Valorization of agro-industrial wastes of sugarcane bagasse and rice husk for biosorption of Yellow Tartrazine dye

Abstract

The use of agro-industrial wastes as biosorbents is a promising alternative for sustainable, economical and effective adsorption. However, few studies evaluate the use of Brazilian agro-industrial waste as biosorbents without physicochemical pre-treatment. This study explored the potential of sugarcane bagasse (SCB) and rice husk waste (RHW) as low-cost biosorbents for yellow tartrazine dye removal. Characterization of the materials were carried out using ATR-FTIR, SEM, pH_PZC and lignocellulosic composition. Cellulose and hemicellulose were the major constituents of both materials. By the Box-Behnken experimental design, the response surfaces indicated maximum removal of 60.1% for SCB and 83.1% for RHW. While the q_max of SCB and RHW for tartrazine were relatively low at 2.45 ± 0.03 mg g^-1 and 3.55 ± 0.02 mg g^-1, respectively, the potential for achieving higher dye removals by increasing the adsorbent dosage in large-scale applications warrants further investigation. For both biosorbents, the pseudo-second-order kinetic model and Brouers-Sotolongo isotherm provided the best fit for the data, and the adsorption processes were spontaneous and exothermic. In conclusion, SCB and RHW demonstrated high tartrazine removal, promoting sustainable agro-industrial waste management.

Key words biosorbents; agro-industrial wastes; tartrazine; lignocellulosic biomass; low-cost adsorbents

INTRODUCTION

Synthetic dyes, derived from petroleum-based compounds, are organic substances extensively employed across a range of industries, including textiles, food, and cosmetics (Ardila-Leal et al. 2021, Oliveira et al. 2021). The production of these dyes, which involves complex chemical processes and the use of potentially hazardous reagents, results in a wide variety of colors and shades (Alegbe & Uthman 2024, Singh et al. 2018). Despite their potential applications, the complexity of their molecular structures and the presence of specific functional groups hinder their biodegradation, leading to environmental persistence and posing a substantial obstacle for wastewater treatment processes (Millbern et al. 2024, Das et al. 2021).

Tartrazine yellow dye (TAR), also known as Acid Yellow 23 (CAS number 1934-21-0; E-102; FD&C Yellow 5 and C.I. 19140), is an anionic azo dye. TAR confers, intensifies or restores the color of products from yellow to orange, depending on its concentration (Leulescu et al. 2018, Pay et al. 2023). Given its extensive use in the food, pharmaceutical, cosmetic, and textile industries, TAR poses a significant risk of contaminating water bodies through raw industrial wastewater discharge (Micheletti et al. 2023). To safeguard environmental integrity, effluent treatment techniques are employed to eliminate contaminants from industrial wastewater prior to its discharge (Balarak et al. 2021, Pillai 2020).

Adsorption is a widely employed technique for the treatment of dye-contaminated effluents owing to its efficiency, ease of implementation, and comparatively low operational costs (Balarak et al. 2020, Paul et al. 2022). This process involves the binding of dye molecules to the surface of a solid adsorbent material, effectively removing them from the wastewater (Yousef et al. 2020). The choice of adsorbent significantly influences the adsorption capacity and selectivity, with common materials including activated carbon, activated alumina, zeolites, and various polymeric resins (Husien et al. 2022, Escamilla-Lara et al. 2023, Kainth et al. 2024). By carefully selecting the appropriate adsorbent and optimizing operating conditions, adsorption can achieve remarkable removal rates for a diverse range of dyes, making it a valuable tool for environmental remediation. To address specific wastewater characteristics or challenges, additional treatment methods such as photocatalysis, nanofiltration, ozonation, anodic oxidation, and electrocoagulation can be employed (Balarak et al. 2019, Amdeha 2021, Al-Musawi et al. 2021).

