Bentonite blended with bagasse ash as an adsorbent for reactive red 198 dyes

Dye-laden industrial effluents bring several environmental challenges with them. Both reactive and basic dyes are consumed in the textile, paper, plastic, and other industries as coloring agents. The textile industry, uses about 56% of total annual synthetic dye production worldwide (Jain & Gogate 2018). Because of incomplete binding onto materials and inadequate control, huge volumes of dyes are discharged into the environment in untreated wastewater, leading to serious health and environment issues.

Dyes are harmful to living organisms due to their recalcitrant and non-biodegradable properties (Moussavi & Khosravi 2011). Aquatic organisms and humans can be affected by carcinogenic and teratogenic effects of dye (Song et al. 2017). Dyes can also affect various human organs including the kidney, liver, and nervous system (Zhou et al. 2015). Discharge of wastewater containing dyes into water bodies reduces the photosynthetic activity of aquatic plants and, in turn, the natural equilibrium.

The binding rates of reactive dyes onto material fibers are estimated to be between 60 and 70%, so that about 40% of that used is released into the environment (Alimohammadi et al. 2016). Dyes are water soluble and inhibit light transmission through water, and cannot be removed easily using conventional treatment techniques. Reactive dye-laden effluents are toxic and mutagenic, and enhance water body hazards as well as being difficult to remove by biological treatment. Thus, it is necessary to design efficient wastewater treatment technologies for dye removal or remediation before effluents are released into the environment. Known technologies including the advanced oxidation process, ion exchange, microbial fuel cells, ozonation, coagulation, flocculation, and adsorption have been applied to remediate or remove different dyes from industrial effluents (Adebayo et al. 2020). The limitations of these technologies include cost, fouling issues, regular maintenance, and complicated operational procedures (Garg et al. 2003; Dizge et al. 2008).

Adsorption is still one of the most widely used dye treatment methods, taking the lead in high removal efficiency, operating simplicity, low operating cost, eco-friendliness, and the availability of a large variety of adsorbents (Gupta et al. 2004; Khalilzadeh Shirazi et al. 2020). Commercial activated carbon is commonly used as an adsorbent for dye removal from wastewater due to its adsorptive efficiency and capacity. Its high associated cost and difficulty in regeneration offer disadvantages (Srivastava et al. 2007). Various adsorbents have been investigated in their natural form for dye removal from wastewater including bagasse fly ash (Mall et al. 2005), bentonite/charred dolomite (Khalilzadeh Shirazi et al. 2020), natural bentonite (Carvalho et al. 2019), fly ash (Dizge et al. 2008), and pine cone (Dawood & Sen 2012). Many of these suffer from long synthesis procedures, however, or short lifespans making their use impractical in many parts of the world. Consequently, efforts have been made to obtain easily accessible and efficient adsorbents that can be used to treat dye-laden wastewaters in low-income countries such as Ethiopia.

Adsorption can be enhanced by blending with bio-waste materials, which enhance or increase an adsorbent mineral's capacity. Sugar cane bagasse ash (SCBA) is used in landfill and has negligible value (Huabcharoen et al. 2017) but creates environmental and human health problems when handled improperly and disposed into the soil, air or water (Farirai et al. 2020). Thus, agricultural wastes used as adsorbent reduce solid waste volumes and follow the principle of generating money from waste.

Little experimental data or literature are available on reactive dye removal – e.g. reactive red 198 (RR198) – using bentonite blended with SCBA. Bentonite is widely available and with its dye adsorption capacity (Pandey & Ramontja 2016) can be used as a low-cost dye removal adsorbent (Toor et al. 2015; Carvalho et al. 2019).

In this study, adsorption experiments were carried out to investigate the application of a novel blended adsorbent system – bentonite and SCBA – for reactive dye sorption.

Bentonite and SCBA were collected from Gewane (Afar region) and Wonji Shewa sugar factory (Oromia region), Ethiopia, respectively. The bentonite was crushed, washed using distilled water, dried in an oven (FAITHFUL) at 105 °C for 12 hours, and ground to a powder. The latter was screened in a sieve shaker to 125 μm particle size. The SCBA was also washed using distilled water and dried in an oven (FAITHFUL) at 105 °C for 12 hours before screening to 125 μm particle size. The adsorbent materials were stored in glass bottles. The RR198 dye was obtained from the Yirgalem Addis textile factory, Addis Ababa, Ethiopia. The dye's characteristics and structure are illustrated in Table 1 and Figure 1, respectively. A 1,000 mg/L stock dye solution was prepared using distilled water and diluted as necessary to obtain dye solutions required for batch experiments.

