Adams Model Research Articles

Modeling of hexavalent chromium removal onto natural zeolite from air stream in a fixed bed column

The increasing use of hexavalent chromium (Cr(VI)) has exposed large populations to this environmental and occupational carcinogenic agent. Therefore, researchers have been interested in removing this substance through adsorbents. This study aimed to investigate the efficiency of natural zeolite in the direct adsorption of Cr(VI) from airflow and its adsorption modeling. In this study, a nebulizer device produced the Cr(VI) mist. The efficiency of natural zeolite in Cr(VI) adsorption from airflow, modeling of fixed column adsorption, and the effective parameters on adsorption efficiency including the initial concentration of chromium, airflow rate, and adsorption bed depth were studied. To facilitate the prediction of the performance of natural zeolite’s adsorption column, Yoon–Nelson, Thomas, BDST, and Buhart–Adams models were used. The results showed that the adsorption capacity diminished with increased airflow rate and initial concentration, while it increased with elevated height of the adsorption bed. Yoon–Nelson, Thomas, and BDST models corresponded to experimental data with a correlation coefficient of 0.9933, but the information of the Buhart–Adams model had a lower correlation coefficient (around 0.6677). In conclusion, natural zeolite can be used as an efficient low-cost adsorbent for directly Cr(VI) removing from the airflow in a fixed bed column.

Fabric defects identification for textile industry with a deep learning approach

The textile and clothing industries are crucial sectors that make significant contributions to the economic growth and advancement of countries. In addition to low labor costs, the sector’s development performance is influenced by flexible production structure and defect-free products manufacturing. However, in the textile companies, the manufacture, processing, and weaving of fabric can lead to undesirable defects, which ultimately leads to a decline in both quality and cost. This study investigates the effectiveness of different deep learning frameworks to accurately classify fabric defects commonly encountered in the textile industry in Turkey. For this purpose, an innovative data set is generated as comprising fabric defects such as lines, wrinkle marks, machine oil leaks, holes, and bleaching. Furthermore, the effectiveness of Adam and Ranger optimization functions in defect identification has been evaluated using various models in connection with explainable artificial intelligence. The findings indicate that the ResNet18 + Adam model, which is very simple and shallow, obtained a notable level of success with an accuracy of 99.30%. On the other hand, the more complex EfficientNetv2m + Adam model emerged by achieving an accuracy of 99.42%. The research results suggest that deep learning algorithms can effectively replace human manual control in the task of detecting fabric defects.

Preserving authenticity: transfer learning methods for detecting and verifying facial image manipulation

Facial retouching in supporting documents can have adverse effects, undermining the credibility and authenticity of the information presented. This paper presents a comprehensive investigation into the classification of retouched face images using a fine-tuned pre-trained VGG16 model. We explore the impact of different train-test split strategies on the performance of the model and also evaluate the effectiveness of two distinct optimizers. The proposed fine-tuned VGG16 model with “ImageNet” weight achieves a training accuracy of 99.34 % and a validation accuracy of 97.91 % over 30 epochs on the ND-IIITD retouched faces dataset. The VGG16_Adam model gives a maximum classification accuracy of 96.34 % for retouched faces and an overall accuracy of 98.08 %. The experimental results show that the 50 % - 25 % train-test split ratio outperforms other split ratios mentioned in the paper. The demonstrated work shows that using a Transfer Learning approach reduces computational complexity and training time, with a max. training duration of 39.34 min for the proposed model.

Treatment of Water Contaminated by Ship Oil: Study of Adsorption in a Fixed-Bed Column