The production of most adsorbents consists of complex and high-cost processes, in addition to generating wastes from physicochemical procedures that must be treated and properly disposed (Wang & Wang 2022). To enhance the sustainability of chemical processes, the development of innovative adsorbents is paramount. These materials must adhere to the principles of Green Chemistry, prioritizing cost-effective acquisition, utilization, and disposal, while minimizing environmental impact (Amdeha 2023, Elgarahy et al. 2021). In this way, the substantial generation of agro-industrial wastes (AIW) in Brazil presents a unique opportunity to valorize these materials as biosorbents rather than discarding them. This approach aligns with the principles of the circular economy, promoting the use of renewable resources, waste minimization, and material reuse (Martinez-Burgos et al. 2021). By adopting this strategy, we can reduce our reliance on natural resources, mitigate the environmental impacts associated with the production and disposal of traditional adsorbents, and contribute to a more sustainable chemical industry (Kainth et al. 2024).

Driven by the high production of AIW, the availability of raw materials, and the need to treat industrial effluents, several researchers have sought alternative materials for the adsorption of dyes in wastewater (Mosoarca et al. 2022, Joshiba et al. 2022, Oliveira et al. 2018, Módenes et al. 2019). The use of AIW is considered a strategic proposal for adsorption applications since they often contain solid biopolymers such as cellulose, hemicellulose, and lignin, which possess a rich array of functional groups, including hydroxyl and carboxyl groups. These functional groups can act as effective binding sites for dyes, facilitating the removal of contaminants from aqueous solutions (Mo et al. 2018, Quesada et al. 2019). This approach is more economical and environmentally friendly, offering greater sustainability for the valorization of these wastes (Bilal et al. 2022).

The application of AIW without chemical or physical treatment as biosorbents offers several advantages in terms of process sustainability (Dai et al. 2018). Firstly, the application of untreated AIW as biosorbents significantly reduces production costs due to the abundance of available material. The lignocellulosic composition of AIW provides a natural framework for adsorption processes, eliminating the need for extensive pretreatment. Secondly, avoiding physicochemical treatments eliminates the generation of harmful effluents, wastes, and polluting gases, thereby minimizing the environmental impact (Asemave et al. 2021, de Almeida et al. 2024). To mitigate the environmental impacts of AIW, there is potential to repurpose it for alternative applications, such as adsorption (Singh et al. 2020, Kumawat et al. 2022).

Rice and sugarcane cultivation play a pivotal role in human activities, serving as sources of food, energy, and various products and by-products. The sugar and alcohol industry generates a substantial quantity of AIW from sugarcane cultures (Lee et al. 2023). In the sugar and alcohol industry, a common practice is to burn sugarcane wastes as fuel in boilers (Ungureanu et al. 2022). However, the excessive generation often surpasses combustion needs, leading to accumulation of wastes. The remaining AIW can be repurposed as plant biomass or explored for other applications (Nunes et al. 2020, Hiranobe et al. 2024, Gbadeyan et al. 2024). Similarly, rice production, a significant agricultural activity in certain regions (Fukagawa & Ziska 2019), generates large amounts of husk waste that is frequently utilized as fuel or biomass (Goodman 2020).

Consequently, this study investigates the potential of two AIW of significance to the Brazilian agribusiness sector, sugarcane bagasse (SCB) and rice husk waste (RHW), as biosorbents for TAR dye in aqueous solutions. Both SCB and RHW were employed in their natural forms, which is a significantly different approach compared to what is typically found in the scientific literature, where AIW are commonly physicochemical activated or transformed in various types of carbons or ashes. Studies for the adsorption of TAR dye have already been carried out using sugarcane bagasse (Tahir et al. 2016) and rice husk modified under pyrolysis processes for the production of activated carbon (Li et al. 2016); however, to the best of our knowledge, no studies were found using these wastes in natura for the removal of TAR (Micheletti et al. 2023).

MATERIALS AND METHODS

Preparation of biosorbents

Sugarcane and rice husk wastes were supplied by agro-industries from the Umuarama city (Paraná, Brazil). The AIW were washed thoroughly with tap water and them with distilled water to remove macroscopic and water-soluble impurities. Afterwards, they were dried in an oven with air recirculation at 105 °C for 24 h. SCB was cut into small pieces using scissors, and then both materials were mechanically processed using an IKA A11 basic automatic mill. Subsequently, the materials were sieved, and particle sizes between 1.18 mm and 0.50 mm were selected.

