Prediction Of Breakthrough Research Articles

Penetration-model-based criteria for the identification of the instantaneous regime for irreversible gas-liquid reactions

Generalized and improved criteria for gas–liquid reactions operating in the instantaneous regime were recently reported, correcting the shortcomings of the classic criteria, especially for low values of the infinite enhancement factor EA,∞. These criteria were derived based on the film model for mass transfer and fully exploit the geometrical symmetry of the liquid film, yielding two complementary sets of two equations each, involving a new dimensionless number: φ∞=HaE∞-1-E∞.Depending on the hydrodynamics of the system, however, the penetration model can be physically more expedient and, in this follow-up paper, a corresponding and more concise set of three criteria for the instantaneous regime was developed. Whereas the first and second criterion are identical to the classic film-model criteria, a completely different third criterion emerged due to the semi-infinite nature of the liquid elements in penetration theories. The accumulated amount of gas-phase reactant A in such an element was shown inversely proportional to the distance of the reaction plane from the gas–liquid interface, yielding a new criterion with a clear relation between the breakthrough of A and EA,∞. The criteria for operating in the instantaneous regime according to the penetration model, hence, become: HaA>2, φA,∞+EA,∞>10 and EA,∞≥10.It is generally accepted that the various mass-transfer models only quantitatively differ in the intermediate regime 0.3<Ha<2. This work demonstrates, however, that in the asymptotic instantaneous regime relative differences up to 85 % may occur with respect to breakthrough to the bulk. Both mass-transfer models result in virtually identical breakthrough predictions for EA,∞≅1 or EA,∞≥10.

SO2 removal performance and breakthrough prediction of activated carbon-based chemical filters for electronics cleanroom air purification

Electronic cleanrooms have strict control requirements for SO2 concentration due its significant influence on product yield, and application of chemical filters is the main measure of SO2 control. In this study, 8 activated carbon (AC)-based chemical filters were collected and tested for their SO2 purification performance and resistance. These filters had very similar initial efficiencies (all >95 %) but large differences in 20 % breakthrough adsorption capacity (49–294 g at 9 ppm and 0.35 m/s). The adsorption capacity is found to be positively correlated with pleat density, which is a main structural parameter of pleated chemical filter. Meanwhile, the filter resistance (9.3–34 Pa at 0.35 m/s) demonstrates a decreasing and then increasing trend with increasing pleat density. This indicated the existence of an optimal range of pleat numbers that can be used to achieve a balance between resistance and adsorption capacity. A comprehensive performance evaluation index (Z′-index) is proposed for assessing the integrated resistant and adsorption capacity performance of chemical filters (19–210 g/W for 8 samples). A lumped parameter model was proposed for predicting the breakthrough curves at low concentration based on the traditional semi-empirical breakthrough models. The prediction deviation of the breakthrough curve at real cleanroom scenario (2 ppb) is less than 15 %. In addition, a rapid filter life prediction method based on filter design parameters is developed from the lumped parameter model. This work helps the understanding of the impact of filter structure parameters on performance and facilitate the development of high-performance chemical filters for SO2 contaminant control in electronic cleanrooms.

Erratum to “Variable PFAS removal by adsorbent media with sufficient prediction of breakthrough despite reduced contact time at pilot scale”

Erratum to “Variable PFAS removal by adsorbent media with sufficient prediction of breakthrough despite reduced contact time at pilot scale”

Variable PFAS removal by adsorbent media with sufficient prediction of breakthrough despite reduced contact time at pilot scale.

One alternative adsorbent (AA) and five ion exchange (IX) resins were tested for the removal of per- and polyfluoroalkyl substances (PFAS) from groundwater in pilot-scale columns for up to 19 months using empty bed contact times (EBCTs) representative of full-scale treatment. For the six detected PFAS in the pilot feed water, the long-chain PFAS (perfluorooctanoic acid [PFOA], perfluorooctanesulfonic acid [PFOS], and perfluorohexanesulfonic acid [PFHxS]) were well removed with only PFOA, which is a perfluoroalkyl carboxylic acid (PFCA) eventually breaking through as the media became exhausted. Perfluorobutanesulfonic acid (PFBS), a short-chain perfluorosulfonic acid (PFSA), was also well removed, whereas short-chain PFCAs (perfluoropentanoic acid [PFPeA] and perfluorobutanoic acid [PFBA]) were not removed (i.e., immediate breakthrough). Overall, IX and AA demonstrated superior removal of PFSAs compared to PFCAs (i.e., later breakthrough of PFSAs translating to longer media life). Media life varied, ranging from 6 to 15 months before adsorbents reached a significant PFOA breakthrough. The performance of the two adsorbents piloted at shorter EBCT reasonably predicted the longer (representative) pilot EBCT results (within ±20-30%) for the same adsorbents following data scaling. This suggests that pilot-scale testing may be conducted at a faster pace and therefore more economically. PRACTITIONER POINTS: Long-chain PFAS (PFOA, PFOS, and PFHxS) were well removed by five ion exchange and one alternative adsorbent tested herein. One short-chain PFAS (PFBS) was well removed with no removal of two other short-chain PFAS (PFBA and PFPeA). Performance of the two adsorbents piloted at shorter EBCT reasonably predicted the longer (representative) pilot EBCT results for the same adsorbents following data scaling.