Aquatic macrophytes like Salvinia sp. have rapid proliferation and a great capacity for ecological adaptation. In addition to these characteristics, this study points to their ability to adsorb contaminants such as dyes, metals, and oils. This work aims, through an adsorption study, to propose an alternative treatment using chemically modified Salvinia sp. (SOH) biomass to remove oil from water. Batch adsorption experiments were performed to observe the effects of concentration, pH, time, temperature, desorption, and reuse of the biomass. The adsorption mechanisms, performance, kinetics, isotherm, thermodynamics, and reusability of biomass were evaluated. Both adsorbents were well-defined by the Freundlich model isotherm. According to the results obtained, the qmax was 898.0 mg g−1 for SOH in oil-in-salt water emulsion in 15 min and 930.59 mg g−1 for Salvinia sp. in natura (SS) in the oil-in-water emulsion. In the fixed-bed column adsorption, the adsorption capacity was 2.99 g g−1 for SS and 3.49 g g−1 for SOH, and the saturation capacity was 42.89 g g−1 SS and 42.99 g g−1 SOH. According to the adsorption models, the Bohart–Adams model best fits the experimental data of this study. The SOH adsorbed oil recovery test was successful, with 100% oil recovery.

The Removal of As(III) Using a Natural Laterite Fixed-Bed Column Intercalated with Activated Carbon: Solving the Clogging Problem to Achieve Better Performance

Natural laterite fixed-bed columns intercalated with two types of layers (inert materials, such as fine sand and gravel, and adsorbent materials, such as activated carbon prepared from Balanites aegyptiaca (BA-AC)) were used for As(III) removal from an aqueous solution. Investigations were carried out to solve the problem of column clogging, which appears during the percolation of water through a natural laterite fixed-bed column. Experimental tests were conducted to evaluate the hydraulic conductivities of several fixed-bed column configurations and the effects of various parameters, such as the grain size, bed height, and initial As(III) concentration. The permeability data show that, among the different types of fixed-bed columns investigated, the one filled with repeating layers of laterite and activated carbon is more suitable for As(III) adsorption, in terms of performance and cost, than the others (i.e., non-intercalated laterite; non-intercalated activated carbon, repeating layers of laterite and fine sand; and repeating layers of laterite and gravel). A study was carried out to determine the most efficient column using breakthrough curves. The breakthrough increased from 15 to 85 h with an increase in the bed height from 20 to 40 cm and decreased from 247 to 32 h with an increase in the initial As(III) concentration from 0.5 to 2 mg/L. The Bohart–Adams model results show that increasing the bed height induced a decrease in the kAB and N0 values. The critical bed depths determined using the bed depth service time (BDST) model for As(III) removal were 15.23 and 7.98 cm for 1 and 20% breakthroughs, respectively. The results show that the new low-cost adsorptive porous system based on laterite layers with alternating BA-AC layers can be used for the treatment of arsenic-contaminated water.

Efficient Decontamination: Caffeine/Triclosan Removal using Rice Husk in Batch and Fixed-Bed Columns

Abundant, easily accessible, and low-cost agro-industrial residues represent attractive alternatives for removing emerging contaminants from water. In this work, the aqueous adsorption of caffeine/triclosan onto rice husk (RH) was studied in batch and continuous processes. For this purpose, adsorbents with three particle size ranges (120–150, 300–600, 800–2000 µm) were prepared and evaluated. The composition, structure, surface morphology, functionality, and specific surface area of the RH biosorbents were determined. This characterization revealed that RH primarily consists of lignin, cellulose, and hemicellulose, making up to 80.1% of its composition. RH also exhibited an irregular surface, with several functional groups (OH, C=O, CH, C=C, C-OH), and a relatively small specific surface area (1.18 m2/g). Batch tests were carried out using different RH sizes, doses (1–50 g/L), and contact times (5–300 min), using 20 mL of caffeine/triclosan solutions (30 mg/L). Tests were conducted to fit the most adequate kinetics and isotherm models. The optimal doses (g/L) for caffeine and triclosan removal were 4.5 and 1.5 with small RH, 8.5 and 2.5 with medium RH, and 50.0 and 10.0 with large RH, respectively. The optimal contact times for all three particle sizes were 180 and 60 min. Triclosan removal was greater than that of caffeine (2.5–25.5%) with all three particle sizes, requiring less adsorbent (2.5–5.0 times) and shorter times (3 times). The experimental data fit better the Sips isotherm and Elovich kinetics models. The small (120–150 µm) particles achieved the highest caffeine/triclosan batch adsorption capacities (6.3/28.6 mg/g). Continuous tests were performed on fixed-bed columns of 1 cm in diameter, packed with 4, 5, and 8 cm of RH, operated with hydraulic loading rates between 2 and 4 m3/m2day. Small particles also reached the highest adsorption capacity in the removal of caffeine (352.7 mg/L) and triclosan (3797.2 mg/L), and the experimental data were well-fitted to the Bohart–Adams model. The research results not only demonstrate the effective removal of contaminants but also illustrate the versatility and applicability of rice husk in various conditions and systems.

