In this study, we investigate the removal of Bemacid red dye using brewery waste in a packed bed column. We examined the effects of bed height, inlet flow, and inlet dye concentration on the column dynamics of adsorption. To assess the favorable column dynamics, we analyzed the breakthrough curves (BTCs). We also used the Clark, Thomas, Bed Depth Service Time (BDST), and Adams-Bohart models to determine the kinetic constants of the adsorption column from the obtained results of the dynamic studies curve of the BTCs. Analysis of the BTC studies revealed that both the BTCs time and worn-out time values increased with an increase in bed height and inlet Bemacid red dye dosage but decreased with an increase in the inlet flow rate. The results further showed that Thomas’ model was the most suitable for describing the entire BTCs (R2 > 0.93). Using a full factorial design to estimate the percentage effects of cited dynamic parameters, we found that these parameters accounted for 98% of the adsorption capacity. This methodology for estimation provides crucial information on the effects of parameters and the extent to which the adsorption capacity depends on the studied parameters.

  • Brewery wastes are an efficient adsorbent for the removal of Bemacid red dye.

  • The increase in the bed height of the column enhances the efficiency of the adsorption column.

  • An increase in the initial dye concentration and flow rate weakens the efficiency of the adsorption column.

  • Operational factors have an 89% dependency rate on the adsorption capacity.

  • The experimental data are in agreement with the estimated data obtained through the full factorial design.

Textile industries have used increasingly large quantities of industrial dyes, leading to a corresponding increase in the quantity of unloaded wastewater. The presence of these dyes in wastewater gives it an unattractive color and has negative effects on aquatic ecosystems, including the reduction of the permeation of light and photosynthesis (Zümriye 2005; Yagub et al. 2014).

To address the issue of water pollution, various treatment processes have been utilized to remove dyes from wastewater. These methods include oxidation, coagulation and flocculation, biological treatment, membrane filtration, and others. Of these, the adsorption process has emerged as the simplest and most effective alternative treatment for removing dyes from wastewater (Wong et al. 2004; Crini & Badot 2008; Almeida & Corso 2014). Activated charcoal is the most commonly used adsorbent in the adsorption process, but its relatively high cost often limits its usage (Gupta & Suhas 2009; Mobasherpour et al. 2014). As an alternative, it is recommended to utilize low-cost adsorbents, such as industrial waste materials like those from the steel and fertilizer industries (Jain et al. 2003), brewery waste (Ouazani et al. 2017), natural materials (Bulgariu et al. 2019), or agricultural by-products (Bharathi & Ramesh 2013). The adsorption technique can be performed using either batch or continuous modes (Bharathi & Ramesh 2013). The batch mode is typically used in laboratory-scale experiments but is not suitable for industrial applications due to the continuous flow of pollutants and the large volumes involved. In contrast, the continuous mode (or dynamic mode) is suitable for large-scale applications in wastewater treatment due to its flexibility and ease of operation (García-Mateos et al. 2015).

The breakthrough curve (BTC) obtained from the dynamic study provides significant insights into the dynamic behavior of the adsorption column and is crucial for designing, optimizing, and operating the separation process. Several dynamic models, such as the Bed Depth Service Time (BDST) model, Clark model, Yoon-Landon model, Adams-Bohart model, and Thomas model, are utilized to evaluate and predict the efficiency of the column (Kyzas et al. 2012; Sicupira et al. 2015; Yang et al. 2015).

In this study, the feasibility of using brewery waste as a low-cost adsorbent for the removal of ETL dye in a packed bed column adsorption process was investigated. The effects of various dynamic factors such as bed height, inlet ETL dye concentration, and flow rate were evaluated in a continuous column adsorption system. The kinetic constants of the BTCs were determined and optimized using the different dynamic models. The percentage of dependence between dynamic factors and adsorption capacity is estimated by using the full factorial design.