Effect of irreversible and unstable processes on reservoir recovery

Hysteresis behaviour, during filtration, is caused by lithological differences in geo-fluid-dynamical media, geological-physical characteristics, and properties of saturating fluids. Typically, irreversible changes are caused by excessive external loads on the investigated porous media. The proposed solutions are part of a new concept of unsteady water flooding, which allows for early prediction of water breakthrough into production wells. This text discusses the negative impact of hysteresis phenomena on oil recovery and proposes a solution. To solve this task, control parameters, based on growth models and catastrophe theory, are selected so to predict local instability and irreversibility before the bifurcation moment., Herewith it is necessary to develop technologies, which can prevent irreversible changes in permeability, capillary retention of oil, oil degassing, and to consider the unstable behavior of the displacement front. Systemic optimization of oil field development aims to increase oil production, effectively mobilize, save injected and recovered water and gas. Keywords: hysteresis; instability; irreversible changes in permeability and porosity; phase transition; capillary forces; oil recovery.

In silico approaches for the prediction of the breakthrough of organic contaminants in wastewater treatment plants.

The removal efficiency (RE) of organic contaminants in wastewater treatment plants (WWTPs) is a major determinant of the environmental impact of chemicals which are discharged to wastewater. In a recent study, non-target screening analysis was applied to quantify the percentage removal efficiency (RE%) of more than 300 polar contaminants, by analyzing influent and effluent samples from a Swedish WWTP with direct injection UHPLC-Orbitrap-MS/MS. Based on subsets extracted from these data, we developed quantitative structure-property relationships (QSPRs) for the prediction of WWTP breakthrough (BT) to the effluent water. QSPRs were developed by means of multiple linear regression (MLR) and were selected after checking for overfitting and chance relationships by means of bootstrap and randomization procedures. A first model provided good fitting performance, showing that the proposed approach for the development of QSPRs for the prediction of BT is reasonable. By further populating the dataset with similar chemicals using a Tanimoto index approach based on substructure count fingerprints, a second QSPR indicated that the prediction of BT is also applicable to new chemicals sufficiently similar to the training set. Finally, a class-specific QSPR for PEGs and PPGs showed BT prediction trends consistent with known degradation pathways.

Prediction of water breakthrough during waterflooding under unstable water displacement front conditions

Unsteady modifications of waterflooding technologies are usually used as the main technique of reservoir pressure maintenance and enhanced oil recovery in the development of hydrocarbon fields. The disadvantages and imperfections of the conventional approach remain even after wide application of geological and hydrodynamic mathematical modeling and its truncated modifications, including proxy models, synthetic models and other simplified versions. At this stage it was not possible to achieve a fullfledged unification of waterflooding technology, because the theory of two-phase flow filtration created by Buckley-Leverett does not allow taking into account the influence of instability of the displacement front and its negative consequences, provoking a jump change and triple water saturation. This study attempts to return to the solution of this problem and proposes an alternative concept of monitoring and optimization of unsteady waterflooding of oil deposits, taking into account the consequences of instability of the front of oil displacement with water. Keywords: water flooding; water saturation; instability of the displacement front; optimization; catastrophe theory; phase plane.

