Modeling Of Breakthrough Curves Research Articles

Magnetite encapsulated alginates tailored material for the sustainable treatment of electroplating industrial wastewater: column dynamics and mass transfer studies

The current study proposes the use of magnetic beads for the treatment of nickel ions of the industrial wastewater system. More specifically, the removal of nickel ions is studied in single and multi-metal ion systems which enabled the scalability of nano-enabled technology to industrial systems. The current synthesis neither involves expensive precursors nor complex procedures. Indeed, the improved surface properties of the adsorbent are due to the use of Lantana camara, in the synthesis. The surface properties and functional attributes of the magnetic beads were characterized by FTIR and SEM analyses. The breakthrough experiments were done for selected column depths, varying feed flow rates and metal ion concentrations. In particular, the interventions of the interfering ions of the electroplating effluents are captured in the breakthrough analysis. Besides the lowest bed capacity reported in the multi-metal systems, the column operated with nickel ions showed a maximum bed capacity of 12.36 mg/g at a flow rate of 2 mL/min in the 20 cm bed. Furthermore, an extended breakthrough time of 780 min is obtained for 50 mg/L nickel ion solution at a flow rate of 2 mL/min. In addition, the modelling of breakthrough curves using Thomas, Yoon–Nelson and BDST models have shown reasonable fits. In addition, repeated cycles of regeneration studies showed improved efficiency of 65% in the first cycle. More specifically, the alginate validated the selective preferential adsorption of cationic substances over anionic components in the studied column.

β-FeOOH/C nanocomposite for elemental mercury removal as a new approach to environmental and natural gas processes

β-FeOOH/C nanocomposite was successfully prepared by self-assembled method of iron oxyhydroxide on carbonized sample from date stone. The phases, morphology and structure of the prepared β-FeOOH/C sample were characterized by XRD, FT-IR, BET and TEM, then compared to both parent amorphous carbon and FeOOH. The three samples are tested for their adsorptive properties toward elemental mercury removal in fixed-bed adsorption at different temperatures (25, 40, 55 and 70 °C). The data indicate high dispersion of β-FeOOH nanorods, of ≈ 5-25 nm in length and ≈ 1-2 nm in diameter, within the porous carbon structure. The maximum adsorption capacity was 1405.1 μg/g for β-FeOOH/C at a flow rate 250 mL/min, temperature 25 °C and 50 μg/Nm3 as Hg° initial concentration. The operating life of FeOOH/C is detrimined from breakthrough curve. The breakthrough time (tb) at Cout/Cin=0.05‒0.1 and equilibrium time (te) at Cout/Cin = 0.9‒1.0 were found to be 11 hours and 27 days, respectively. The adsorption mechanisms follow pseudo‒first‒order kinetics. Yoon‒ Nelson and Thomas models good described the experimental Hg° breakthrough curve model. The findings reveal the feasibility of FeOOH/C composite to be used as a potential and low-cost adsorbent for removing Hg° from natural gas, by not only reducing the environmental problems associated with gaseous mercury emissions but also by making the process more economical and simple.

Novel combinatorial extensions to breakthrough curve modeling of an adsorption column — Depth filtration hybrid process

This work introduces novel scenarios for the breakthrough curve modeling of an adsorption column/depth filtration hybrid system. Four well-known theoretical breakthrough models including Thomas, Adams–Bohart, Yoon–Nelson, and BDST were employed to describe the normalized concentration profiles. In the first approach, the theoretical models were combined for better estimation of the breakthrough curves and the Thomas/Yoon–Nelson and Thomas/BDST combinations were obtained as the best extensions. In the second approach, an adsorption-transport model was developed for the depth filter to obtain the transient concentration gradient across the filter medium thickness. The model was then combined with the theoretical breakthrough models to predict the adsorption column breakthrough curves. Results revealed that a decrease in the feed flowrate and filter pore size could intensify the concentration polarization on the filter surface. Both model extensions could successfully approximate the breakthrough curves of the hybrid system with more than 99% agreement with the experimental data, while the prediction accuracy of the original breakthrough models seldom exceeds 93%.

