7.8 Estimation of the Difference Between Two Population Proportions 7.9 Estimation of a Population Variance 7.10 Estimation of the Ratio of Two Population Variances 7.11 Choosing the Sample Size 7.12 Alternative Estimation Methods: Bootstrapping and Bayesian Methods (Optional) Statistics in Action: Bursting Strength of PET Beverage Bottles CHAPTER 8: TESTS OF HYPOTHESES 8.1 The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals 8.2 Elements and Properties of a Statistical Test 8.3 Finding Statistical Tests: Classical Methods 8.4 Choosing the Null and Alternative Hypotheses 8.5 Testing a Population Mean 8.6 The Observed Significance Level for a Test 8.7 Testing the Difference Between Two Population Means: Independent Samples 8.8 Testing the Difference Between Two Population Means: Independent Samples 8.9 Testing a Population Proportion 8.10 Testing the Difference Between Two Population Proportions 8.11 Testing a Population Variance 8.12 Testing the Ration of Two Population Variances 8.13 Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional) Statistics in Action: Comparing Methods for Dissolving Drug Tablets - Dissolution Method Equivalence Testing CHAPTER 9: CATEGORICAL DATA ANALYSIS 9.1 Categorical Data and Multinomial Probabilities 9.2 Estimating Category Probabilities in a One-Way Table 9.3 Testing Category Probabilities in a One-Way Table 9.4 Inferences About Category Probabilities in a Two-Way (Contingency) Table 9.5 Contingency Tables with Fixed Marginal Totals 9.6 Exact Tests for Independence in a Contingency Table Analysis (Optional) Statistics in Action: The Public's Perception of Engineers and Engineering CHAPTER 10: SIMPLE LINEAR REGRESSION 10.1 Regression Models 10.2 Model Assumptions 10.3 Estimating ss0 and ss1: The Method of Least Squares 10.4 Properties of the Least Squares Estimators 10.5 An Estimator of d2 10.6 Assessing the Utility of the Model: Making Inferences About the Slope ss1 10.7 The Coefficient of Correlation 10.8 The Coefficient of Determination 10.9 Using the Model for Estimation and Pediction 10.10 A Complete Example 10.11 A Summary of the Steps to Follow in Simple Linear Regression Statistics in Action: Can Dowser's Really Detect Water? CHAPTER 11: MULTIPLE REGRESSION ANALYSIS 11.1 General Form of a Multiple Regression Model 11.2 Model Assumptions 11.3 Fitting the Model: The Method of Least Squares 11.4 Computations using Matrix Algebra Estimating and Making Inferences about the ss Parameters 11.5 Assessing Overall Model Adequacy 11.6 A Confidence Interval for E(y) and a prediction interval for a Future Value of y 11.7 A First-Order Model with Quantitative Predictors 11.8 An Interaction Model with Quantitative Predictors 11.9 A Quadratic (Second-Order) Model with a Quantitative Predictor 11.10 Checking Assumptions: Residual Analysis 11.11 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 11.12 A Summary of the Steps to Follow in a Multiple Regression Analysis Statistics in Action: Bid-Rigging in the Highway Construction Industry CHAPTER 12: MODEL BUILDING 12.1 Introduction: Why Model Building is Important 12.2 The Two Types of Independent Variables: Quantitative and Qualitative 12.3 Models with a Single Quantitative Independent Variable 12.4 Models with Two Quantitative Independent Variables 12.5 Coding Quantitative Independent Variables (Optional) 12.6 Models with One Qualitative Independent Variable 12.7 Models with Both Quantitative and Qualitative Independent Variables 12.8 Tests for Comparing Nested Models 12.9 External Model Validation (Optional) 12.10 Stepwise Regression Statistics in Action: Deregulation of the Intrastate Trucking Industry CHAPTER 13: PRINCIPLES OF EXPERIMENTAL DESIGN 13.1 Introduction 13.2 Experimental Design Terminology 13.3 Controlling the Information in an Experiment 13.4 Noise-Reducing Designs 13.5 Volume-Increasing Designs 13.6 Selecting the Sample Size 13.7 The Importance of Randomization Statistics in Action: Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc CHAPTER 14: ANALYSIS OF VARIANCE FOR DESIGNED EXPERIMENTS 14.1 Introduction 14.2 The Logic Behind an Analysis