SHORT-TERM MUSIC TRAINING ENHANCES INHIBITION IN AGING

Abstract Music training was shown to benefit inhibition in adults; however, little is known about the benefits of specific types of music training and the dosages necessary to demonstrate gains in older adults. Based on the ADAM model, professional musicians use predictive temporal extrapolation to anticipate and perform coordinated bimanual movements. We applied this model to beginning adult musicians by isolating the effects of bimanual coordination when compared with non-motor standardized music interventions. We evaluated the effects of two standardized music programs (piano training, music listening instruction) on inhibition in novice adults after four-week intervals. Older adults (N=18) were recruited and randomly assigned to a manualized piano program (Keys to Staying Sharp) or music listening instruction (Music Listening Today, standard protocol). Participants completed a battery of standardized measures including measures of inhibition (Flanker, Cued Color Word Stroop) at pretesting and at each of the three follow-up intervals: 4 weeks, 8 weeks, and 12 weeks. Results of a Repeated Measures Group X Time ANOVA for the first four months of training showed reduced error rates, F(1, 14)=6.48, p=.023, and faster reaction times, F(1, 14) =5.27, p=.038 for piano training as compared to music listening instruction. Increased differences were prominent after eight-weeks of training for the piano group as compared to music listening. Data suggests that music training interventions while manualized are not created equal. Benefits from complex music performance are evident after short-term manualized music interventions. Short-term bimanual music interventions may increase cognitive performance beyond music listening instruction due to cognitive load.

A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.

Adsorptive removal of pharmaceutical contaminants: Accurate description of tailing breakthrough curves

Fixed bed adsorption is a well-established method used to remove various contaminants, including pharmaceuticals, from aqueous solutions. Mathematical modeling plays a pivotal role in analyzing the dynamic behavior of fixed bed adsorption processes. The classic Bohart–Adams model, dating back over a century, is commonly used to describe fixed bed breakthrough curves. However, it encounters difficulties in effectively interpreting tailing breakthrough curves resulting from the slow approach of the fixed bed to saturation. This study presents an enhanced version of the Bohart–Adams model, specifically tailored to address the challenge of analyzing tailing breakthrough data concerning pharmaceutical contaminant adsorption in fixed bed adsorbers. The new formulation incorporates a conceptual steric hindrance model, proposing that the tailing breakthrough behavior is a consequence of a reduction in the rate of adsorption as surface coverage increases. Through rigorous testing, the new model showcased superior performance compared to the original model, accurately describing the tailing breakthrough curves commonly observed with pharmaceutical contaminants such as diclofenac, sulfamethoxazole, and tetracycline. Additionally, comparative analyses of the new model, a mechanistic model, and the empirical Yan model conclusively demonstrated the superiority of the newly proposed approach. The new Bohart–Adams model can be used to precisely predict breakthrough and exhaustion times or optimize column operating conditions.

On the Contact Angles of a Small Sessile Drop and a Captive Bubble in View of the Size Dependence of Surface Tension

New mathematical models of a sessile drop and a captive bubble are constructed taking into account the size dependence of surface tension. If the Tolman length tends to zero the well-known Bashforth–Adams model can be considered as a special case of the constructed models. Numerical calculations of the contact angles are carried out for various numeric values of the equilibrium volume. The study shows that the size dependence of the surface tension leads to a violation of the consistency condition between the contact angles of a drop and a bubble in an external force field.