Adsorbent and adsorbate

In our previous study (Ouazani et al. 2017), we described of brewery waste. Based on the results of Fourier Transform Infra Red (FTIR) analysis, it was found that the waste contains lignin and cellulose, classifying it as a lignocellulosic adsorbent. The ETL dye, which is used in this study, is an industrial dye commonly used in the textile industry. The dye was obtained from the SOITEX plant in Tlemcen, which is located in western Algeria. The structure of the dye is unknown, and the solution used in this study was prepared by dissolving 1 g of the ETL dye in 1 L of distilled water.

The spectrophotometer analysis UV-Visible (HACH DR 2000) is utilized to measure the residual concentration of samples at 500 nm.

Fixed-bed column study

The study on fixed-bed column adsorption involved packing a specific amount of brewery waste into a glass column with an internal diameter of 1.1 cm and a length of 20 cm. The column was then subjected to a controlled flow rate of ETL dye solution at a predetermined concentration. At predetermined time intervals, samples were collected and analyzed to determine the remaining concentration of ETL dye. The impact of various operational parameters, including bed height (2.5–5 cm), feed flow rate (1.5–5.1 mL/min), and inlet ETL dye concentration (25–100 mg/L), was investigated. The effectiveness of the packed bed was determined by analyzing the BTC, which plots the concentration of adsorbed dye against time. The efficiency of the packed bed can also be represented by the normalized concentration, which is the ratio of the residual dye concentration to the inlet dye concentration (C/C0), plotted against either flow time (t) or effluent volume (Veff = Q*ttotal). From this formula, the ttotal and Q are the total process time (min) and volumetric flow rate (mL/min), respectively (Kannan et al. 2012; Pei-Jen et al. 2014).

To assess the experimental results and make the packed bed suitable for industrial applications, various models such as Adams-Bohart, Thomas, Clark, and BDST were employed to analyze the performance of the packed bed.

Thomas model

The Thomas model is used to describe the jamming behavior of flow in the fixed bed, employing the Langmuir isotherm and the parameters of the second-order kinetic model. This model is particularly useful for adsorption processes where the effects of external and internal diffusion are negligible (Hutchins 1973), and the equation of this model is (Ming-Shen & Hsing-Ya 2002; Muhamad et al. 2010; Gassan et al. 2014) as follows:
formula
(1)

Equation (1) can be used to determine the rate constant (KT) and the adsorption capacity (q0) of the packaged bed. M is the weight of the adsorbent (g), Q is the flow (L/min), and C is the residual concentration of the Adsorbat in time t. The KT and q0 coefficients can be calculated by plotting Ln (C0/C − 1) against time (t) (Hameed 2009).

The Adams-Bohart model

The Adams-Bohart model assumes that the amount of adsorption is dependent on both the adsorbent and the residual effluent dosage and is expressed as follows (Nkem et al. 2011):
formula
(2)
where KAB is the Adams-Bohart constant rate (L/mg·min), V is the cumulative liquid throughput volume (cm/min) and N0 the adsorption amount per volume unit (mg/L).

Clark model

The model utilized the Freundlich isotherm in conjunction with the concept of mass diffusion (Ghosh et al., 2015). The equation for the model (Equation (3)) includes the Clark constants A and r, as well as the Freundlich parameter n.
formula
(3)

The BDST model

The BDST model is commonly used to establish a relationship between the bed height (Z), service time (t), and adsorption parameters (Sekhula et al. 2012). The equation for the BDST model is given by the following equation:
formula
(4)
Hutchins (1973) proposed an equation to establish a linear relationship between bed height and service time, as shown in Equation (5):
formula
(5)

The quantity of dye captured by the fixed bed is denoted by the area under the BTC (C versus t) and can be determined through numerical integration (Sicupira et al. 2015). The ratio of the dye mass (mad) and the adsorbent mass (M) can be used to determine the capacity (q) of the packed bed.

The performance of the continuous fixed-bed column process was depicted by plotting the BTC, which shows the relationship between the residual dye concentration and the dye inlet concentration (Cres/C0) as a function of operating time (t).