Scanning electron microscopy (SEM) (INSPECT, F50, USA) was used to examine the surface morphology of adsorbent materials. Fourier transform infrared spectroscopy (FTIR) (Perkin Elmer, USA) was used to analyze the adsorbents' functional groups. Infrared spectra were recorded in the 4,000–400 cm⁻¹ region at 4 cm⁻¹ resolution. Brannuer-Emmett-Teller (BET) (SA-9600 series, Japan) was used to determine raw adsorbent specific surfaces. The structure of the crystalline phases of the bentonite and SCBA were identified using XRD (XRD-7000, Japan). UV A spectrophotometer (JASCO V-770) was used to determine the concentration of RR198 in the solution by recording the UV absorbance at λmax 520 nm.

The effect on adsorption of parameters such as solution pH (2 to 12), contact time (0 to 240 minutes), adsorbent dosage (0.5 to 3 g/L), and initial dye concentration (15 to 100 mg/L) were investigated. Batch experiments were carried at 25 °C using a magnetic stirrer at 200 rpm. The desired quantity of the blended adsorbents was added in 250 mL Erlenmeyer flasks (working volume 200 mL) containing 50 mg-RR198/L. The solution pH was adjusted by adding 0.1M HCl or NaOH. After mixing for a given contact time, the mixture was filtered using Whitman filter paper (185 mm diameter Cat No 1004 185) and the filtrate analyzed. An adsorbent recycling study was also carried out.

Three known established kinetic models – the pseudo-first-order (PFO), pseudo-second-order (PSO) and intraparticle diffusion (ID) models – were chosen to analyze adsorption capacity at different contact times and reaction rate constants involved during adsorption.

The parameters K (L/kg) and n (dimensionless) are the Freundlich constants, and are related to the total adsorption capacity and intensity of adsorption, respectively. The Freundlich constant K_f is used to evaluate the relative capacity or power of adsorption of an adsorbent.

The specific surfaces determined were 57.7 m²/g for raw bentonite and 4.94 m²/g for raw SCBA. Previous study reports for bentonite were 66.2 (Al-Essa 2018), 25.7 (Toor et al. 2015), and 13.9 m²/g (Carvalho et al. 2019), and 1.5 m²/g for raw SCBA (Farirai et al. 2020). The XRD results for bentonite and SCBA are shown in Figure 2(a) and 2(b) respectively. Strong peaks at 2 = 19.5°, 26.5°, and 28.4° for bentonite and 2 = 21.7°, 24.3°, and 28° for SCBA were found. The bentonite peaks at 2 = 26.5°, 28.4°, and 35.2° show the presence of quartz, as do those at 2 = 24.3°, 28° and 35.7° for SCBA.

The FTIR results for both adsorbents are shown in Figure 3(a) and 3(b). The raw bentonite absorption band at 3,630 and 1,638 cm⁻¹ indicates O-H group bending and stretching in the hydration water on the bentonite surface. Al-Essa (2018) observed peaks at 3,450 and 1,650 cm⁻¹ indicating the presence of OH groups on the bentonite surface. Özcan & Özcan (2004) observed peaks at 3,446 and 3,629 cm⁻¹, because of OH group stretching bands. While the band at 1,640 cm⁻¹corresponds to OH group deformation on natural bentonite.

The very strong absorption band at 1,009 cm⁻¹ is recognized as representing Si-O band stretching vibration, providing strong evidence for the presence of a silicate structure. The peaks at 779 and 507 cm⁻¹ show the presence of quartz in the bentonite. Zhirong et al. (2011) report that the band at 796 cm⁻¹ confirms the presence of quartz.

The FTIR analysis of SCBA shows a peak at 3,315 cm⁻¹ representing the presence of OH on the ash surface. The peaks at 847, 784, and 422 cm⁻¹ are due to Si-O-M (metal impurity) bonding, Al-O-Si bending vibrations, Si-O-Al deformation, and Si-O stretching, indicating the presence of crystalline quartz. The absorption band at approximately 770 to 797 cm⁻¹ is caused by Si–O–Si symmetric stretching (Rahman et al. 2015).

