Removal of boron by a modified resin in fixed bed column: Breakthrough curve analysis using dynamic adsorption models and artificial neural network model

A robust self-regenerating graphene-based adsorbent for pharmaceutical removal in various water environments

The presence of active pharmaceutical ingredients (APIs) in wastewater effluents and natural aquatic systems threatens ecological and human health. While activated carbon-based adsorbents, such as GAC and PAC, are widely used for API removal, they exhibit certain deficiencies, including reduced performance due to the presence of natural organic macromolecules (NOMs) and high regeneration costs. There is growing demand for a robust, stable, and self-regenerative adsorbent designed for API removal in various environments. In this study, we synthesized a self-generating metal oxide nano-composite (S-MGC) containing titanium dioxide (TiO2) and silicon dioxide (SiO2) combined with 3D graphene oxide (GO) to adsorb APIs and undergo regeneration via light illumination. We determined optimal TiO2:SiO2:GO compositions for the S-MGCs through experiments using a model contaminant, methylene blue. The physical and chemical properties of S-MGCs were characterized, and their adsorption and photodegradation capabilities were studied using five model APIs, including sulfamethoxazole, carbamazepine, ketoprofen, valsartan, and diclofenac, both in single-component and multi-component mixtures. In the absence of TiO2/SiO2, 3D graphene oxide (CGB) displayed better adsorption performance compared to GAC, and S-MGCs further improve CGB's adsorption capacity. This performance remained consistent in two complex water environments: aqueous solutions at varying NOM levels and artificial urine. TiO2 supported on the GO surface exhibits similar photocatalytic activity to suspended TiO2. In a continuous fixed-bed column test, S-MGCs demonstrated robust API adsorption performance that is maintained in the presence of NOM or urine, and can be regenerated through multiple cycles of adsorption and light illumination.

Removal of V(V) from a Mixed Solution Containing Vanadium and Chromium Using a Micropocrous Resin in a Column: Migration Regularity of the Mass Transfer Zone and Analysis of Dynamic Properties

In China, both vanadium(V) and chromium(VI) are present in wastewater resulting from vanadate precipitation (AVP wastewater) and from leaching vanadium-chromium reduction slag. Addressing environmental preservation and the comprehensive utilization of metal resources necessitates the extraction and separation of V(V) and Cr(VI) from these mixed solutions. However, their separation is complicated by very similar physicochemical properties. This study establishes a method for the dynamic selective adsorption of V(V) from such mixtures. It evaluates the impact of various operating conditions in columns on dynamic adsorption behavior. This study examines the migration patterns of the mass transfer zone (MTZ) and forecasts its effective adsorption capacity through multivariate polynomial regression and a neural network (NN) model. The NN model's outcomes are notably more precise. Its analysis reveals that C 0 is the most critical factor, with Q and H following in importance. Furthermore, the dynamic properties were analyzed using two established models, Thomas and Klinkenberg, revealing that both intraparticle and liquid film diffusion influence the rates of exchange adsorption, with intraparticle diffusion being the more significant factor. Using 3 wt % sodium hydroxide as the eluent to elute V(V)-loaded resin at a flow rate of 4 mL/min resulted in a chromium concentration of less than 3 mg/L in the V(V) eluate, indicating high vanadium-chromium separation efficiency in this method. These findings offer theoretical insights and economic analysis data that are crucial for optimizing column operation processes.

Impact of sand media continuous drying and rewetting cyclic on nutrients transformation performance from reclaimed wastewater effluent at soil aquifer treatment

Reusing reclaimed wastewater became a practical resource for water utilization in groundwater recharge and irrigation activities. However, the quality of reclaimed wastewater needs improvement to meet the environmental regulations and reduce contamination risks. A laboratory-scale study simulated a soil aquifer treatment (SAT) system, exploring the synergistic effects of wet and dry cycles alongside key physicochemical parameters on pollutant removal efficiency using a glass column filled with quartz sand as the filtration medium. The investigation focused on the cyclic wetting and drying phases to unravel their impact on removing NH4+, NO3-, and PO43-. The synthetic wastewater introduced into the system exhibited varying pollutant concentrations during wet and dry periods, influenced by dynamic soil water content (WC%), pH, dissolved oxygen (DO), and oxidation-reduction potential (ORP). The high removal rates of 93% for PO43- and 43% for Total N2 demonstrate the system's capability to reduce concentrations significantly under dynamic alternating between wet and dry conditions. Results unveiled that the wet period consistently yielded higher removal rates for N2 species. Interestingly, for PO43-, the dry periods demonstrated a higher removal efficiency. Moreover, the study identified an average NO3- production during the experimental phases as a byproduct of nitrification. The average NO3- production in wet periods was 2.5 mg/L, whereas it slightly decreased to 2.2 mg/L in dry periods. These findings underscore the nuanced influence of wet and dry conditions on specific pollutants within SAT systems. Applying the logistic regression model and principal component analysis demonstrated the statistical significance of WC, pH, DO, and ORP in predicting wet/dry conditions, providing quantitative insights into their influential roles on the nutrient dynamic concentrations. This study contributes valuable data to our understanding of SAT systems, offering practical implications for designing and implementing sustainable wastewater treatment practices and pollution management across diverse environmental contexts.