Fitting the Gompertz equation to asymmetric breakthrough curves

Fixed bed adsorption studies often report asymmetric breakthrough curves which exhibit a tailing phenomenon as the effluent approaches the influent concentration. Evaluations of models capable of describing such curves are lacking in the literature. This paper examines the ability of the Gompertz equation to correlate asymmetric breakthrough data collated from reports published in the environmental adsorption literature. It is shown that the Gompertz equation, which has received little attention in this field of research, is able to track asymmetric breakthrough curves displaying a moderate degree of tailing. The logistic equation, which is mathematically analogous to the Bohart-Adams, Thomas, and Yoon-Nelson models, cannot effectively describe such asymmetric data. The Gompertz equation provides only an approximate representation of breakthrough data exhibiting a pronounced degree of tailing. To fit such data, this paper presents two modified forms of the Gompertz equation, which are shown to be highly accurate (R2 > 0.996). The Gompertz equation and the two modified versions are useful additions to the toolbox of breakthrough curve modeling which has long been filled with the Bohart-Adams, Thomas, and Yoon-Nelson models. These popular logistic-based equations are confined to fitting symmetric breakthrough curves.

Studija sorpcije na koloni sa fiksnim slojem za uklanjanje nitrata katjonski modifikovanom korom nejestive bundeve

A fixed bed column was applied to remove nitrate ions from an aqueous solution using a cationic modified pumpkin shell as a sorbent. The fixed bed column performances were assessed by varying the influent nitrate concentrations (50 mg dm-3 and 100 mg dm-3) and flow rates (20 cm 3 min-1 and 40 cm 3 min-1) with 13 cm bed height of the sorbent. The obtained results showed that increase of the concentration of the initial nitrate solution affects the increase in the amount of nitrate in the effluent and reduces the breakthrough time. A higher flow rate led to the faster column exhaustion, resulting in the shortened lifespan of the column. In this study, the best nitrate removal was achieved for an initial nitrate solution of 100 mg dm-3 at the flow rate of 20 cm 3 min-1 , when a total nitrate removal of 86% is reached. The relationship between the sorption capacity of this sorbent and the varied parameters was assessed and predicted using two different theoretical breakthrough curve models: the Thomas and Yoon-Nelson models. This study confirmed that the cationic modified pumpkin shell in the fixed bed column has good potential for removing nitrate from aqueous solutions.

A study of lead removal using sargassum tenerrimum (brown algae): Biosorption in column study

The sorption capacity of sargassum tenerrimum powder is studied for various flow rates i.e., 10, 20 and 30 mL/min. The initial pH of aqueous solution is maintained at 5. For the flow rates of 10, 20 and 30 mL/min, the breakthrough times are 32, 26 and 18 min respectively while the exhaust times are 90, 85 and 75 min respectively. The time taken to achieve breakthrough has decreased with increase of flow rate. A glass column having 2.54 cm internal diameter (ID) and length of 40 cm were used. The experiments are conducted for two different bed heights (5 and 10 cm) using 2 and 4 g of biosorbent. A flow rate of 30 mL/min of aqueous solution (Co = 20 mg/L) is maintained. With an increase in bed height, the throughput volume is increased to achieve the saturation and this increase in input volume, would result in higher contact time, thereby increasing the uptake capacity. Lead biosorption uptakes are increased from 13.852 to 20.839 mg/g as the bed height is increased from 5 to 10 cm. the breakthrough curve models BDST, Thomas and Yoon-Nelson models were studied for discussing the fixed bed column onto biosprion of lead.

Phosphate recovery with granular acid-activated neutralized red mud: Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud (AaN-RM) has been successfully prepared with good chemical stability and physical strength. However, its potential for industrial application remains unknown. Therefore, the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated. The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters, such as the bed depth, flow rate, initial solution pH and initial phosphate concentration. With the optimal empty-bed contact time (EBCT) of 24.27 min, the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g, respectively. Then, the saturated fixed-bed column could be effectively regenerated with a 0.5 mol/L HCl solution. The desorption efficiency remained as high as 83.45% with a low weight loss of 3.57% in the fifth regeneration cycle. In addition, breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network (ANN) could be effectively applied for the optimization of the fixed-bed adsorption system; the coefficient of determination (R2) and the root mean square error (RMSE) evaluated on the validation-testing data were 0.9987 and 0.0183, respectively. Therefore, granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.