The biosorbents prepared from sugarcane bagasse and rice husk are designated as SCB and RHW (Figure 1), and they refer to materials in natura, without physicochemical modification, obtained solely through the water-washing and size standardization. They were stored in bottles protected from light, heat, and humidity.

Figure 1
Biosorbents (a) SCB and (b) RHW, after the washing process and standardization of granulometry.

Characterization of biosorbents

Scanning electron microscopy (SEM) images were obtained on a QUANTA 250 FEI microscope with an operating acceleration voltage of 12.50 kV. Prior to imaging, the samples were coated with a thin layer of gold. SCB SEM images were magnified by 359x and 1500x, and the RHW SEM images were magnified by 359x and 1000x.

To determine the lignocellulosic composition, moisture-free biosorbents were used in triplicate. The cellulose, hemicellulose, and lignin contents were determined through compositional analysis (Van Soest 1967). The data was evaluated using analysis of variance (ANOVA), followed by the Tukey test, with a significance of 5% (p<0.05), employing the STATISTICA® 7.0 software.

To evaluate the point of zero charge (pH_PZC), 0.100 g of each biosorbent were mixed in 100 mL of aqueous solution of KCl 1.00 mol L^-1 (Dinâmica® ,99-100.5% pure) solution and 100 mL of deionized water. The experiment was performed in triplicate. After shaking for 15 min, the samples were centrifuged and the final values of pH were measured with a pHmeter (Quimis, Q-400MT) to estimate the pH_PZC values of the biosorbents, as described in Eq. 1 (Batistela et al. 2017, de Oliveira et al. 2019).

$$p{H}_{PZC}=2\bullet p{H}_{KCl}–p{H}_w$$ (1)

Where pH_KCl is the pH final of the aqueous solution of KCl 1.00 mol L^-1 and pH_w is the pH final of the aqueous solution without salt. To evaluate the surface charge of the biosorbents, the relationship presented by Eq. 2 was applied, in which ΔpH is the difference between the pH_KCl and pH_w.

$$\Delta pH=p{H}_{KCl}-p{H}_w$$ (2)

The Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance (ATR-FTIR) spectra were obtained using an ATR-FTIR spectrophotometer (Agilent Cary 630), with a diamond crystal module for attenuated total reflection, from 4000 to 400 cm^-1, 64 scans and 4 cm^-1 of precision.

The X-ray diffractograms of the biosorbents were carried out with a Bragg-Bentane X-ray diffractometer (Shimadzu XRD-6000) using Cu-Kα as radiation source (λ = 0.154 nm) at 40 kV and 30 mA, with diffraction angle (2Ө) varying from 5 to 60°. The crystallinity index (CrI) was determined using Segal’s method by Eq. 3 (Xu et al. 2013, Uzun 2023), in which I_c represents the maximum diffraction intensity for 2θ between 22° and 23° relative to the crystalline form of cellulose (200), and I_am denotes the minimum intensity of the peak between 18° and 19° for amorphous cellulose (110).

$$CrI(\mathrm{\%})=(\frac{I_c-I_{am}}{I_c})\bullet 100$$ (3)

Textural parameters were determined using N₂ adsorption-desorption isotherms (Messer >99.999% pure) on a QuantaChrome® Novatouch LX2, at 77 K. The specific surface area (BET) and pore volume of the biosorbents were calculated by the QuantaChrome® software.

Adsorption experimental design

The adsorption process was evaluated using TAR (C₁₆H₉N₄O₉S₂Na₃, 534.36 g mol^-1, InLab, 85% pure) as a model dye. Box-Behnken design was implemented to assess the adsorption of TAR by SCB and RHW biosorbents. Three variables were investigated at three levels: aqueous solution pH, TAR concentration (C), and adsorbent dosage (D), according to Table I. To adjust the pH to acidic levels, small amounts of concentrated HCl (LabSynth) were added. These experimental conditions were selected based on previous research (de Oliveira et al. 2019, Ponce et al. 2021).

Table I
Coded level of the three variables chosen for Box-Behnken Design.