Design and Analysis of Active Speed-Limit Mechanism for Large-Scale Spatial Solar Array

The multidimensional deployment of large-scale spatial solar arrays has been the basis for high-power advanced spacecraft and a symbol of the leaps forward in aerospace technology. Activated and passive drives have often been used in combination to implement the driving mechanism of large-scale solar arrays, which can reduce the impact of the deployment and locking process. For the first time, a novel active speed-limit mechanism was introduced into the two-dimensional secondary deployable drive system of a large-scale spatial solar array, achieving a balance between large deployable driving torque and small locking impact load. A highly integrated and lightweight drive system has been designed, integrating motor drive, gear drive, and adaptive torque limiting device to achieve adaptive control of the deployment torque of the solar array. A dynamic simulation system for the entire process of a large-scale spatial solar array based on the Kane method and ADAMS model has been established. A two-dimensional secondary deployable motion control law for a large-scale solar array using an active speed-limit mechanism has been established, and the dynamic characteristic parameters of the active speed-limit driving mechanism have been determined, such as driving torque, driving mode, and driving speed. The results can be used to guide the design of the deployable driving mechanism for large-scale spatial solar arrays.

Wastewater treatment by using fixed bed column biosorption process with bio-degradable waste modified Aloe.barbadensis miller leaves powder

Present study focused on abundantwaste of Aloe. barbadense miller leaves treated with sodium bicarbonate(MABL)for the removal of Cd (II) from aqueous solution and industrial effluent using fixed bed continuous process.The experimentation data obtainedfrom breakthrough curves for different conditions (flow rate = 10––30 mL/min, Cd (II) concentration from 20 to 60 mg/L, Different bed height (Bio-adsorbent doses = 10–30 g).Uptake capacity ofCd(II) increased with increased bed depth but remained constant at any flow rate.The optimum conditions obtained were a flow rate 10 mL/min, bed height of 10 cm, and initial concentration of 20 mg/L at the maximum total adsorption capacity is 54.05 mg/g and removal efficiency is 56.3 %. Characterization of the scanning electron microscopy analysis–energy dispersive X-ray spectroscopy (SEM-EDX) analysis confirms the adsorption of Cd (II) in the bio-adsorbent surface. Column behaviors was predicted by mathematical model with desirable accuracy. Thomas model was the best Kinetics model in the biosorption process on the Cd(II) with good correlation coefficient of the experimental data in comparison of Yoon Nelson model,Bohart- Adam model (R2 > 0.90).The efficiency of bio-adsorbent regeneration achieved by 0.1 M HCl was very high, that was 50–33 % for Cd (II)during first to third reused cycle. MABL is also used to removal of Cd (II) by applying a semi-simulated real wastewater in fixed bed column So that it can be concluded that MABL is a good bio-adsorbent for treating wastewater having low concentrations of Cd (II) contamination.

Remediation of Amitriptyline Pharmaceutical Wastewater by Heteroatom-Doped Graphene Oxide: Process Optimization and Packed-Bed Studies

Amitriptyline residue released into the aquatic ecosystem can have detrimental consequences on marine organisms and human wellbeing via consumption of polluted water. With a uniquely large surface area and abundant functionalities, graphene oxide adsorption offers a remediation solution for such water pollution. This study focused on synthesizing a novel graphene-based adsorbent via ice-templating of boron-doped graphene substrate. The batch adsorption performance of the as-synthesized adsorbent was explored by central composite design (CCD), while its potential large-scale application was evaluated with a packed-bed column study. The CCD optimized conditions of 12.5 mg dosage, 32 min adsorption time, 30 °C operating temperature and 70 ppm concentration produced the highest removal efficiency of 87.72%. The results of the packed-bed study indicated that continuous adsorption of amitriptyline was best performed at a graphene bed of 3.5 cm in height, with 100 ppm of the pharmaceutical solution flowing at 2 mL/min. Furthermore, the breakthrough curve was effectively portrayed by the Log Bohart–Adams model. The as-synthesized adsorbent showed a high regeneration potential using ethanol eluent via multiple adsorption–desorption cycles. The results suggest the boron-doped graphene adsorbent in packed-bed as a highly effective system to remediate amitriptyline in an aqueous environment.