Influence of bed depth on column efficiency

Figure 1 shows the BTCs of ETL dye adsorption obtained under the following operational conditions: bed depths of 25, 35, and 50 mm, a flow rate of 1.5 mL/min, and an inlet dye dosage of 25 mg/L. It can be observed that increasing the bed depth resulted in a longer contact time for adsorption within the column. The increase in bed depth resulted in a larger service zone available for adsorption and an increase in the number of active adsorbent sites (Zümriye & Gönen 2004). Consequently, the breakthrough time and the exhaustion time increased from 6 to 84 and 17 to 542 min, respectively, when the bed depth was increased from 25 to 50 mm. The most effective bed depth in the adsorption column was 50 mm, corresponding to 1.42 g of brewery waste, as it resulted in the slowest breakthrough among the tested bed depths (Figure 1). As the bed depth decreased, the breakthrough of the dye occurred more rapidly and the packed bed column was depleted more quickly, leading to decreased efficiency. The bed depth had a significant impact on both the breakthrough time and bed efficiency of the column. Similar observations were made for other flow rates and concentrations. The findings of this study were consistent with those of previous studies on the adsorption of methylene blue and diazo dye Brilliant Black BN by chitosan beads, as reported by Chowdhury et al. (2016) and Shadeera & Nagapadma (2015), respectively.
Figure 1

Influence of bed depth on ETL dye column efficiency.

Figure 1

Influence of bed depth on ETL dye column efficiency.

Close modal

Influence of the flow rate on ETL dye column efficiency

Various factors affect the adsorption capacity of a fixed-bed column, including the inlet dye flow rate. In this research, the flow rates ranged from 1.5 to 5. 1 mL/min, while the bed depth and dye concentration remained constant. Figure 2 shows that the BTCs for the dye adsorption process decreased as the inlet dye flow rate increased from 1.5 to 5.1 mL/min. This indicates a shorter operational column time. This suggests that a higher dye flow rate leads to a shorter operating time for the column. This can be explained by the fact that at a fast inlet flow rate, the dye molecules do not have enough time to reach adsorption equilibrium and exit the column before equilibrium is achieved. However, at a lower inlet flow rate, the residence time of the dye molecules in the column is increased, allowing for sufficient contact time with the adsorbent. Thus, a lower flow rate results in a higher removal of dye molecules in the column. These findings are consistent with those reported by Al-Baidhany & Al-Salihy (2016).
Figure 2

Influence of flow rate on ETL dye column efficiency.

Figure 2

Influence of flow rate on ETL dye column efficiency.

Close modal

Influence of initial dye concentration on ETL dye column efficiency

Figure 3 shows the BTCs obtained for ETL dye adsorption at concentrations of 25 and 100 mg/L, with the fixed inlet flow rate (1.5 mL/min) and bed depth (5 cm). At the lower dye concentration, a longer time was needed for the active sites of the brewery waste to be saturated due to the lower amount of adsorbate and slower transport of dye molecules (Nishil et al. 2015). This finding is consistent with previous studies (Nouri & Ouederni 2013; Nihla et al. 2015). However, the adsorption capacity increased from 6.44 to 73 mg/g when the inlet ETL dye dosage was raised from 25 to 100 mg/L.
Figure 3

Influence of initial dye concentration on ETL dye column efficiency.

Figure 3

Influence of initial dye concentration on ETL dye column efficiency.

Close modal

Assessment of breakthrough curves and determination of kinetic parameters

In our study, we used the column service time when the normalized concentration (C/C0) reaches 0.05 based on the data of previous research (Mobasherpour et al. 2014; Nishil et al. 2015). The relationship between the bed altitude (Z) and time (t) at a flow rate of 1.5 mL/min is plotted in Figure 4, which represents a linear relationship described by Equation (4) with a determination coefficient (R2) above 0.98. This indicates that the BDST model is suitable for the studied column system. Using the BDST model, the estimated values for the adsorption capacity (N0) and rate constant (KAB) are 14.1 mg/L and 3.64 cm3/mg·min, respectively. The advantage of the BDST model is that it allows for the reliable scaling of experimental data to other inlet flow values without further experimentation (Nouri & Ouederni 2013).
Figure 4

BDST model of ETL dye-packed bed column breakthrough.