Variables	Representation	Coded levels
		-1	0	1
pH	pH	1.0	2.0	3.0
Concentration of TAR (mg L-1)	C	5.0	10.0	15.0
Dosage of biosorbent (g L-1)	D	5.0	10.0	15.0

Box-Behnken design was employed comprising 16 experimental runs with a central point replicated four times. To evaluate reproducibility and validate statistical replicates, each experiment was conducted in triplicate. This resulted in a total of 48 randomized runs (n=48) for each biosorbent.

The experiments were conducted in closed Falcon tubes containing 10.0 mL of each aqueous TAR solution at 25 °C and 90 rpm. Initial and equilibrium absorbances were measured at 426 nm using a Shimadzu® UV-1900 spectrophotometer. The adsorption process was allowed to proceed for 120 minutes under each experimental condition (equilibrium). Subsequently, the percentage of TAR removal (%Rem) was calculated according to Eq. 4.

$$\mathrm{\%}Rem=\frac{Ab{s}_0-Ab{s}_t}{Ab{s}_0}\times 100$$ (4)

In which Abs₀ represents the absorbance at 426 nm of TAR solution at the initial time, and Abs_t denotes the absorbance at 426 nm for TAR solution after adsorption equilibrium, and centrifugation of solids, in each condition.

A polynomial equation was proposed for the prediction of % of removal of TAR by SCB and RHW, with linear (L), square (S) and interaction terms. Pareto charts were constructed for each biosorbent to visualize the relative importance of these terms, at 95% of confidence. Model’s validity was assessed using analysis of variance (ANOVA) to evaluate the significance of each term. Based on the analysis of these results, response surface plots and contour plots were constructed for each biosorbent, with the third variable kept at its respective central point. The goodness of fit for these models was assessed by comparing predicted responses to actual experimental data and examining histograms of standardized residuals. For each biosorbent, the critical condition (maximum TAR removal) was evaluated. All analysis were performed in STATISTICA® 7.0 software.

Kinetic and isotherms models

The adsorption kinetics and isotherms were calculated based on the amount of dye solute adsorbed (q), determined by Eq. 5, and expressed in mg g^-1 (Behloul et al. 2018).

$$q=\frac{(C_0-C_f)v}{w}$$ (5)

In which C₀ and C_f are the initial and final dye concentrations, respectively, v is the sample volume and w is the biosorbent mass.

The experiments of adsorption kinetics of TAR were carried out with TAR aqueous solution at 10.0 mg L^-1 with SCB or RHW biosorbents at 1.0 g L^-1 in Falcon tubes, at pH 2.0, in a shaker Marconi® 830/A, at 25 °C and 90 rpm. The adsorption process was monitored at 0, 5, 10, 15, 30, 45, 60, 90 and 120 min. The kinetic models evaluated were pseudo-first-order (PFO) and pseudo-second-order (PSO), as described in Eq. 6 and Eq. 7 (Musah et al. 2022).

$$q_t=q_e(1-e^{k_{1}t})$$ (6)

$$q_t=\frac{k_2{q}_e^{2}t}{1+k_2{q}_{e}t}$$ (7)

In which q_t is the amount adsorbed at the contact time (mg g−¹); q_e is the amount adsorbed at equilibrium (mg g−¹); t is the contact time (min); k₁ is the PFO adsorption constant (min−¹); k₂ is the PSO adsorption constant (g mg−¹ min−¹).

To investigate the adsorption isotherms, experiments were conducted in Falcon tubes using TAR aqueous solution at varying concentrations (0 – 100.0 mg L^-1), with dosage of biosorbents at 1.0 g L^-1, at pH 2.0, in a shaker Marconi® 830/A, at 25 °C and 90 rpm. The adsorption process was allowed to reach equilibrium within 120 min. The isotherm models of Langmuir (Eq. 8), Freundlich (Eq. 9), Temkin (Eq. 10), Sips (Eq. 11), and Brouers-Sotolongo (Eq. 12) were fitted to the isotherm curves (Majd et al. 2022, Ehiomogue et al. 2022).

$$q_e=\frac{q_{max}{K}_L{C}_e}{1+K_L{C}_e}$$ (8)

$$q_e=K_F{C}_e^{\frac{1}{n}}$$ (9)

$$q_e=\frac{RT}{b_T}ln(K_T{C}_e)$$ (10)

$$q_e=\frac{q_{max}(K_S{C}_e{)}_s^m}{1+(K_S{C}_e{)}_s^m}$$ (11)

$$q_e=q_{max}(1-exp(-K_{BS}{C}_e^{{\alpha }_{BS}}))$$ (12)