Forecasting JPFA Share Price using Long Short Term Memory Neural Network

To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. Machine Learning currently has a good level of accuracy in processing and predicting data. In this study, we modeled data using the Long-Short Term Memory (LSTM) algorithm to predict the stock price of a company called Japfa Comfeed. The main objective of this journal is to analyze the level of accuracy of Machine Learning algorithms in predicting stock price data and to analyze the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction shown in mape values and the data model obtained on variations in epochs values. All optimization models show that the higher the epoch value, the lower the loss value. Adam's Optimization Model is the model with the highest accuracy value of 98.44%.

Automatic Traction Control for Articulated Off-Road Vehicles

Construction equipment is designed to maintain good traction while operating in difficult off-road conditions. To curb wheel slip, the vehicles are equipped with differential locks. The locks may be engaged/disengaged to switch between two distinct operating modes: the closed mode is characterized by greater off-road passability, while the open mode allows better manueverability. This brief compares three on/off differential lock control algorithms. The algorithms are based on the same kinematic vehicle model, but each relies on the availability of output signals from different sensors. The validity of the kinematic model and the algorithms’ sensitivity to the values assumed by a couple of unobservable states, the wheel slip angles, is investigated by comparison to a realistic articulated hauler model in the multibody physics simulator MSC ADAMS.

Reduction of chemical oxygen demand of vinasse using sugar cane bagasse ash geopolymer

The aim of this study was to determine the use of sugar cane bagasse ash geopolymer (SCBAG) as an adsorbent for organic compounds to reduce the chemical oxygen demand (COD) of vinasse. The effects of solid loading, time and temperature on batch adsorption were investigated, while the effects of bed height and flow rate were investigated through column studies. The adsorption of organic compounds onto SCBAG could be modelled well using the Langmuir isotherm and pseudo-second-order kinetics. The maximum batch adsorption capacity was 738 mg/g at 298.15 K after 5 h of adsorption. The column studies showed that the highest COD reduction of 81% could be achieved using a flow rate of 2.5 ml/min and a bed height of 13 cm. These conditions gave a dynamic uptake of 107 458 g and a breakthrough time of 600 min. The column adsorption could be best described using the Bohart–Adams model, giving a correlation coefficient of 0.98, a Bohart–Adams rate constant of 3.05 × 10−8 l/(mg min) and a saturation concentration of 6.93 × 107 g/l. The SCBAG could be regenerated and be used as an adsorbent in three cycles without significant loss in adsorption capacity.

Water Cleaning by a Continuous Fixed-Bed Column for Cr(VI) Eco-Adsorption with Green Adsorbent-Based Biomass: An Experimental Modeling Study

In this study, chromate adsorption onto red peanut skin (RPS) was investigated in a fixed-bed column; FTIR, PZC, SEM, DLS, and BET were used to evaluate its adsorption properties. The experiments were conducted to determine the effect of physical parameters, including the inlet initial Cr(VI) concentration (100–500 mg L−1), bed height (10–20 cm), and feed flow rate (13.59–23.45 mL min−1). They were carried out to predict breakthrough curves. The adsorption capacity coefficients were determined using the most widely used Bohart–Adams model. It was tested to fit experimental data, for a better understand the dynamic behavior, and for further optimize column performance. It was found that the Cr(VI) uptake decreases when increasing the flow rate and that high chromate concentration and bed height consequently increase the column’s life span. A high column adsorption capacity can be achieved with a higher Cr(VI) concentration due to the higher driving force. The results indicated that the Bohart–Adams model provides a good description (R2 > 0.98) of the experimental data of the Cr(VI)’s removal from the aqueous solution on the RPS suggesting that the surface diffusion is the rate-limiting step in the continues adsorption process.. Breakthrough adsorption capacity is crucial for comparing RPS with other similar materials. Indeed, possible mechanisms have been suggested for illustrating adsorption onto RPS. The obtained results showed significant potential of 26.23 mg g−1 of RPS on Cr(VI) elimination at a natural pH of 5.35. Furthermore, this global investigation allowed for the design of a promising low-cost material for the future scale-up of cleaning wastewater polluted by metal and determine the properly conditions for operating column adsorption. This material provides an economical, efficient means of eliminating pollutants, thus meeting the main aims of the UN Sustainable Development Goals (UN SDGs).