Valorization of winemaking residues as biochar for removing Ni(II) from real industrial painting process effluent in a fixed-bed column

In this work, the adsorption of nickel ions from a real effluent from a metal-mechanic industry was investigated in a fixed-bed column using biochar. Biochar was prepared from winemaking residues originating from the Beifiur® composting process. The use of wine industry residues as precursor materials for biochar production is established in biomass residue valorization using the existing logistics and the lowest possible number of manipulations and pre-treatments. The results found in the work showed that the optimal conditions for nickel adsorption in fixed-bed columns were bed height (Z) of 7 cm, initial nickel concentration (C0) of 1.5 mg L-1, and flow rate (Q) of 18 mL min-1. In this condition, the maximum adsorption capacity of the column was 0.452 mg g-1, the mass transfer zone (Zm) was 3.3 cm, the treated effluent volume (Veff) was 9.72 L, and the nickel removal (R) was 92.71%. The Yoon-Nelson and BDST dynamic models were suitable to represent the breakthrough curves of nickel adsorption. Finally, the fixed-bed column adsorption using biochar from winemaking residues proved to be a promising alternative for nickel removal from real industrial effluents.

Breakthrough curve analysis of phosphorylated hazelnut shell waste in column operation for continuous harvesting of lithium from water

In batch-scale operations, biosorption employing phosphorylated hazelnut shell waste (FHS) revealed excellent lithium removal and recovery efficiency. Scaling up and implementing packed bed column systems necessitates further design and performance optimization. Lithium biosorption via FHS was investigated utilizing a continuous-flow packed-bed column operated under various flow rates and bed heights to remove Li to ultra-low levels and recover it. The Li biosorption capacity of the FHS column was unaffected by the bed height, however, when the flow rate was increased, the capacity of the FHS column decreased. The breakthrough time, exhaustion time, and uptake capacity of the column bed increased with increasing column bed height, whereas they decreased with increasing influent flow rate. At flow rates of 0.25, 0.5, and 1.0 mL/min, bed volumes (BVs, mL solution/mL biosorbent) at the breakthrough point were found to be 477, 369, and 347, respectively, with the required BVs for total saturation point of 941, 911, and 829, while the total capacity was calculated as 22.29, 20.07, and 17.69 mg Li/g sorbent. In the 1.0, 1.5, and 2.0 cm height columns filled with FHS, the breakthrough times were 282, 366, and 433 min, respectively, whereas the periods required for saturation were 781, 897, and 1033 min. The three conventional breakthrough models of the Thomas, Yoon-Nelson, and Modified Dose-Response (MDR) were used to properly estimate the whole breakthrough behavior of the FHS column and the characteristic model parameters. Li's extremely favorable separation utilizing FHS was evidenced by the steep S-shape of the breakthrough curves for both parameters flow rate and bed height. The reusability of FHS was demonstrated by operating the packed bed column in multi-cycle mode, with no appreciable loss in column performance.

ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.

Green composite aerogel based on citrus peel/chitosan/bentonite for sustainable removal Cu(II) from water matrices

Here, we propose a green and sustainable 3D porous aerogel based on citrus peel (CP), chitosan (CS), and bentonite (BT). This aerogel is prepared through a simple sol-gel and freeze-drying process and is designed for efficient capture of Cu(II) ions from water matrices. CCBA-2, with its abundance of active binding sites, exhibits an impressive Cu(II) adsorption yield of 861.58 mg/g. The adsorption isotherm and kinetics follow the Freundlich and pseudo-second-order models, respectively. In the presence of coexisting mixed-metal ions, CCBA-2 demonstrates a significantly higher selectivity coefficient (KdCu = 1138.5) for removing Cu(II) ions compared to other toxic metal ions. Furthermore, the adsorption of Cu(II) ions by CCBA-2 is not significantly affected by coexisting cations/anions, ionic strength, organic matter, or different water matrices. Dynamic fixed-bed column experiments show that the adsorption capacity of Cu(II) ions reaches 377.4 mg/g, and the Yoon-Nelson model accurately describes the adsorption process and breakthrough curve. Through experiments, FTIR, and XPS analyses, we propose a reasonable binding mechanism between CCBA-2 and metal cations, involving electrostatic attraction and chemical chelation between Cu(II) and the functional groups of the aerogel. CCBA-2 saturated with Cu(II) ions can be successfully regenerated by elution with 1 M HNO3, with only a slight decrease in adsorption efficiency (5.3%) after 5 adsorption-desorption cycles. Therefore, CCBA-2 offers a cost-effective and environmentally friendly material that can be considered as a viable alternative for the green and efficient removal of toxic Cu(II) ions from wastewater.

Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate

Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R²) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.

Performance assessment of the MOF adsorbent MIL-101 for removal of gaseous benzene and toluene: kinetic column modeling and simulation studies of fixed-bed adsorption

The adsorbent MIL-101, a metal-organic framework material, was synthesized, characterized, and tested for removal of relatively low concentrations of benzene and toluene adsorbates (200 ppm) from a gas phase in a continuous flow system. Breakthrough studies were modeled based on Thomas, Yoon-Nelson, Yan, Clark, Bohart-Adams, bed-depth service time, modified dose response, Wolborska, and Gompertz in the continuous fixed-bed operation. Through statistical analysis, it was determined which type of regression is most suitable for the studied models, linear or nonlinear. By comparing the values of error functions, it was possible to infer that the Thomas model is the best match for the experimental breakthrough curves for benzene (with maximum solid-phase concentration qT=126,750 mg/g) and the Gompertz model for toluene (parameter β=0.01 min-1). Overall, when compared to the model parameters of the linear regression, those obtained through nonlinear regression show a stronger correlation with the results found experimentally. Thus, this type of regression is more suitable for the adsorption model analysis. The liquid film and intraparticle diffusion analysis was described, and it was suggested that both types of diffusion contribute to the adsorption mechanism of benzene and toluene on MIL-101. As for the isotherms, the adsorption process was better fitted by the Freundlich isotherm. The reusability of MIL-101 after six cycles was 76.5% for benzene and 62.4% for toluene, indicating that MIL-101 was a better adsorbent for the removal of benzene in comparison with toluene.

A model for the adsorption process of water dissolved elements flowing into reactive porous media: Characterization and sizing of water mining/filtering systems

This study presents a mathematical model describing the adsorption-desorption process of water dissolved elements onto reactive porous materials during filtering operations performed under dynamic flow conditions. The developed model is based on a reversible second order adsorption kinetic featuring the progressive reduction of the purifying capacity of the filtering material due to the gradual exhaustion of the active sites available for solute retention. It enables the simulation of the performances of water filtering systems through the use of parameters having a clear chemical-physical significance or it can be used for the estimation of these parameters to characterize the adsorption properties of the reactive material. Starting from the same adsorptive conceptual model used for the filtering system marked by ongoing flowing conditions, an adaptation for static systems was performed on the mathematical framework in order to process the same chemical physical parameters in both schemes. Adsorption laboratory tests were carried out to validate the developed model. Results show that the kinetic constants and adsorption capacities (a maximum of about 45 mg g^-1 was obtained for the tested material) are highly comparable, both within the same experimental system, and between different experimental setup. This confirms the validity of the developed model which is able to perfectly fit the observed concentration data in all tested configurations.

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Traditional freshwater supplies have been over-abstracted in the current global problem of water scarcity. Through the analysis of complex experimental and real-time data, to improve the activity of water and wastewater treatment (WWT) systems, an artificial neural network (ANN), a computational model inspired by the human brain, and its variants were created. This review paper focuses on recent trends and advances in modeling and simulating different water and wastewater systems using ANN. This study uses ANN in watershed management, impurity removal from wastewater, and wastewater treatment plants. According to the literature review, ANN can predict nonlinear, linear, and complex systems with high accuracy and well control. Finally, the limitations and future scope of ANNs were discussed. ANN proved itself in the prediction of various water and WWT processes. Still, implementation has practical challenges, which include a lack of data availability, poorly built models, timely updates in developed models, and low repeatability. The use of a proper toolbox, faster computing power, and proper domain knowledge makes the practical implementation of ANN successful. As a result, ANN can build a solid foundation for attracting and motivating investigators to work in this region in the forthcoming.