Figure 4

BDST model of ETL dye-packed bed column breakthrough.

Close modal

The bed data were fitted with three different dynamic models previously cited to determine the rate constant (KAB) and maximum adsorption capacity (q). The results for the two kinetic parameters (KAB) and (q) and their determination coefficients are presented in Table 1. The obtained results indicate that the Clark model provided a good fit to the experimental data, with determination coefficients ranging from 0.94 to 0.97 for all operational conditions. Furthermore, Table 1 reveals that the value of the kinetic constant was influenced by the flow rate, inlet dye concentration, and bed depth. It was observed that the bed capacity (q0) was inversely proportional to the flow rate but proportional to the inlet dye concentration.

Table 1

Kinetic parameters of ETL dye packed bed column

ETL dye concentration25 (mg/l)
100 (mg/l)
Bed depth2.5 (cm)
3.5 (cm)
5 (cm)
2.5 (cm)
3.5 (cm)
5 (cm)
Flow rate (mL/min)1.52.65.11.52.65.11.52.65.11.52.65.11.52.65.11.52.65.1
Thomas model 
KT (L/mg·min) ×10322.4 10 15.6 4.4 7.6 4.4 20 11.2 1.4 4.9 3.5 1.7 2.9 1.4 2.5 2.1 
q0 (mg/g) 2.86 4.19 7.42 5.83 5.87 5.83 10.47 10.4 8.77 3.69 13.2 7.2 13.2 18.3 18.7 12.2 16.6 31.7 
R2 0.94 0.95 0.97 0.93 0.97 0.93 0.94 0.92 0.92 0.96 0.4 0.5 0.97 0.96 0.92 0.95 0.98 0.98 
Adams-Bohart model 
KAB (L/mg·min × 1031.2 1.2 1.2 0.4 0.4 0.2 0.7 0.7 0.4 0.5 0.4 0.1 0.2 0.9 
N0 (mg/L) 1.1 0.17 0.55 0.4 0.18 0.24 0.45 0.49 0.85 0.66 0.44 1.11 0.53 0.49 1.36 0.94 0.62 1.16 
R2 0.94 0.88 0.93 0.95 0.93 0.92 0.98 0.93 0.94 0.94 0.88 0.98 0.99 0.96 0.92 0.92 0.96 0.99 
Clark model 
A 36.4 5.72 5.55 4.63 4.68 3.95 4.36 3.79 1.49 24.04 6.95 2.25 5.59 6.76 1.93 7.98 6.76 5.57 
R 0.54 0.3 0.39 0.11 0.19 0.21 0.5 0.6 0.6 0.73 0.49 0.47 0.2 0.29 0.26 0.17 0.29 0.2 
R2 0.93 0.93 0.97 0.93 0.97 0.89 0.93 0.92 0.9 0.91 0.97 0.95 0.92 0.96 0.93 0.93 0.96 0.97 
ETL dye concentration25 (mg/l)
100 (mg/l)
Bed depth2.5 (cm)
3.5 (cm)
5 (cm)
2.5 (cm)
3.5 (cm)
5 (cm)
Flow rate (mL/min)1.52.65.11.52.65.11.52.65.11.52.65.11.52.65.11.52.65.1
Thomas model 
KT (L/mg·min) ×10322.4 10 15.6 4.4 7.6 4.4 20 11.2 1.4 4.9 3.5 1.7 2.9 1.4 2.5 2.1 
q0 (mg/g) 2.86 4.19 7.42 5.83 5.87 5.83 10.47 10.4 8.77 3.69 13.2 7.2 13.2 18.3 18.7 12.2 16.6 31.7 
R2 0.94 0.95 0.97 0.93 0.97 0.93 0.94 0.92 0.92 0.96 0.4 0.5 0.97 0.96 0.92 0.95 0.98 0.98 
Adams-Bohart model 
KAB (L/mg·min × 1031.2 1.2 1.2 0.4 0.4 0.2 0.7 0.7 0.4 0.5 0.4 0.1 0.2 0.9 
N0 (mg/L) 1.1 0.17 0.55 0.4 0.18 0.24 0.45 0.49 0.85 0.66 0.44 1.11 0.53 0.49 1.36 0.94 0.62 1.16 
R2 0.94 0.88 0.93 0.95 0.93 0.92 0.98 0.93 0.94 0.94 0.88 0.98 0.99 0.96 0.92 0.92 0.96 0.99 
Clark model 
A 36.4 5.72 5.55 4.63 4.68 3.95 4.36 3.79 1.49 24.04 6.95 2.25 5.59 6.76 1.93 7.98 6.76 5.57 
R 0.54 0.3 0.39 0.11 0.19 0.21 0.5 0.6 0.6 0.73 0.49 0.47 0.2 0.29 0.26 0.17 0.29 0.2 
R2 0.93 0.93 0.97 0.93 0.97 0.89 0.93 0.92 0.9 0.91 0.97 0.95 0.92 0.96 0.93 0.93 0.96 0.97 