Figure 4(a) and 4(b) show the surface morphology of raw bentonite and SCBA before adsorption and Figure 4(c) after adsorption. The SEM images in Figure 4(a) and 4(b) show that both adsorbents are rough and dense before adsorption, with pores and active sites available; the internal surface can also be seen. This confirms successful adsorbent formation, with homogeneous component dispersion and porous structures on the surfaces, which increase their dye sorption capacity. However, Figure 4(c) shows a rather smaller proportion of irregular structures, including pores, which can be attributed to dye adsorption on the adsorbent surfaces with active sites occupied. The surface texture of SCBA is similar to that observed by others (Alves et al. 2017; Farirai et al. 2020). The bentonite surface morphology is also similar to that reported by others (Javed et al. 2018; Carvalho et al. 2019).

The solution pH is an essential factor in sorption because it can alter the adsorbate-adsorbent interaction surface charge, enhancing attraction or repulsion (Akar et al. 2018). In this study, the batch runs were carried out at various dye solution pH values (2, 4, 6, 8, 10, and 12) at 25 °C and stirred at 200 rpm. Figure 5 shows the impact of pH on RR198 sorption onto the blended adsorbents dosed at 2 g/L, initial dye (30 mg/L), and contact time 60 minutes. As can be seen, removal efficiency fell from 86 to 24% with increasing pH. The maximum dye removal efficiency and sorption ability were achieved at pH 2. Acidic conditions (low pH) enhance H⁺ ion concentrations, which develop a positive surface charge by binding the H⁺ ion onto the adsorbent surface. Negatively charged groups on the dye surface were attracted by the positive groups on the adsorbent surface (Alimohammadi et al. 2016). The effect of pH on the adsorption of reactive dyes such as reactive red 2, reactive orange and reactive blue 4, is to yield maximum removal efficiency at low values. For instance, when using coir pith as the adsorbent Çolak et al. (2009) and Salari et al. (2021) also reported the highest RR198 removal efficiency at pH 2.

A series of batch runs was carried out using different adsorbent doses; that is, 0.5, 1, 1.5, 2.5, and 3 g/L, other conditions remaining the same (stirred at 200 rpm, temperature 25 °C, contact time 60 minutes, initial dye concentration 70 mg/L, and solution pH 2). Figure 6 shows that as the adsorbent dose was increased from 0.5 to 3 g/L, the proportional amount of dye adsorbed (q_e) fell from 8.24 to 3.4 mg/g, while dye removal efficiency rose from 27.4 to 73%. Other studies have shown similar phenomena; for example, Carvalho et al. (2019) used activated carbon and bentonite to adsorb direct orange 39 dye, and Das et al. (2020b) removed malachite green dye using bio-degradable sodium alginate/NaOH-treated activated sugar cane bagasse charcoal. Increasing the adsorbent dose increases the number of active sites available (Dehvari et al. 2017).

A similar series of runs to those reported in sub-section 3.3 involved different initial dye concentrations; that is, 15, 30, 50, 70, and 100 mg/L, other conditions remaining the same, with contact time 150 minutes, adsorbent dose 3 g, and pH 2. Figure 7 shows the results. As can be seen, a higher initial dye concentration leads to a decrease in proportional dye removal. The amount of dye adsorbed increased from 0.89 mg/g at 15 mg/L initial dye concentration to 3.8 mg/g at 100 mg/L initial dye concentration, while proportional dye removal fell from 89% at 15 mg/L to 57% at 100 mg/L initial concentration. If active sites are available on the adsorbent surface, proportional dye removal increases with increasing initial concentration, while, if active sites are saturated, proportional dye removal decreases (Zhou et al. 2019). Thus, the higher proportional dye removal at lower initial concentration can be attributed to increased probability of binding all dye molecules to vacant adsorbent sites. Mahanna & Samy (2020) reported similar results in reactive red 195 dye removal using acid-modified soybean leaves.

A further, similar set of batch experiments was carried out with different contact times; that is, 0, 30, 60, 90, 120, 150, 180, 210, and 240 minutes. All other factors were kept the same as noted above (sub-section 3.4). Figure 8 shows that dye removal began at some point within 50 minutes of start-up, and then proceeded at a fairly constant rate until almost reaching equilibrium after 150 minutes, after which removal efficiency decreased, perhaps because desorption occurred and active sites were occupied, leading to an increase in dye concentration in solution (Saha et al. 2012; Das et al. 2020b).

Table 3 shows that the PSO model fitted the trial results better than the other models, with R² = 0.98 indicating that chemical adsorption is more probable than physical adsorption. This is attributed to electron interaction between RR198 dye molecules and the blended bentonite and SCBA during chemical adsorption (Alimohammadi et al. 2016; Tayebi et al. 2016).