of Variance 14.3 One-Factor Completely Randomized Designs 14.4 Randomized Block Designs 14.5 Two-Factor Factorial Experiments 14.6 More Complex Factorial Designs (Optional) 14.7 Nested Sampling Designs (Optional) 14.8 Multiple Comparisons of Teatment Means 14.9 Checking ANOVA Assumptions Statistics in Action: On the Trail of the Cockroach CHAPTER 15: NONPARAMETRIC STATISTICS 15.1 Introduction: Distribution-Free Tests 15.2 Testing for Location of a Single Population 15.3 Comparing Two Populations: Independent Random Samples 15.4 Comparing Two Populations: Matched-Pair Design 15.5 Comparing Three or More Populations: Completely Randomized Design 15.6 Comparing Three or More Populations: Randomized Block Design 15.7 Nonparametric Regression Statistics in Action: Agent Orange and Vietnam Vets CHAPTER 16: STATISTICAL PROCESS AND QUALITY CONTROL 16.1 Total Quality Management 16.2 Variable Control Charts 16.3 Control Chart for Means: x-Chart 16.4 Control Chart for Process Variation: R-Chart 16.5 Detecting Trends in a Control Chart: Runs Analysis 16.6 Control Chart for Percent Defective: p-Chart 16.7 Control Chart for number of Defectives per item: c-Chart 16.8 Tolerance Limits 16.9 Capability Analysis (Optional) 16.10 Acceptance Sampling for Defectives 16.11 Other Sampling Plans (Optional) 16.12 Evolutionary Operations (Optional) Statistics in Action: Testing Jet Fuel Additive for Safety CHAPTER 17: PRODUCT AND SYSTEM RELIABILITY 17.1 Introduction 17.2 Failure Time Distributions 17.3 Hazard Rates 17.4 Life Testing: Censored Sampling 17.5 Estimating the Parameters of an Exponential Failure Time Distribution 17.6 Estimating the Parameters of a Weibull Failure Time Distribution 17.7 System Reliability Statistics in Action: Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration APPENDIX A: MATRIX ALGEBRA APPENDIX B: USEFUL STATISTICAL TABLES APPENDIX C: SAS FOR WINDOWS TUTORIAL APPENDIX D: MINITAB FOR WINDOWS TUTORIAL APPENDIX E: SPSS FOR WINDOWS TUTORIAL ANSWERS TO SELECTED EXERCISES INDEX
725 citations
TL;DR: In this paper, the rate of interphase mass transfer between the nonaqueous phase liquids (NAPLs) phase and the aqueous phase is investigated in two-fluid systems.
Abstract: Many groundwater contamination incidents begin with the release of an essentially immiscible fluid into the subsurface environment. Once in the subsurface, an immiscible fluid participates in a complex pattern of transport processes. For immiscible fluids that are commonly found in contaminated groundwater environments the interphase mass transfer between the nonaqueous phase liquids (NAPLs) phase and the aqueous phase is an important process. An experimental apparatus and procedure were used to isolate and measure mass transfer between toluene and water in glass bead media systems. The rate of interphase mass transfer was investigated in two-fluid systems as a function of aqueous phase velocity, aqueous- and nonaqueous-phase fluid saturations, and porous media characteristics. The rate of interphase mass transfer is found to be directly related to aqueous phase velocity and nonaqueous phase fluid saturation level, but no significant relation to mean particle size is found. Correlation expressions for the rate of interphase mass transfer are developed using relevant dimensionless parameters and are compared to literature values. Equilibrium between the two fluid phases investigated is shown to be achieved rapidly, over wide ranges of nonaqueous phase fluid saturations and aqueous phase velocities. The derived correlations provide a means for estimating the appropriateness ofmore » the local equilibrium assumption for a nonaqueous phase liquid-aqueous phase couple in multiphase groundwater systems.« less
553 citations
"Mass transfer correlations for nona..." refers background or methods or result in this paper
...[25] The importance of NAPL saturation and aqueous phase velocities in determining mass transfer rates has been well established from previous studies in homogeneous systems with low NAPL saturations [Miller et al., 1990; Imhoff et al., 1994; Powers et al., 1994]....