According to the results presented in Table 1, the values of the kinetic constants obtained using the Adams-Bohart model remained constant for the operational conditions where the dye concentration was 25 mg/L and the flow rate was 1.5 mL/min, regardless of the bed altitude. However, for other conditions, a slight variation in the KAB constant was observed. These findings suggest that the bed depth did not have a significant impact on the values of the kinetic constant.

The estimated values of the kinetic constant KAB in this study were found to be lower than the values reported by Hamdaoui (2006) for the removal of methylene blue using sawdust of cedar, which was 34 × 10−3 L/mg·min at an altitude of 8 cm and a flow rate of 23 mL/h. On the contrary, the volume capacity obtained in this study is higher than the value reported by Bennani et al. (2015) when they used Moroccan clay for the removal of basic red dye 46, which was 160 mg/L under the operational conditions of a dye concentration of 160 mg/L, a bed depth of 15 cm, and a flow rate of 4 mL/min.

The results presented in Table 1 indicate that the Thomas rate constant KT values vary significantly under different operational conditions. For instance, when the inlet dye concentration is 25 mg/L and the flow rate is 1.5 mL/min, the KT value ranges from 2 × 10−3 to 22.4 × 10−3 L/mg·min, and a similar trend is observed for other flow rates.

The estimated values of the Thomas rate constant KT in this study are higher than those reported in previous research. For instance, Sivakumar & Palanisamy (2009) found a rate constant of 1.4 mL/mg·min for the removal of acid blue dye 92 using activated charcoal at an initial concentration of 25 mg/L and a bed height of 5 cm. Similarly, Shadeera & Nagapadma (2015) reported a rate constant of 0.35 mL/mg·min for the removal of Brilliant diazo dye at a concentration of 20 mg/L, a bed height of 6 cm, and a flow rate of 0.8 m/min. However, the values obtained in this study are within the same range as those reported by Manase (2012) for the removal of Rhodamine B using activated charcoal.

Table 1 shows that the Clark constant r ranges from 0.11 to 0.73 min−1. These values are higher than those obtained for the removal of methylene blue dye (ranging from 0.02 to 0.07 min−1) as reported by Hamdaoui (2006). Notably, the highest value of the Clark constant r (0.73 min−1) was observed for a bed altitude of h = 2.5 cm, a flow rate of 1.5 mL·min−1, and an initial dye concentration of 100 mg/L. Furthermore, the variation in the Clark constant r was insignificant when the altitude was increased from 2.5 to 5 cm, suggesting that altitude has no influence on the constant for a consistent flow rate.

Full factorial design for optimizing the effects of operational parameters

In our adsorption column study, it was found that the adsorption process of ETL dye on brewery waste was affected by inlet ETL dye dosage, bed altitude, and flow rate. A statistical study was conducted to optimize these factors and model the adsorption capacity in the experimental area.