In which q_e is the amount adsorbed at equilibrium (mg g−¹); q_max is the maximum adsorption capacity (mg g^-1) and K_L is the Langmuir constant (L mg^-1); K_F is the Freundlich constant (mg^-1/ⁿ L¹/ⁿ g^-1), n is the constant indicative of the adsorption intensity; R is the universal gas constant (8.314 J K^-1 mol^-1); T is the absolute temperature of the solution in Kelvin (298.15 K); K_T and b_T are Temkin constants; K_S and m_S are Sips constants; C_e is the equilibrium concentration (mg L^-1); K_BS (L mg^-1) and α_BS are Brouers-Sotolongo constants.

The mathematical models of adsorption kinetics and isotherms described in the Eqs. 6-7 and Eqs. 8-12, respectively, were fitted to the experimental results. The selection of the best model was based on the integrated analysis of the values of coefficient of determination (R²), residual sum of squares (RSS) and chi-square parameter (χ²) (Behloul et al. 2018).

Adsorption thermodynamics

To investigate the effect of temperature on adsorption isotherms, aqueous solutions of TAR at varying concentrations (0-100.0 mg L^-1) were prepared in Falcon tubes containing SCB or RHW biosorbents at a dosage of 1.0 g L^-1. The tubes were maintained at pH 2.0 and agitated using a Marconi® 830/A shaker at 180 rpm for 120 minutes. The temperature was varied at 30 °C, 40 °C, and 50 °C. The aforementioned adsorption isotherm models (Eqs. 8-12) were fitted to the experimental data, and the best-fit model was determined based on the R² coefficient, residual sum of squares (RSS), and chi-square parameter, as previously described.

The literature presents varying perspectives on the appropriate method for calculating thermodynamic adsorption parameters by the equilibrium constants (Chen et al. 2022). Several studies have adopted the use of K° instead of the direct K values obtained from the isotherm models (Spessato et al. 2021, Bedin et al. 2016). This approach is considered more accurate for determining thermodynamic parameters derived from best-fit isotherm models (Lima et al. 2019). Therefore, K° values of TAR adsorption by RHW and SCB were calculated from the equilibrium constants of the best-fit isotherm parameters, for each temperature, according to Eq. 13.

$$K°=\frac{1000\bullet K\bullet M_{dye}\bullet [dye]°}{\gamma }$$ (13)

In which K° is the dimensionless equilibrium thermodynamic constant; K is the equilibrium constant obtained by the best fitted isotherm model; γ is the activity coefficient; M_dye is the molecular mass of the dye and [dye]° is the concentration of the dye (1 mol L^-1).

The thermodynamic parameters of TAR adsorption by each biosorbent were initially obtained by applying Eq. 14 (Van’t Hoff linear equation) and plotting ln K° versus 1/T (Lima et al. 2020). The standard enthalpy change (ΔH°) and standard entropy change (ΔS°) were obtained by the angular and linear coefficients of the adjusted line, respectively. Subsequently, the standard Gibbs free energy change (ΔG°) was determined using Eqs. 15-16 (Lima et al. 2019).

$$ln(K°)=\frac{-∆H°}{R}\bullet \frac{1}{T}+\frac{∆S°}{R}$$ (14)

$$∆G°=-RTln(K°)$$ (15)

$$∆G°=∆H°-T∆S°$$ (16)

In which R is the universal gas constant (8.314 J K^-1 mol^-1) and T is the temperature in Kelvin.

RESULTS AND DISCUSSION

Characterization

SEM images of the surface of SCB and RHW are presented in Figure 2. On the surface of the SCB, a large number of small overlapping layers were observed, Figure 2 (a). Meanwhile, for the RHW, a smooth flat surface structure with some grooves was observed in Figure 2 (b), with a small number of cavities that resemble pores.

Figure 2
Scanning Electron Microscopy of biosorbents (a) SCB at 359x, (b) SCB at 500x, (c) RHW at 359x, (d) RHW at 1000x.