Short-Term Photovoltaic Power Forecasting Based on a Novel Autoformer Model

Deep learning techniques excel at capturing and understanding the symmetry inherent in data patterns and non-linear properties of photovoltaic (PV) power, therefore they achieve excellent performance on short-term PV power forecasting. In order to produce more precise and detailed forecasting results, this research suggests a novel Autoformer model with De-Stationary Attention and Multi-Scale framework (ADAMS) for short-term PV power forecasting. In this approach, the multi-scale framework is applied to the Autoformer model to capture the inter-dependencies and specificities of each scale. Furthermore, the de-stationary attention is incorporated into an auto-correlation mechanism for more efficient non-stationary information extraction. Based on the operational data from a 1058.4 kW PV facility in Central Australia, the ADAMS model and the other six baseline models are compared with 5 min and 1 h temporal resolution PV power data predictions. The results show in terms of four performance measurements, the proposed method can handle the task of projecting short-term PV output more effectively than other methods. Taking the result of predicting the PV energy in the next 24 h based on the 1 h resolution data as an example, MSE is 0.280, MAE is 0.302, RMSE is 0.529, and adjusted R-squared is 0.824.

Methods to introduce floating bridge motion and wind excitation on a model for the investigation of heavy vehicle dynamics

The proposed floating bridge solution at Bjørnafjorden in connection with the E39 infrastructure upgrade is an enabler to realize efficient transportation. This bridge and the vehicles shuttling on it will be exposed to inclement weather conditions. The waves and wind excite the floating bridge to induce compound motion in addition to the aerodynamic crosswinds directly interacting with the vehicles. Methods to introduce the complex motion of the floating bridge (multi-post test rig) and aerodynamic crosswinds on a tractor semi-trailer have been established and presented in this paper. The environment-vehicle-driver system is enabled through a co-simulation between MATLAB/Simulink (primary) and Adams (secondary). This complex interplay is studied on the intricate 627-DoF Adams vehicle model coupled with the Adams driver model. Numerical simulations are performed for multiple constant vehicle speeds under laden condition on a road with friction of 0.7 for the 1-year storm weather condition. Vehicle stability and safety assessments such as lane violation, path following ability, rollover risk, and lateral side slip limit are evaluated to draw inferences. Subsequently, permissible vehicle speed for a laden tractor semi-trailer to operate on the floating bridge is suggested. Furthermore, a simpler 9-DoF tractor semi-trailer vehicle model developed in MATLAB/Simulink combined with the pure pursuit tracking based driver model is compared with the Adams model under identical environmental conditions for an unladen case. The simpler vehicle-driver model is validated against the detailed Adams vehicle-driver model through numerical simulations for different constant vehicle speeds.

Bromate Removal from Water Using Ion Exchange Resin: Batch and Fixed Bed Column Performance

ABSTRACT In this study, the removal of bromate, a regulated ozone by-product, was evaluated using a strong-base anion (SBA) exchange resin in batch and column experiments. The kinetics studies in batch mode showed that film diffusion-controlled bromate exchange in SBA and the isotherm studies showed that the Langmuir model fitted the experimental results with a maximum exchange capacity of 296.66 mg BrO3 −/g (~1.3 meq/mL resin). In the fixed-bed column studies, breakthrough curves were obtained under different operating conditions to examine the effects of feed flow rate, inlet bromate concentration, and bed height on column performance. A modified n-order Bohart and Adams model (n-BAM-c), which considered the asymmetry of the breakthrough curve and flow channeling, was applied for the first time to describe the experimental data obtained from the column and to predict the breakthrough curves. It was found that n-BAM-c fitted the experimental data well (R2 > 0.99) and the effects of the key operating conditions on the model parameters were determined. Overall, the results show that SBA exchange is suitable for bromate removal from water and n-BAM-c could be a powerful tool for the design and upscaling of bromate ion exchange columns.