The Langmuir isotherm model was evaluated using Equation (6), and constants q_max (mg/g) and b (L/g) were analyzed from the slope and intercept of C_e/q_e vs C_e respectively. The dye concentration range was from 15 to 100 mg/L, but no other factors changed. The values of q_max and b were 4.24 mg/g and 0.097 L/g. The Freundlich isotherm model was analyzed using Equation (7), and the constant values K and n were evaluated from the intercept and slope of logq_e vs logC_e respectively. The calculated values of n and K were 2.4636 (n > 1) and 0.755 L/kg, indicating the greater favorability of the sorption process in the Freundlich than the Langmuir model. The sorption equilibrium result indicated the best fit for the Freundlich model (R² = 0.95), R² for the Langmuir model was 0.87 (Table 4). This showed the heterogeneous distribution of binding sites on the surface of the blended adsorbents.

The dye removal results were evaluated using RSM. Figure 9 shows that the actual and predicted dye removal efficiencies were close for the RR198 removal studies.

Figure 10(a) shows that proportional dye removal increased with increasing adsorbent dose and contact time, and that the maximum adsorption of 82.9% was achieved with an adsorbent dose of 1.75 g/L and contact time 105 minutes. This arose because of the number of vacant active adsorbing sites and the availability of enough time for adsorbent-adsorbate contact (Igwegbe et al. 2019; Das et al. 2020a).

The interactive effect of contact time and initial adsorbate concentration on RR198 removal efficiency is illustrated in the 3D graph in Figure 10(b). This shows that RR198 removal efficiency increased with increasing contact time. The maximum calculated adsorption efficiency was 93.2%, at 197 minutes contact time and just under 19 mg/L initial dye concentration. The interaction model results indicate that adsorption efficiency was higher with longer contact time because enough time is available for interaction between dye molecules and the adsorbent surface (Brahmi et al. 2019).

Figure 10(c) illustrates the interactive effect of adsorbent dose and initial dye concentration on RR198 removal. As can be seen, raising the adsorbent dose from 0.5 to 3 g/L increased dye removal efficiency, while the increase in initial dye concentration reduced efficiency. The highest proportional dye removal was achieved at low initial dye concentration and high adsorbent dose, and the optimal result was 90.7% dye removal efficiency at a bit more than 15 mg/L initial dye concentration and 2.9 g/L blended adsorbent dose. The adsorbent dose increase enhanced the availability of free active sites on the adsorbent surface (Saha et al. 2012; Davarnejad et al. 2020). This may occur because of the reduced ratio of active sites to dye molecules, so that there was insufficient vacant space on the adsorbent surface (Brahmi et al. 2019).

Figure 11 shows the blended adsorbent's removal efficiency through three cycles of desorption-adsorption. As can be seen, the proportional removal performance fell from 86 to 84.2% between the first and third cycles. At the same time, the adsorption capacity also fell from 2.6 to 2.3 mg/g. These results confirm that bentonite blended with SCBA can be reused for RR198 removal from wastewaters.

RR198 adsorption was investigated using bentonite blended with SCBA as the adsorbent. The adsorption isotherm model was fitted better by Freundlich than Langmuir, while the pseudo-second-order model fitted the adsorption kinetics best. CCD under RSM was used to investigate the interaction between contact time, adsorbent dose, and initial dye concentration – three independent parameters – on RR198 adsorption. Use of RSM enabled determination of the optimal parameters for removal of the dye, and the precise values of 107 minutes contact time, 0.934 g adsorbent dose, and 15 mg/L initial dye concentration were found to give 85.2% RR198 dye removal efficiency. Desorption and reusability studies were carried out in batch mode and confirmed that bentonite blended with SCBA can be used multiple times for dye removal from aqueous solution. In general, the results indicated that natural bentonite blended with sugar cane bagasse ash can be used to treat textile industry wastewaters to remove RR198 dyes. The blend is an efficient and environment-friendly adsorbent.

This work was funded by Addis Ababa Science and Technology University and Debre Tabor University. The authors would like to thank the Environmental Engineering Department of Addis Ababa Science and Technology University, for offering complete laboratory facilities, Yirgalem Addis Textile factory for providing reactive dyes, Wonji Shewa sugar factory for providing sugar cane bagasse ash and Candela pharmaceutical industry for supporting FTIR instrument analysis of functional sample groups.

We declare that there is no conflict of interest.

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