In the literature, raw sugarcane bagasse exhibited a rigid and compact morphology (Corrales et al. 2012), as observed in the present study. Regarding the rice husk evaluated as an adsorbent, various surface textures and differing groove sizes in its structure were observed, suggesting an influence on its adsorption capacity (Schneider et al. 2022).

From the textural analysis, SCB exhibits a BET surface area of 12.53 m² g^-1, a value approximately 21.2% greater than the area of RHW, which was 10.34 m² g^-1. The higher BET surface area of SCB compared to RHW can be attributed to the greater presence of overlapping material layers, as observed in the SEM characterization analysis shown in Figure 2. Regarding pore volume, SCB had a total pore volume of 10.72 × 10^-3 cm³ g^-1 and RHW presented 15.66 × 10^-3 cm³ g^-1, which corresponds to 54.4% higher than for RHW. Although RHW has pores, the effect of these overlapping layers in SCB was more significant, which explains the greater surface area.

Lower BET surface area values were reported for SCB and RHW subjected to alkali treatment (NaOH 0.1 mol L^-1, 24 h), 4.88 and 1.64 m² g^-1, respectively (Ponce et al. 2021), indicating that this treatment affects the reduction of area. In the literature, BET surface area values have been reported for various TAR biosorbents. For hydrochars from Pinus caribaea prepared by chemical activation (NaOH 0.1 mol L^-1, 24 h) followed by thermal activation (200 °C), the BET surface area was 6.4 m² g^-1 (Andrade et al. 2023). Activated carbon based on cola nut shells had a BET surface area of 6.276 m² g^-1 (Brice et al. 2021). Finally, activated carbon from Attalea speciosa had a BET surface area of 18.93 m² g^-1 (Reck et al. 2018). In this context, the BET surface area values of SCB and RHW are consistent with those found for lignocellulosic materials.

Table II presents the percentages of lignocellulosic components of the SCB and RHW biosorbents, in terms of cellulose, hemicellulose and lignin.

Table II
Lignocellulosic composition of the SCB and RHW biosorbents.

Parameters	SCB	RHW
Cellulose (g 100-1 g-1)	39.8a ± 1.9	37.3a ± 1.7
Hemicellulose (g 100-1 g-1)	25.9a ± 2.6	27.5a ± 1.9
Lignin (g 100-1 g-1)	7.1a ± 0.8	12.3b ± 1.0
Other components (g 100-1 g-1)	~27.2	~22.8

• Caption: Means followed by the same lowercase letter in the same line do not differ from each other by Tukey’s test (p<0.05).

In Table II, the lowercase letters adjacent to the means refer to the comparisons between values using the Tukey Test (p<0.05), where identical letters in the same line indicate that the factors do not differ significantly. For example, mean values marked with the letter “a” are statistically equal to each other at the 5% significance level. Cellulose was the predominant component in the biosorbents, followed by hemicellulose, with similar values in SCB and RHW, which were marked with the letter “a”, there were no significant differences between their means. Conversely, for lignin, the Tukey test showed the letters “a” and “b”, indicating a statistical difference between the means at the 5% significance level, where lignin content was higher in RHW compared to SCB. Other components present in the waste include ashes, proteins and carbohydrates (Syguła et al. 2024).

Values of 51.0, 20.2, and 14.8 g 100^-1 g^-1 were found for cellulose, hemicellulose and lignin, respectively, in sugarcane bagasse biosorbents chemically treated with NaOH (0.10 mol L^-1) for 24 h (de Oliveira et al. 2019). Another study applying the same biosorbent and chemical treatment reported levels of 58.76, 17.67 and 12.74 g 100^-1 g^-1 for cellulose, hemicellulose and lignin, respectively, while rice husk presented contents of 49.63, 10.44 and 21.76 g 100^-1 g^-1 for the same components (Ponce et al. 2021). The difference between these values and those obtained in the present work can be associated with the basic chemical treatment applied, which can change the composition of fibers through interaction with alkaline solutions (Andrade et al. 2023).