The Design Expert 13.0 software (evaluation version) was employed to estimate the coefficients of the proposed model, utilizing the matrix calculations provided by the software. The comparison between the estimated values obtained through the full factorial design model and the experimental results is illustrated in Figure 5.
Figure 5

Calculation of mean adsorption capacity through actual and residual values.

Figure 5

Calculation of mean adsorption capacity through actual and residual values.

Close modal

Table 2 shows that the R2 value is 0.97, indicating that 97% of the adsorption capacity value is attributed to the three studied parameters (dye concentration, inlet flow, and bed depth). This suggests that the statistical model is effective in estimating the adsorption capacity value compared to the experimental measurement. The difference between the adjusted R2 and R2 is less than 0.0245, implying that the statistical model is consistent with the experimental outcomes. Additionally, the software utilized in this study enables for testing noise in the studied area, with an Adeq precision greater than 4, indicating acceptable precision. In this work, the Adeq precision of 33.015 indicates that the signal is appropriate.

Table 2

Statistics parameter fit of ETL dye adsorption

Std. Dev.1.54R²0.9709
Mean 21.95 Adjusted R² 0.9630 
CV % 7.02 Predicted R² 0.9385 
  Adeq precision 33.0153 
Std. Dev.1.54R²0.9709
Mean 21.95 Adjusted R² 0.9630 
CV % 7.02 Predicted R² 0.9385 
  Adeq precision 33.0153 

In Table 3 of the statistical analysis, the estimated coefficients of the model, standard error, and variance inflation factor (VIF) are presented. In this study, the VIF value is equal to 1, which indicates the orthogonality of the factors.

Table 3

Estimation of the parameter effects on adsorption capacity

FactorCoefficient estimateStandard errorVIF
Intercept 21.95 0.3977  
A – bed depth 5.30 0.5446 1.0000 
B – flow rate −4.41 0.5446 1.0000 
C – concentration 7.83 0.5446 1.0000 
FactorCoefficient estimateStandard errorVIF
Intercept 21.95 0.3977  
A – bed depth 5.30 0.5446 1.0000 
B – flow rate −4.41 0.5446 1.0000 
C – concentration 7.83 0.5446 1.0000 

The linear model estimated to demonstrate the relationship between the coded parameters and the adsorption capacity is expressed as follows:
formula
When expressed as a function of the actual parameters, the estimated linear model is as follows:
formula
where BD is the bed depth (cm), FR is the feed flow rate (mL/min), and C is the initial concentration (mg/L).
The mathematical formulation derived from the full factorial design provides the opportunity to determine the significant order of the influence of operating dynamic parameters of the column. This ranking is based on the coefficients of the formula and is represented as follows:
formula
Figure 6 presents both a 2D (a) and 3D (b) representation of the relationship between adsorption capacity and dynamic parameters.
Figure 6

(a) Contour plot of ETL dye adsorption and (b) 3D response surface of ETL dye adsorption at C = 62.5 mg·L−1.

Figure 6

(a) Contour plot of ETL dye adsorption and (b) 3D response surface of ETL dye adsorption at C = 62.5 mg·L−1.

Close modal

Figure 6 enables the deduction of additional values for the adsorption capacity from the studied area. Based on the statistical tests and diagrams, the polynomial model is considered a valid representation of the experimental design. This model allows for the prediction of responses for other values included in the studied area.

The color degradation in Figures 5 and 6 indicates that the adsorption capacity is positively influenced by the dye concentration and bed depth, whereas the inlet flow has a negative effect on dye removal. The same observations are recorded in the dynamic study.

The dynamic study confirms that brewery waste is effective in removing Bemacid red dye from aqueous solutions. Several dynamic models (BDST, Thomas, Adams-Bohart, and Clark) were used to analyze BTCs in the packed bed column. The BDST model describes the initial part of the curve, while the Thomas model fits well to the entire curve under the selected operational conditions. The results suggest that to increase the amount of dye removed, low flow rates, higher bed altitudes, and high dye concentrations should be used. The Thomas model has a high determination coefficient (R2 ranging from 0.93 to 0.98) under these dynamic conditions. The full factorial design was used to model the adsorption capacity of brewery waste for the studied factors (bed altitude, dye concentration, and flow rate) using a first-degree polynomial model. This approach provides the degree of dependence between the response (adsorption capacity) and the factors, which was approximately 98% in our study.