Fixed bed biosorption of silver and investigation of functional groups on acidified biosorbent from algae biomass.

This work investigated the use of acidified dealginated residue from Sargassum filipendula algae for biosorption of silver in fixed bed column. This biosorbent was esterified for carboxyl and sulfonate groups to investigate the influence of these groups on silver removal percentage. Potentiometric titration was also evaluated on acidified and esterified biosorbents. Porosity and specific weight were determined by helium picnometry and mercury porosimetry techniques. The results showed that carboxyl and sulfonate groups were significant on silver biosorption. Potentiometric titration showed that esterified and acidified biosorbents presented different acid groups. The porosity of the acidified biosorbent was around 40%. Fixed bed assays were conducted to evaluate the influence of inlet concentration and feed flow rate of silver. Higher percentage removal of silver was obtained at 1.0 mmol · L-1 and 0.5 mL · min-1. Modified dose-response model obtained the best prediction of experimental data among breakthrough curve models.

Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Artificial neural network (ANN) techniques are effective in modeling nonlinear processes, are simple to implement and require low computational time. In this work, the lactose adsorption process for continuous flow in a fixed-bed column with a molecularly imprinted polymer (MIP) adsorbent was modeled using an ANN technique. The neural models allowed predicting the relative lactose concentration (C/C0) from the interactions between the variables of contact time (min), temperature (?C), granulometry (mesh), bed height (cm) and flow rate (mL min-1). The ANN models were developed in MATLAB using multilayer perceptrons (MLP) and a radial basis function network (RBF). The MLP model was developed using a three-layer feed forward backpropagation network with 5, 8 and 4 neurons in the first, second and third layer, respectively. The function (RBF) network is also proposed and its performance is compared to a traditional network type. The best architecture configuration RBF model was developed using 5, 14 and 1 neurons in the first, second and third layer, respectively. The proposal of development of mathematical models applied to multi-component adsorption system for milk using these approaches is innovative. The resulting breakthrough curve models for lactose adsorption were in good agreement with the experimental results. Performance indices, such as R?, MSE, RMSE, SSE, MAE and RME were used to evaluate the reliabilities and accuracies of the models. A comparison between the ANN models shows the ability to predict the breakthrough curves of lactose removal in the milk adsorption process. Though, the MLP network model shows more accurately a higher correlation coefficient (R2 = 0.9751) and lower values for the obtained error indices. The accuracy of the model is confirmed by the comparison between the predicted and experimental data. The results showed that both neural models efficiently described the non-linear process of lactose adsorption in a fixed-bed column.

Adsorption of methyl tert-butyl ether (MTBE) onto ZSM-5 zeolite: Fixed-bed column tests, breakthrough curve modelling and regeneration

ZSM-5, as a hydrophobic zeolite, has a good adsorption capacity for methyl tert-butyl ether (MTBE) in batch adsorption studies. This study explores the applicability of ZSM-5 as a reactive material in permeable reactive barriers (PRBs) to decontaminate the MTBE-containing groundwater. A series of laboratory scale fixed-bed column tests were carried out to determine the breakthrough curves and evaluate the adsorption performance of ZSM-5 towards MTBE under different operational conditions, including bed length, flow rate, initial MTBE concentration and ZSM-5 dosage, and regeneration tests were carried out at 80, 150 and 300 °C for 24 h. Dose-Response model was found to best describe the breakthrough curves. MTBE was effectively removed by the fixed-bed column packed with a ZSM-5/sand mixture with an adsorption capacity of 31.85 mg g−1 at 6 cm bed length, 1 mL min−1 flow rate, 300 mg L−1 initial MTBE concentration and 5% ZSM-5 dosage. The maximum adsorption capacity increased with the increase of bed length and the decrease of flow rate and MTBE concentration. The estimated kinetic parameters can be used to predict the dynamic behaviour of column systems. In addition, regeneration study shows that the adsorption capacity of ZSM-5 remains satisfactory (>85%) after up to four regeneration cycles.