Table III presents the values of pH_PZC for SCB and RHW biosorbents. The pH_PZC is a physicochemical parameter that reflects the pH at which a surface is electrically neutral, with equal amounts of negative and positive charges. When pH<pH_PZC, the surface is electrically positive; when pH=pH_PZC the surface is electrically neutral; and when pH>pH_PZC is electrically negative (Komulski 1956).

Table III
Values of pH_KCl, pH_w, ΔpH and pH_PZC for SCB and RHW.

Biosorbent	pHKCl	pHw	ΔpH	pHPZC
SCB	5.13 ± 0.02	6.42 ± 0.03	-1.30 ± 0.04	3.83 ± 0.04
RHW	5.07 ± 0.04	6.64 ± 0.03	-1.57 ± 0.07	3.50 ± 0.05

The pH_PZC values are 3.83 ± 0.03 for SCB and 3.50 ± 0.05 for RHW at 25 °C, with similar behavior observed for both biosorbents. The negative ΔpH values presented by the biosorbents indicated their preference for accumulating negative charge (Batistela et al. 2017). The pH_PZC of the raw sugarcane bagasse biosorbent was reported in 3.40, which is close to that found here for SCB (Xavier et al. 2021). In another research, the pH_PZC found for rice husk was approximately 5.0, slightly higher than that found for RHW now (Costa Junior et al. 2018).

The pH_PZC of lignocellulosic biosorbents is a complex property influenced by several factors (Ren et al. 2024, Shahzadi et al. 2014). Low pH_PZC values are explained, for example, by the large number of acidic groups in biosorbents with high lignin contents, due to the presence of phenolic groups (Martínková et al. 2023). The crystallinity of cellulose can also affect the accessibility of functional groups for interaction with the solution (Jang et al. 2023).

Values of pH below pH_PZC indicate a positive charged surface, while pH values above pH_PZC correspond to a negative surface charge (Kosmulski 2021). Therefore, at pH lower than pH_PZC, biosorbents from AIW exhibit positive charges that favor the adsorption of anionic species (de Oliveira et al. 2019). A recent publication has indicated that pH 2 is the most efficient for TAR removal in many natural and synthetic adsorbents, including some lignocellulosic materials (Micheletti et al. 2023). In the present study, TAR adsorption experiments were conducted at pH 1.0, 2.0, and 3.0 to optimize the adsorption of this anionic dye.

Figure 3(a) and Figure 3(b) depict the ATR-FTIR spectra of SCB and RHW biosorbents, respectively. Comparative analysis of these spectra reveals a striking similarity in peak patterns, suggesting a shared composition primarily consisting of cellulose, hemicellulose, and lignin, as previously characterized in Table III (Bhatia & DilipKumar 2023). Both biosorbents exhibit prominent peaks within the 3350-3300 cm⁻¹ range, indicative of hydroxyl group (O-H) stretching vibrations characteristic of cellulose and hemicellulose (Peets et al. 2019). Additionally, peaks centered around 2900 cm⁻¹ are observed in both spectra, suggesting the presence of aliphatic C-H bonds associated with methyl and methylene groups (CH, CH₂, and CH₃) in the biosorbent (Sharma et al. 2020).

Figure 3
ATR-FTIR spectra of (a) SCB and (b) RHW biosorbents.

Additional spectral features include a prominent peak at 1735 cm⁻¹, characteristic of carbonyl (C=O) stretching vibrations associated with acetyl and ester groups in hemicelulose and lignin (Peets et al. 2019). The band at 1640 cm⁻¹ is attributed to C=O stretching vibrations of carboxylate groups (COO^-) present in glycosides (Abou-Hadid et al. 2024). A weaker band at approximately 1235 cm⁻¹ is indicative of C-O stretching vibrations of the acetyl group in lignin (Zhuang et al. 2020). Both biosorbents exhibit strong signals near 1030 cm⁻¹, suggesting the presence of carbon-oxygen (C-O) bonds typical of alcohols found in lignocellulosic materials (Hospodarova et al. 2018).These spectral findings corroborate the compositional data presented in Table III. The band at 790 cm^-1 for RHW can be attributed to the aromatic rings present in the lignin of the biosorbent (Blindheim & Ruwoldt 2023).