Immune response after two doses of the BNT162b2 COVID-19 vaccine and risk of SARS-CoV-2 breakthrough infection in Tyrol, Austria: an open-label, observational phase 4 trial

Correlates of protection could help to assess the extent to which a person is protected from SARS-CoV-2 infection after vaccination (so-called breakthrough infection). We aimed to clarify associations of antibody and T-cell responses after vaccination against COVID-19 with risk of a SARS-CoV-2 breakthrough infection and whether measurement of these responses enhances risk prediction. We did an open-label, phase 4 trial in two community centres in the Schwaz district of the Federal State of Tyrol, Austria, before the emergence of the omicron (B.1.1.529) variant of SARS-CoV-2. We included individuals (aged ≥16 years) a mean of 35 days (range 27-43) after they had received a second dose of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We quantified associations between immunological parameters and breakthrough infection and assessed whether information on these parameters improves risk discrimination. The study is registered with the European Union Drug Regulating Authorities Clinical Trials Database, 2021-002030-16. 2760 individuals (1682 [60·9%] female, 1078 [39·1%] male, mean age 47·4 years [SD 14·5]) were enrolled into this study between May 15 and May 21, 2021, 712 (25·8%) of whom had a previous SARS-CoV-2 infection. Over a median follow-up of 5·9 months, 68 (2·5%) participants had a breakthrough infection. In models adjusted for age, sex, and previous infection, hazard ratios for breakthrough infection for having twice the immunological parameter level at baseline were 0·72 (95% CI 0·60-0·86) for anti-spike IgG, 0·80 (0·70-0·92) for neutralising antibodies in a surrogate virus neutralisation assay, 0·84 (0·58-1·21) for T-cell response after stimulation with a CD4 peptide pool, and 0·77 (0·54-1·08) for T-cell response after stimulation with a combined CD4 and CD8 peptide pool. For neutralising antibodies measured in a nested case-control sample using a pseudotyped virus neutralisation assay, the corresponding odds ratio was 0·78 (0·62-1·00). Among participants with previous infection, the corresponding hazard ratio was 0·73 (0·61-0·88) for anti-nucleocapsid Ig. Addition of anti-spike IgG information to a model containing information on age and sex improved the C-index by 0·085 (0·027-0·143). In contrast to T-cell response, higher levels of binding and neutralising antibodies were associated with a reduced risk of breakthrough SARS-CoV-2 infection. The assessment of anti-spike IgG enhances the prediction of incident breakthrough SARS-CoV-2 infection and could therefore be a suitable correlate of protection in practice. Our phase 4 trial measured both humoral and cellular immunity and had a 6-month follow-up period; however, the longer-term protection against emerging variants of SARS-CoV-2 remains unclear. None.

Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications

This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite [Se(IV)] removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite’s ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of Se (IV), and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, XGBoost, LightGBM, and CatBoost models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The CatBoost model provided the best fit for a breakthrough prediction with a coefficient of determination R2 equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model’s accurateness. A feature importance assessment indicated that Se (IV) initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed Se (IV) sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation.

Experimental Support for a Simplified Approach to CTRW Transport Models and Exploration of Parameter Interpretation

AbstractWe empirically test our earlier theoretical arguments about simplification of continuous‐time random walk (CTRW) solute transport models, namely that without loss of generality the velocity‐like term, vψ, may be set to mean groundwater velocity, the dispersion‐like term, Dψ, defined by a classical, velocity‐independent dispersivity, and the so‐called time constant, τ, to unity. We also argue that for small‐scale heterogeneous advection (HA) and mobile‐immobile mass transfer (MIMT) CTRW transition time distributions, ψ(t), are unaffected by mean flow velocity. To experimentally test these claims, we re‐analyze two bench‐scale transport experiments—one for HA, one for MIMT—each performed at multiple flow rates in otherwise identical conditions, and show it is possible to simultaneously explain all breakthrough curves in each, subject to the above constraints. We compare our calibrations with earlier efforts for the same data sets. In the HA calibration we identify a ψ(t) of the same functional form as previous authors, and which yielded breakthrough predictions essentially identical to theirs, but with greatly differing parameters. This illustrates how values of individual CTRW parameters may not map one‐to‐one onto underlying physics. We recommend reporting complete model descriptions, discuss how the simplified approach assists in this and other theoretical considerations.