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...Variables incorporated into other NAPL dissolution correlations were considered here with the exception of the Schmidt number [Miller et al., 1990] and the length of the NAPL zone [Imhoff et al., 1994] because neither of these varied in the experiments analyzed here....
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...Variables incorporated into other NAPL dissolution correlations were considered here with the exception of the Schmidt number [Miller et al., 1990] and the length of the NAPL zone [Imhoff et al....
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...[7] Correlations for mass transfer rate coefficients have been published in numerous studies for the dissolution of residual NAPL blobs [Miller et al., 1990; Geller and Hunt, 1993; Powers et al., 1992, 1994; Imhoff et al., 1994; Dillard and Blunt, 2000; Zhou et al., 2000] and NAPL pools [Kim and Chrysikopoulos, 1999; Chrysikopoulos and Kim, 2000]....
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...[7] Correlations for mass transfer rate coefficients have been published in numerous studies for the dissolution of residual NAPL blobs [Miller et al., 1990; Geller and Hunt, 1993; Powers et al., 1992, 1994; Imhoff et al., 1994; Dillard and Blunt, 2000; Zhou et al., 2000] and NAPL pools [Kim and…...
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TL;DR: In this paper, the authors focus on the experimental measurement and mathematical modeling of processes affecting the dissolution of nonaqueous phase liquids (NAPLs) entrapped in sandy porous media.
Abstract: This work focuses on the experimental measurement and mathematical modeling of processes affecting the dissolution of nonaqueous phase liquids (NAPLs) entrapped in sandy porous media. Results of a series of laboratory-scale one-dimensional column dissolution experiments indicate that the length of time required to dissolve NAPLs and substantially reduce aqueous phase effluent concentrations is many times greater than predicted by equilibrium calculations. Experimental measurements clearly show an influence of both grain size and grain size distribution on the evolution of effluent concentrations. The longer cleaning times associated with coarse or graded media are attributed to the larger and more amorphous NAPL blobs associated with these media. A general correlation for transient dissolution rates is proposed which incorporates porous medium properties, Reynolds number, and volumetric fraction of NAPL. The model is calibrated with results from styrene dissolution experiments and is shown to adequately predict trichloroethylene dissolution rates in the same sandy media over the period of time required to dissolve the NAPL.
523 citations
"Mass transfer correlations for nona..." refers background or methods in this paper
...The mass transfer rate coefficient was calculated as [Powers et al., 1992]: k̂ ¼ q L ln C* C C* ! ð2Þ where q is the Darcy velocity, and L is the length of the NAPL source zone....
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...…rate coefficients have been published in numerous studies for the dissolution of residual NAPL blobs [Miller et al., 1990; Geller and Hunt, 1993; Powers et al., 1992, 1994; Imhoff et al., 1994; Dillard and Blunt, 2000; Zhou et al., 2000] and NAPL pools [Kim and Chrysikopoulos, 1999;…...
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...This parameter directly influences the specific surface area of NAPL blobs entrapped in one or a few pore spaces [Powers et al., 1992]....
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...Other parameters that could potentially influence the interfacial area available for mass transfer were identified as porous medium grain size [Powers et al., 1992] and size of the contaminated region as a whole [Nambi and Powers, 2000]....
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