Artificial neural network-based surrogate modeling of multi-component dynamic adsorption of heavy metals with a biochar

This paper reports the application of four neural network surrogate models for the correlation and prediction of asymmetric breakthrough curves obtained from the multi-component adsorption of cadmium, nickel, zinc and copper ions on a biochar. Artificial neural networks namely: Feed forward back propagation neural network, Feed forward back propagation neural network with distributed time delay, Cascade forward neural network and Elman neural network have been assessed and compared where their limitations and capabilities have been discussed. The impact of the architecture of these surrogated models, including the activation functions and training algorithms, has been analyzed using error and residuals analyses in different zones of the adsorption breakthrough curves obtained from single, ternary and quaternary solutions of tested heavy metals. Overall, the bed adsorption capacities for these metals ranged from 2.01 to 5.40, 0.16 to 4.46 and 0.03 to 2.15 mmol/g in single, ternary and quaternary feeds, respectively, at tested operation conditions. Highest adsorption capacities were obtained for copper in single and multi-metallic solutions and they ranged from 2.15 to 5.4 mmol/g. Results of this paper showed that Cascade forward neural network was the best model for multi-metallic adsorption breakthrough curve modeling. This neural network showed the lowest modeling errors for the multi-component adsorption breakthrough curves. This paper introduces new results on the application of ANNs surrogate models for the simulation of multi-component adsorption process involved in water treatment and purification.

Hydroxyl aluminium silicate clay for biohydrogen purification by pressure swing adsorption: Physical properties, adsorption isotherm, multicomponent breakthrough curve modelling, and cycle simulation

Hydroxyl aluminium silicate clay (HAS-Clay) is a novel adsorbent in pressure swing adsorption for CO2 capture (CO2-PSA) and can also adsorb H2S. To investigate the performance of HAS-Clay as a CO2-PSA adsorbent, multicomponent breakthrough curves were determined using experimental measurements and theoretical models, and, based on those results, CO2-PSA simulations were conducted. The breakthrough curves produced from the theoretical models agreed well with those derived from experiment. CO2-PSA with HAS-Clay could purify biomass-gasification-derived producer gas of contaminants (carbon dioxide, methane, carbon monoxide, and hydrogen sulfide) with high CO2 recovery and low energy input. The CO2 recovery rate of CO2-PSA with HAS-Clay was 58.4%, and the CO2 purity was 98.4%. The specific energy demand was 2.83 MJ/kg-CO2. In addition, the H2S regenerability of HAS-Clay was investigated. The results show that HAS-Clay retained the ability to adsorb H2S at a steady-state value of 0.02 mol/kg for the regeneration cycles. Therefore, it is suggested that CO2-PSA with HAS-Clay is suitable for CO2 separation from multicomponent gas mixtures.

Modeling of Breakthrough Curves for Removal of Chromium from Waste Water

Modeling of Breakthrough Curves for Removal of Chromium from Waste Water

Chitosan/nanohydroxyapatite composite based scallop shells as an efficient adsorbent for mercuric ions: Static and dynamic adsorption studies

Chitosan/nanohydroxyapatite composites based on scallop shells (CP12, CP14 and CP21) were prepared with different chitosan: nanohydroxyapatite ratios (1:2, 1:4 and 2:1, respectively). Nanohydroxyapatite (P), chitosan(C) and their composites were characterized by means of TGA, XRD, N2 adsorption/desorption analysis, SEM, Zeta potential and FTIR. The BET surface area ranged between 189 and 512 m2/g. Static adsorption of Hg+2 was tested for the effect of adsorbent dosage, pH, time and initial Hg+2 concentrations indicating that maximum static adsorption capacity was confirmed by CP12 (111.6 mg/g). Static adsorption well fitted with Langmuir adsorption isotherm and Pseudo-second order kinetic models. CP12 was selected for dynamic adsorption of Hg+2 considering the effect of bed height, flow rate and the effect of Hg+2 concentrations. Maximum dynamic adsorption capacity was confirmed at bed height of 3 cm, 2.0 mL/min flow rate and 300 mg/L as Hg+2 concentration with breakthrough time (tb) and exhaustion time (te) of 9 and 21 h. Yoon-Nelson and Thomas models best described the experimental Hg+2 breakthrough curve model. After static adsorption, EDTA solution confirmed the maximum desorption efficiency. The validity of CP12 was tested through three cycles of column dynamic adsorption-desorption.