The present research did not receive any financial support.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Al-Baidhany
J. H.
&
Al-Salihy
S. T.
2016
Removal of methylene blue dye from aqueous solution by using commercial granular activated carbon with different types of adsorbers
.
Mesopotamia Environmental Journal
2
,
1
11
.
Bennani
K. A.
,
Mounir
B.
,
Hachkar
M.
,
Bakasse
M.
&
Yaacoubi
A.
2015
Adsorption of cationic dyes onto Moroccan clay: Application for industrial wastewater treatment
.
Journal of Materials and Environmental Science
6
,
2483
2500
.
Bulgariu
L.
,
Escudero
L. B.
,
Bello
O. S.
,
Iqbal
M.
,
Nisar
J.
,
Adegoke
K. A.
,
Alakhras
F.
,
Kornaros
M.
&
Anastopoulos
I.
2019
The utilization of leaf-based adsorbents for dyes removal: A review
.
Journal of Molecular Liquids
276
,
728
747
.
Chowdhury
S.
,
Yasin
M.
,
Uddin
M. T.
&
Islam
M. A.
2016
Batch and continuous (fixed bed column) adsorption of methylene blue by rubber leaf powder
.
International Journal of Integrated Sciences & Technology
2
,
24
28
.
García-Mateos
F. J.
,
Ruiz-Rosas
R.
,
Marqués
M. D.
,
Cotoruelo
L. M.
,
Rodríguez-Mirasol
J.
&
Cordero
T.
2015
Removal of paracetamol on biomass-derived activated carbon: Modeling the fixed bed breakthrough curves using batch adsorption experiments
.
Chemical Engineering Journal
279
,
18
30
.
Gassan
H.
,
Ben Driss Alami
S.
,
Ochando-Pulido
J. M.
&
Víctor-Ortega
M. D.
2014
Iron removal from liquid effluents by olive stones on adsorption column: Breakthrough curves
.
Ecological Engineering
73
,
270
275
.
Ghosh
A.
,
Rechakrabarti
S.
,
Biswas
K.
&
Dghosh
U.
2015
Column performance on fluoride removal by agglomerate Ce(IV)-Zr (IV) mixed oxide nanoparticles packed fixed –beds
.
Journal of Environmental Chemical Engineering
3
,
653
661
.
Gupta
V. K.
&
Suhas
A.
2009
Review application of low-cost adsorbents for dye removal – A review
.
Journal of Environmental Management
90
,
2313
2342
.
Hutchins
R. A.
1973
New method simplifies design of activated carbon systems
.
Chemical Engineering
80
,
133
138
.
Jain
A. K.
,
Gupta
V. K.
,
Bhatnagar
A.
&
Suhas
A.
2003
Utilization of industrial waste products as adsorbents for the removal of dyes
.
Journal of Hazardous Materials
101
(
1
),
31
42
.
Kannan
K.
,
Senthilkumar
K.
,
Akilamudhan
P.
,
Sangeetha
V.
&
Manikandan
B.
2012
Studies on effectiveness of low cost adsorbents in continuous column for textile effluents
.
International Journal of Bioscience, Biochemistry and Bioinformatics
6
,
398
402
.
Manase
A.
2012
Fixed bed adsorption studies of rhodamine B dye using oil palm empty fruits bunch activated carbon
.
Journal of Engineering Research and Studies
3
,
03
06
.
Ming-Shen
C.
&
Hsing-Ya
L.
2002
Equilibrium and kinetic modeling of adsorption of reactive dye on cross-linked chitosan beads
.
Journal of Hazardous Materials B
93
,
233
248
.
Mobasherpour
I.
,
Salahi
E.
&
Asjodi
A.
2014
Research on the batch and fixed-bed column performance of red mud adsorbents for lead removal
.
Canadian Chemical Transactions
2
,
83
96
.
Muhamad
H.
,
Doan
H.
&
Lohi