Leveraging DOM UV absorbance and fluorescence to accurately predict and monitor short-chain PFAS removal by fixed-bed carbon adsorbers

Carbon adsorbent fouling by dissolved organic matter (DOM) inhibits the ability of the widely-used rapid small-scale column test (RSSCT) to accurately predict the removal of organic micropollutants (OMP) from water by full-scale carbon adsorbers. Here, the adsorption of 11 short-chain per-/poly-fluoroalkyl substances (PFAS) from groundwater, surface water, and wastewater was examined in pilot columns as well as RSSCTs using constant diffusivity (CD) and proportional diffusivity (PD) designs. Neither the CD- or PD-RSSCT accurately predicted pilot adsorber breakthrough of PFAS using standard diffusional mass transfer models. However, PFAS breakthrough relative to optical property (e.g., peak C, UV absorbance at 254 nm) breakthrough remained constant between pilot column, CD-RSSCT, and PD-RSSCT designs. This finding permitted accurate breakthrough predictions for the sum of PFAS and for 9 of the 11 PFAS on an individual basis in pilot columns using RSSCTs. Multiple linear regressions incorporating influent and treated water optical parameters enabled the modeling approach to be applied to water sources with heterogeneous DOM characteristics. It is hypothesized that this methodology was successful because (i) optical parameters adequately quantified the competitive nature of DOM and their adsorption behaved similar to OMP and (ii) competitive adsorption by low-molecular weight DOM was the predominant fouling mechanism. An OMP monitoring approach was developed for waters containing DOM with heterogenous characteristics that also relied on raw and treated water optical properties. UVA254 and fluorescence monitoring could therefore enable water treatment to remove PFAS in a variety of scenarios that face inhibitory cost and analytical limitations, such as decentralized and low-resource settings.

Assessment of CO2 capture efficiency in packed bed versus 3D-printed monolith reactors for SEWGS using CFD modeling

Sorption Enhanced Water-Gas Shift (SEWGS) couples the water gas-shift reaction with CO2 adsorption on potassium-promoted hydrotalcite (K-HTC) sorbent material for hydrogen production and CO2 capture. Here, computational fluid dynamics (CFD) models are developed to simulate the adsorption step in SEWGS considering both packed bed and monolith reactors. The goal of this research is increasing SEWGS productivity by using 3D-printed structured bed reactors, as opposed to conventional packed bed reactors, due to the well-known advantages of monoliths, such as less mass transfer limitations and lower pressure drops. The paper presents numerical modeling and simulation work supported by experimental validation in order to compare packed bed and monolith reactors with respect to SEWGS performance. The packed bed multiscale CFD model is validated using breakthrough experimental data. A bench-scale CFD structured bed model is developed and validated based on breakthrough measurements performed by TNO using 3D-printed K-HTC monolith structures. Furthermore, geometry effects on mass transfer efficiency are investigated through CFD modeling for the bench-scale reactor. A scale-up of the monolith model to pilot-scale allows for a proper comparison with the packed bed technology. Monolith reactor model breakthrough predictions show there is a considerable increase in mass transfer rate over the packed bed reactor for the adsorption step in SEWGS, demonstrating promising potential towards enhancing the carbon capture technology.

Adsorption of Reactive Black 5 and Basic Blue 12 using biochar from gasification residues: Batch tests and fixed-bed breakthrough predictions for wastewater treatment

This work aimed to evaluate the adsorption of Reactive Black 5 (RB5) and Basic Blue 12 (BB12) dyes using biochar from gasification of wood waste. Langmuir isotherm fit better to experimental data, exhibiting maximum adsorption capacities of 35.67 mg·g−1 (RB5), and 80.41 mg·g−1 (BB12). Kinetics indicated the equilibrium was reached after 90 min for RB5 and 180 min for BB12, and Elovich model best represented the experimental points. Kinetics were also modeled following a phenomenological approach, and Linear Driving Force parameters were used to predict breakthrough curves. Simulations on continuous adsorption were performed for bench and pilot scales. Breakthrough curves estimated that 100 L of RB5, and 224 L of BB12 solution can be treated in a fixed-bed pilot plant. Although the biochar lost over 50% of its adsorption capacity after regeneration, it has high potential to remove cationic and anionic dyes, contributing to the circular economy by adding value to biomass residues.

Upscaling of transport through discrete fracture networks via random walk: A comparison of models.

Many models have been created for upscaled transport modeling in discrete fracture networks (DFNs). Random walk examples of these are the Markov directed random walk (MDRW), Monte Carlo solution of the Boltzmann transport equation (BTE), and the spatial Markov model (SMM). Each model handles the correlation between the random walk steps using different techniques and has successfully reproduced the results of full-resolution transport simulations in DFNs. However, their predictive capabilities under different modeling scenarios have not been compared. We construct a set of random 2D DFNs for three different fracture transmissivity distributions to comparatively evaluate model performance. We focus specifically on random walk models to determine what aspects of the space and time step distributions (e.g., correlation and coupling) must be accounted for to get the most accurate predictions. For DFNs with low heterogeneity in fracture transmissivity, accounting for correlation generally leads to less accurate predictions of transport behavior, but as the fracture transmissivity distribution widens, preferential pathways form and correlation between modeling steps becomes important, particularly for early breakthrough predictions.