Ammonium removal from aqueous solutions by fixed-bed column using corncob-based modified biochar

ABSTRACTThis study investigated the potential of removing ammonium () from aqueous solutions using corncob based on modified biochar (MBCC) in the fixed-bed column. Corncob biochar was soaked in a mixture of HNO3 6.0 M and NaOH 0.3 M to prepare active binding sites for ammonium removal. The effect of initial ammonium concentrations (10–40 mg/L), flow rates (1–9 mL/min) and MBCC fixed-bed heights (8–24 cm) on the breakthrough characteristics of the adsorption system were studied. The results showed that the highest adsorption capacity of fix-bed column, the breakthrough time and value of Ct/Co were 12.83 mg/g, 480 min and 0.862 ± 0.025 at 10 mg/L of initial ammonium concentration, 8 cm of MBCC fixed-bed height and 3 mL/min of flow rate, respectively. The breakthrough curve model in this study also indicated that all Yoon–Nelson, Thomas and Adam–Bohart models well fit with the experimental data with a high R2. The results also proved that MBCC can be used as a potential adsorbent for eliminating in the fixed-bed column. The saturated MBCC was also regenerated and reused consecutively for four cycles. The usage of mixture of NaOH and NaCl in recovering MBCC was better than NaCl only.

ANFIS modeling of rhamnolipid breakthrough curves on activated carbon

Owning to interesting properties of biosurfactants such as biodegradability and lower toxicity, they have broad application in the food industry, healthy products, and bioremediation as well as for oil recovery. The present study was aimed to develop a GA-ANFIS model for predicting the breakthrough curves for rhamnolipid adsorption over activated carbon. To that end, a set of 296 adsorption data points were utilized to train the proposed FIS structure. Different graphical and statistical methods were also used to evaluate the model’s accuracy and reliability. Results were then compared to those of the previously reported Artificial Neural Network (ANN) and Group Method Data Handling (GMDH) models. Absolute average deviation percentage (%AAD) for the proposed model was 1.71% which demonstrates lower value compared to those of ANN and GMDH models. The present ANFIS model can be of immense value for investigating breakthrough curve of rhamnolipid and also it can help chemist who dealing with biosurfactants.

Breakthrough curve modeling of graphene oxide aerogel packed fixed bed column for the removal of Cr(VI) from water

The extensive investigation was conducted on the performance of the laboratory-scale fixed-bed column containing graphene/polylysine aerogel as an adsorbent for the removal of Cr(VI). The effect of important column design parameters on the performance of the column bed was investigated. High bed height, low influent flow rate, and high influent Cr(VI) concentration were found to be the better condition for the adsorption of Cr(VI). The effect of column bed height on the breakthrough was analyzed using bed depth service time (BDST) model and predicted the critical bed height 0.37 and 1.0cm at flow rates 1.66 and 3.32mL/min respectively for the Cr(VI). The Thomas and Yoon-Nelson model were found suitable for describing the breakthrough behavior of the column. The regeneration of exhausted column bed was studied for three cycles of adsorption-desorption. The calculation of life factor suggested that the column bed can avoid the breakthrough time at t=0 for 7.5 cycles whereas, the uptake capacity would be zero after 9.12 cycles of adsorption-desorption.