A.
2010
Batch and continuous fixed bed column biosorption of Cd2+ and Cu2+
.
Chemical Engineering Journal
158
,
369
377
.
Nihla Kamarudzamana
A.
,
Chia Chayb
T.
,
Amira
A.
&
Abdul Taliba
S.
2015
Biosorption of Mn(II) ions from aqueous solution by Pleurotus spent mushroom compost in a fixed-bed column
.
Procedia – Social and Behavioral Sciences
195
,
2709
2716
.
Nishil
M.
,
Grishkewich
N.
,
Ambrose Waeijen
H.
,
Berry R
M.
&
Chiu Tam
K.
2015
Continuous flow adsorption of methylene blue by cellulose nanocrystal – alginate hydrogel beads in fixed bed columns
.
Carbohydrate Polymers
09
,
1
23
.
Nkem
Y.
,
Abustana
I.
,
Latiffa
M. F. P. M.
,
Bello
O. S.
&
Ahmad
M. A.
2011
Fixed-bed column study for Cu (II) removal from aqueous solutions using rice husk based activated carbon
.
International Journal of Engineering & Technology
11
,
248
252
.
Nouri
H.
&
Ouederni
A.
2013
Modeling of the dynamics adsorption of phenol from an aqueous solution on activated carbon produced from olive stones
.
International Journal of Chemical Engineering and Applications
4
,
4
.
Ouazani
F.
,
Iddou
A.
&
Aziz
A.
2017
Biosorption of Bemacid red dye by brewery waste using single and poly-parametric study
.
Desalination and Water Treatment
93
,
1
9
.
Pei-Jen
L.
,
Chih-Sheng
C.
&
Jia-Ming
C.
2014
Binary adsorption breakthrough curves in fixed bed: Experiment and prediction
.
Journal of the Taiwan Institute of Chemical Engineers
4
,
1608
1617
.
Sekhula
M.
,
Okonkwo
J. O.
,
Zvinowanda
C. M.
,
Agyei
N. N.
&
Chaudhary
A. J.
2012
Fixed bed column adsorption of Cu (II) onto maize tassel-PVA beads
.
Chemical Engineering Process Technology
3
,
1
5
.
Shadeera
R.
&
Nagapadma
M.
2015
Modeling of fixed bed column studies for adsorption of azo dye on chitosan impregnated with a cationic surfactant
.
International Journal of Scientific & Engineering Research
6
,
538
545
.
Sicupira
D. C.
,
Tolentino Silva
T.
,
Ladeira
A. C. Q.
&
Mansur
M. B.
2015
Adsorption of manganese from acid mine drainage effluents using bone char: Continuous fixed bed column and batch desorption studies
.
Brazilian Journal of Chemical Engineering
32
(
02
),
577
584
.
Sivakumar
P.
&
Palanisamy
P. N.
2009
Packed bed column studies for the removal of Acid blue 92 and basic red 29 using non-conventional adsorbent
.
Indian Journal of Chemical Technology
16
,
301
307
.
Wong
Y. C.
,
Szeto
Y. S.
,
Cheung
W. H.
&
McKay
G.
2004
Adsorption of acid dyes on chitosan – Equilibrium isotherm analyses
.
Process Biochemistry
39
,
693
702
.
Yagub
M. T.
,
Sen
T. K.
,
Afroze
S.
&
Ang
H. M.
2014
Dye and its removal from aqueous solution by adsorption: A review
.
Advances in Colloid and Interface Science
209
,
172
184
.
Yang
Q.
,
Zhong
Y.
,
Xiaoming
L.
,
Xin
L.
,
Luo
K.
,
Xiuqiong
W.
,
Hongbo
C.
,
Yang
L.
&
Guangming
Z.
2015
Adsorption-coupled reduction of bromate by Fe(II)–Al(III) layered double hydroxide in fixed-bed column: Experimental and breakthrough curves analysis
.
Journal of Industrial and Engineering Chemistry
28
,
54
59
.
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