Influence of low temperature tail water reinjection on seepage and heat transfer of carbonate reservoirs

Seepage and heat transfer in the carbonate reservoir under low-temperature tail water reinjection is a complex coupling process, which is an important basis for scientific and reasonable evaluation of geothermal resource sustainability. This study based on the tracer test of double-well reinjection for carbonate heat reservoir, a coupling model of seepage field and temperature field of fracture network is established by using the finite element software COMSOL. The uncertainty analysis is carried out to study the fluid-thermal coupling process of carbonate fracture under the condition of low-temperature tail water reinjection.The variation law of seepage field and temperature field of thermal reservoir under low-temperature geothermal tail water reinjection is revealed, The variation of measured temperature of thermal reservoir pumping side under different reinjection conditions is predicted. The results show that the dominant fracture channels between wells of the fractured heat reservoir in Xian county geothermal field play an important role in controlling the seepage heat transfer. Under the coupling action of the seepage field, pressure field and the temperature field of the heat reservoir, the low-temperature tail water reinjection forms a preferential flow along the dominant channels, which is one of the important factors to consider in the prediction of thermal breakthrough. Reinjection pressure, temperature and well spacing are the main factors for artificial control of geothermal production and reinjection system. In the pumping and reinjection system of Xian county geothermal field, under the conditions of 0.5 MPa reinjection pressure, 30 °C reinjection tail water temperature and 270 m spacing between pumping and reinjection wells, the heat reservoir temperature at the pumping side decreased by 1.5 °C in 100 years.

Prediction of breakthrough extruding force in large-scale extrusion process using artificial neural networks.

Accurate prediction of breakthrough extruding force is very important for extrusion production, especially for the large-scale extrusion process, which directly affects the production costs and safety. In this paper, based on the production data of the 360-million-newton-tonnage extruding machine, an artificial neural network (ANN) algorithm is used to establish the breakthrough extruding force prediction model for the large-scale extrusion process, and the calculation results are validated. Results show that the proposed model has high accuracy, and the average relative error between the predicted and experimental values is only 1.79%. Further, problems that are difficult to quantitative analyze such as die wear and glass powder residue in actual production, which can be regarded as "noises," are studied. Finally, the model presented is compared with the traditional finite element (FE) model. The accuracy of the ANN model is 10.2 times that of the FE model. Thus, the model established in the study fully considers the difference between actual production and theoretical analysis and provides an effective method for accurately predicting the breakthrough extruding force.

Transport of Colloidal Particles in Microscopic Porous Medium Analogues with Surface Charge Heterogeneity: Experiments and the Fundamental Role of Single-Bead Deposition.

Understanding colloid transport in subsurface environments is challenging because of complex interactions among colloids, groundwater, and porous media over several length scales. Here, we report a versatile method to assemble bead-based microfluidic porous media analogues with chemical heterogeneities of different configurations. We further study the transport of colloidal particles through a family of porous media analogues that are randomly packed with oppositely charged beads with different mixing ratios. We recorded the dynamics of colloidal particle deposition at the level of single grains. From these, the maximum surface coverage (θmax = 0.051) was measured directly. The surface-blocking function and the deposition coefficient (kpore = 3.56 s-1) were obtained. Using these pore-scale parameters, the transport of colloidal particles was modeled using a one-dimensional advection-dispersion-deposition equation under the assumption of irreversible adsorption between oppositely charged beads and colloids, showing very good agreement with experimental breakthrough curves and retention profiles at the scale of the entire porous medium analogue. This work presents a new approach to fabricate chemically heterogeneous porous media in a microfluidic device that enables the direct measurement of pore-scale colloidal deposition. Compared with the conventional curve-fitting method for deposition constant, our approach allows quantitative prediction of colloidal breakthrough and retention via coupling of direct pore-scale measurements and an advection-dispersion-deposition model.

Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning

Water coning is one of the common issues in subsurface systems in which water flows into the production well through perforated zones. This phenomenon can cause severe problems in wellbore and surface facilities. Thus, accurate prediction of water breakthrough can help to adapt to the production mode and avoid such issues. Conducting flow simulations, as a conventional approach, can be very time demanding if one deals with large subsurface systems. Furthermore, several types of data are often collected during the life of a subsurface system each of which can help to predict the breakthrough and water coning. As such, it is very important to produce similar results using the time sequence data gathered from various geo-sensing tools. In this paper, a deep long short-term memory (LSTM) model is developed to predict the water cut and water breakthrough time for multiple production wells in a water flooding case. The dataset is generated by the Egg model with a multi-input-multi-output system. We found that the proposed model can capture the general trend of variation for the water cut time sequence data for a complex subsurface system. To evaluate the performance of our data-driven method, the results are compared with vanilla recurrent neural network (RNN), deep gated recurrent unit (GRU), and artificial neural network (ANN). The conducted comparison indicates that the proposed deep LSTM model outperforms the other three approaches when the results are compared with the numerical data.

Subsurface transport potential of perfluoroalkyl acids (PFAAs): Column experiments and modeling

Transport of ten perfluoroalkyl acids (PFAAs) was studied with one-dimensional (1-D) saturated column experiments using four soil types with an organic carbon fraction (foc) range of ~0–0.045. Columns were operated under conditions relevant to aqueous film-forming foam (AFFF)-impacted fire protection training areas to determine the ability of equilibrium transport parameters to describe 1-D PFAA transport, if rate-limited sorption influences PFAA transport, and if kinetic parameters can be used to evaluate factors causing rate-limited sorption. Results of initial screening of PFAA breakthrough found that over half of the breakthrough curves deviated from equilibrium transport and merited further investigation. Subsequent analysis showed that, in many cases, these deviations could be accounted for by considering the range of applicable equilibrium Kd values (i.e. based on standard deviation) applicable to the solid phase. Thus, transport of the majority of PFAAs in 3 soils with foc of 0–0.017 was not impacted by rate-limited sorption. Further, low sorption led to transport that was essentially simultaneous for the majority of PFAAs in these porous media. Exceptions were observed for long-chain PFAAs, and also in a fourth soil with foc of 0.045, which indicated the potential for rate-limited sorption to impact transport in some scenarios. Subsequent flow interruption experiments isolating kinetic behavior confirmed rate-limited sorption caused nonequilibrium transport. Linear free energy relationships (LFERs) developed in previous work to predict the inverse relationship between mass transfer coefficients (k) and sorption parameters (i.e., Kd) were used to estimate values of k for PFAAs in this study. Resulting k values were 10−3 to 10−8 h−1, consistent with previously measured kinetic parameters for other polar and anionic compounds. Models incorporating estimated k values resulted in improved predictions of breakthrough observed in nonequilibrium scenarios (R2 0.83–0.98), but k values will require further validation prior to broader application. This work illustrates rate-limited sorption considerations are needed to describe 1-D column saturated transport for some PFAAs and solid phases. At field scales, subsurface heterogeneity and PFAA precursor transformation may be equally or even more important in determining saturated PFAA transport, but kinetic parameters in this study may help to determine relative contributions of rate-limited sorption to overall transport.

Fixed-Bed Adsorption Comparisons of Bone Char and Activated Alumina for the Removal of Fluoride from Drinking Water

Fluoride is commonly found at elevated concentrations in groundwaters worldwide and is difficult to remove even with activated alumina (AA) adsorption, the best available technology. Consequently, alternative treatment technologies for fluoride removal continue to be researched, including the use of bone char (BC) adsorption. However, BC studies are limited mostly to batch or equilibrium studies, which can be difficult to extend to full-scale applications. Therefore, the goal of this study was to compare a BC to a commercial AA for treating groundwater with a naturally occurring fluoride concentration of 8.5 mg/L using pilot- and bench-scale fixed-bed adsorption tests. At the pilot scale using an empty bed contact time (EBCT) of 10 min, fluoride breakthrough reached 1.5 mg/L within 450 bed volumes (3.1 days) and 650 bed volumes (4.5 days) for BC and AA, respectively. Two designs of the rapid small-scale column test (RSSCT) were applied to simulate the pilot columns, where the proportional diffusivity RSSCT (PD-RSSCT) design provided a more adequate prediction of fluoride breakthrough to 1.5 mg/L. The PD-RSSCT was also used to evaluate BC and AA EBCTs of 10 and 20 min, with the longer EBCT providing no significant increase in adsorbent use efficiency. Complete BC regeneration using countercurrent flow to the PD-RSSCT was limited by exposure to the sodium hydroxide regenerant solution and, potentially, the presence of arsenic.