Fixed-bed adsorption of atrazine onto microwave irradiated Aegle marmelos Correa fruit shell: Statistical optimization, process design and breakthrough modeling

In this study, the feasibility of using microwave irradiated Aegle marmelos Correa fruit shell was investigated in a fixed-bed column towards sorptive removal of Atrazine from aqueous solutions. Characterizations of adsorbent such as SEM, point of zero charge (6.5), surface functional groups, BET surface area (910.31m2g−1), thermal and elemental analysis were carried out. Box-Behnken response surface methodology was utilized to optimize the process parameters such as influent flow rate (2.5–4.5mlmin−1), initial atrazine concentration (20–60mgl−1), adsorbent particle size (0.082–0.20mm), and fixed-bed height (5–10cm). The highest equilibrium adsorption capacity of atrazine at optimum conditions viz. bed height 9.46cm, initial atrazine concentration 43.29mgl−1, flow rate 4.41mlmin−1 and adsorbent particle size 0.2mm were estimated to be 12,303.3mgg−1. Single stage batch adsorber was designed using BBD quadratic model for desired purification. Fixed-bed adsorption data obtained at optimum conditions and at different bed heights were fitted into different kinetic models to predict the applicable break-through curve model. Dose-response model best suited model for mathematical description of atrazine removal in fixed bed column over other selected models.

Antagonistic binary adsorption of heavy metals using stratified bone char columns

This manuscript introduces the application of reverse stratified bone char adsorbers for the binary adsorption of copper and zinc from aqueous solution. The impact of adsorbent bed layering on the antagonistic adsorption of these heavy metals ions using bone char has been studied. Breakthrough parameters of stratified columns have been calculated and analyzed for the removal of tested heavy metals. Results showed that the stratified adsorption columns have better breakthrough parameters for the antagonistic adsorption of these heavy metals in comparison to conventional packed bed columns. The competitive adsorption effect caused by co-ion can be reduced in stratified packed columns in contrast to the conventional bed configuration enhancing the bed utilization and adsorption capacities besides a reduction in the selectivity (i.e., the adsorbent preference for the adsorption of a specific adsorbate) for heavy metal removal. The improvements in the adsorption process were more evident for Zn2+ removal, which was very sensitive to the presence of Cu2+ ions. These benefits have been obtained even operating at column conditions where the presence of axial dispersion and mass transfer resistances has a significant impact on adsorption effectiveness. On the other hand, an artificial neural network (ANNs) model was used for correlating the breakthrough curves of stratified bone char adsorbers. The modeling of the binary adsorption breakthrough curves of these metals with stratified columns was challenging even for the ANNs approach, which has been recognized as an effective intelligent algorithm for data processing. In particular, ANNs model displayed the highest modeling errors in the breakthrough zone of Ct/C0 patterns. This finding highlighted the importance of developing alternative and robust modeling approaches for simulating and predicting the performance of multicomponent adsorption systems using intensified processes such as stratified packed bed columns. In summary, results of this study contribute to the understanding and application of intensified adsorption processes for facing water pollution problems caused by heavy metals and other priority pollutants.

Fixed-Bed Adsorption of Ranitidine Hydrochloride Onto Microwave Assisted—Activated Aegle marmelos Correa Fruit Shell: Statistical Optimization and Breakthrough Modelling

In this study, the feasibility of using microwave-irradiated Aegle marmelos Correa fruit shell was investigated in a fixed-bed column towards sorptive removal of ranitidine hydrochloride (RH) from simulated aqueous solution. Characterizations of adsorbent such as SEM, point of zero charge, BET surface area, Boehm surface functional groups, thermal and elemental analysis were carried out. Box–Behnken response surface methodology was utilized to optimize the process parameters such as influent flow rate (2.5–4.5 ml min−1), initial RH concentration (100–200 mg l−1), adsorbent particle size (0.082–0.20 mm), and fixed-bed height (5–10 cm). The highest fixed-bed adsorptive removal of RH at optimum conditions viz. bed height 9.19 cm, initial RH concentration 184.94 mg l−1, flow rate 3.76 ml min−1 and adsorbent particle size 0.2 mm was estimated to be 72.86%. Fixed-bed adsorption experiments were carried out at optimum conditions obtained at different bed heights, and the data obtained were fitted into different kinetic models to predict the applicable breakthrough curve model. Dose–response model was observed to be the best suited model for mathematical description of RH removal in fixed-bed column studies over other selected models.