In situ synthesis of magnesium-doped hydroxyapatite aerogel for highly efficient U(VI) separation with ultra high adsorption capacity and excellent recyclability

Mg-doped HAP aerogel (MHAPA) was firstly in situ prepared via freeze-drying-calcination technology to capture U(VI). The U(VI) removal capacity by MHAPA even arrived 2685.6 mg g^-1, which was about 2 times over purchased HAP, illustrating that the incorporation of Mg ions could greatly enhance the U(VI) removal capacity. Compared with HAP, MHAPA also showed better anti-ion interference ability and dynamic removal performances. In comparison with other HAP-based adsorbents, MHAPA possessed good recyclability and its desorption rate was up to 93.4% in the first cycle. The excellent U(VI) removal performances of MHAPA might be owing to its low crystallinity and grain size, fast ion exchange rate and partial ionization under acidic conditions, which would accelerate the process of electrostatic attraction, ion-exchange, and complexation to immobilize U(VI). To sum up, the prepared MHAPA was expected to be an environmentally friendly, recyclable and effective adsorbent to immobilize U(VI) in actual wastewater.

Selective Removal of Boron from Aqueous Solutions Using ECH@NGM Aerogels with Excellent Hydrophilic and Mechanical Properties: Performance and Response Surface Methodology Analysis

The remediation of environmental boron contamination has received extensive research attention. The adsorbent ECH@NGM aerogel with high hydrophilic and mechanical properties was synthesized to remove boron. The ECH@NGM aerogel had a high adsorption capacity of 81.11 mg/g, which was 14.50% higher than that of commercial boron-selective resin Amberlite IRA743. The Freundlich model and pseudo-second-order model described the adsorption behavior well. In addition, the response surface methodology (RSM) could predict the experimental outcomes and optimize the reaction conditions, and X-ray photoelectron spectroscopy (XPS) and control tests were utilized to investigate probable adsorption mechanisms. These data showed that the B ← N coordination bond was the primary adsorption force. The adsorbent had good resistance to interference from coexisting salts, high reusability, good adsorption performance even after five reuse cycles, and a high desorption rate in a relatively short time. The adsorption performance in real brines could be maintained at 80%. Therefore, this work not only provided ECH@NGM aerogels for the removal of boron from brine but also elucidated the main adsorption processes between N-containing adsorbents and boron, facilitating the design of future adsorbents for boron removal.

Enhanced Removal of Bordeaux B and Red G Dyes Used in Alpaca Wool Dying from Water Using Iron-Modified Activated Carbon

The aim of this research was to explore the removal of Red G and Bordeaux B dyes from water using a packed bed column with conventional carbon (C‐conv) and iron‐modified activated carbon (C–FeCl3). The bands increased in C–FeCl3, corresponding to groups already existing in C‐conv, such as C = C and C‐C, and the appearance of new groups, such as C‐O, C‐Cl, Fe‐Cl and Fe‐O. The total ash content (CT) was CT = (10.53 ± 0.12 and 8.98 ± 0.21)% for C‐conv and C–FeCl3, respectively. A molecular structure in the shape of a cross was noticed in Bordeaux B, which was less complex and smaller than the one in Red G. For fixed‐bed columns, the carbon fraction was (0.43 and 0.85) mm. The pH of the adsorbents was 8.55 for C‐conv and 4.14 for C–FeCl3. Breakthrough curves were obtained and the Thomas model (TM) and Yoon–Nelson model (YNM) were applied. The sorption capacity of Bordeaux B on C‐conv and C–FeCl3 was 𝑞TH: (237.88 and 216.21) mg/g, respectively, but the one of Red G was 𝑞TH: (338.46 and 329.42) mg/g. The dye removal (RT) was over 55%. 

Biosorptive Removal of Ethacridine Lactate from Aqueous Solutions by Saccharomyces pastorianus Residual Biomass/Calcium Alginate Composite Beads: Fixed-Bed Column Study

In this study, ethacridine lactate removal from aqueous solution using a biosorbent material based on residual microbial biomass and natural polymers in fixed-bed continuous column was investigated. Composite beads of Saccharomyces pastorianus residual biomass and calcium alginate were obtained by immobilization technique. The prepared biosorbent was characterized by Fourier transformed infrared spectroscopy, scanning electron microscopy, and analysis of point of zero charge value. Then, laboratory-scale experiments by fixed-bed column biosorption were conducted in continuous system. To this purpose, the column bed high (5 cm; 7.5 cm), initial pollutant concentration (20 mg/L; 40 mg/L), and solution flow through the column (0.6 mL/min; 1.5 mL/min) were considered the main parameters. Recorded breakthrough curves suggest that lower flow rates, greater bed heights, and a lower concentration of ethacridine lactate led to an increased biosorption of the target compound. The biosorption dynamic was investigated by nonlinear regression analysis using the Adams–Bohart, Yoon–Nelson, Clark, and Yan mathematical models. Conclusively, our research highlights, firstly, that the obtained biosorbent material has the required properties for retaining the ethacridine lactate from aqueous solution in continuous system. Secondly, it emphasizes that the modeling approach reveals an acceptable fitting with the experimental data for the Yoon–Nelson, Clark, and Yan models.