The XRD patterns of the SCB and RHW biosorbents are shown in Figure 4. Both biosorbents appear to be semi-crystalline based on the XRD curves. Peaks close to 2ɵ of 16.1° and 22.3° corresponding to the lattice planes 110 and 200, indicating the presence of crystallinity due to the fibrous components of cellulose (Bano & Negi 2017). Highly crystalline lignocellulosic materials generally exhibit greater stability and reduced reactivity compared to their amorphous counterparts (Xu et al. 2013).

Figure 4
X-ray diffraction of SCB and RHW biosorbents.

The XRD profile of the SCB and RHW biosorbents, as shown in Figure 4, exhibits a peak characteristic of the monoclinic structure of cellulose at 2ɵ=22.3° (Siti Syazwani et al. 2022). Following this peak, the profile shows a broad region without well-defined peaks, indicating an amorphous region. This amorphous area is attributed to the presence of lignin and hemicellulose, which facilitates the penetration of the dye into the surface of the biosorbent (Homagai et al. 2022).

The CrI of SCB was 30.7%, indicating a higher proportion of amorphous fractions and suggesting lower structural stability (Nam et al. 2024). In contrast, RHW exhibited a CrI of 42.2%, indicating a greater amount of crystalline structure, which may enhance its efficiency in dye adsorption due to its stability and organization. (Salem et al. 2023).

Box-Behnken Design

When applied in the context of Box-Behnken Design, the Pareto chart acts as a valuable tool for identifying and prioritizing the factors that exert the most significant influence on the response of a process or system. Figure 5 presents Pareto charts depicting the standardized effect estimates for TAR adsorption by (a) SCB and (b) RHW biosorbents. These charts are derived from a Box-Behnken design, which incorporates pH, concentration (C), and dosage (D) as independent variables. The charts analyze the linear (L), quadratic (Q), and interaction effects of these factors on TAR adsorption.

Figure 5
Pareto charts of the interactive effects of experimental factors pH, concentration (c), and dosage (d) on TAR adsorption by (a) SCB and (b) RHW, at 95% of confidence. L and Q represents linear and quadratic, respectively.

The pronounced effects of the linear and quadratic terms of pH, as depicted in Figure 5(a) and Figure 5(b), demonstrate its primary influence on the adsorption efficiency of both SCB and RHW. Based on the Pareto charts in Figure 5(a) and Figure 5(b), significant interaction effects were observed, suggesting that the mathematical model should incorporate these parameters to improve predictive accuracy. This implies that the effect of the variables on the response can vary depending on the level of another variable. In other words, the relationship between a variable and the response (i.e., TAR removal) is not simply additive but is influenced by interactions between variables. These interactions create additional effects that cannot be accounted for by considering the individual effects of each variable alone.

The quadratic effect of pH was the most influential factor for both biosorbents. For SCB, the effect C(L)*D(L) and D(Q) were statistically insignificant. However, for RHW, all parameters and their interactions exhibited significant effects at the 95% confidence level as shown by ANOVA (Table IV) performed in the Box-Behnken experimental design.

Table IV
ANOVA parameters for TAR adsorption by SCB and RHW (n=48).

Biosorbent	Source	Sum of squares	Degrees of freedom	Mean square	F-value	p -value
SCB	pH(L)	1330.12	1	1330.12	1478.41	<0.0001
	pH(Q)	10349.66	1	10349.66	11503.56	<0.0001
	C(L)	967.83	1	967.83	1075.73	<0.0001
	C(Q)	124.45	1	124.45	138.32	<0.0001
	D(L)	2861.90	1	2861.90	3180.98	<0.0001
	pH(L)*C(L)	29.37	1	29.37	32.65	<0.0001
	pH(L)*C(Q)	124.40	1	124.40	138.27	<0.0001
	pH(Q)*C(L)	401.38	1	401.38	446.13	<0.0001
	pH(L)*D(L)	65.97	1	65.97	73.32	<0.0001
	pH(Q)*D(L)	1279.47	1	1279.47	1422.12	<0.0001
	Error	35.19	37	2.75
	Total	16209.05	47
RHW	pH(L)	4760.51	1	4760.51	7933.48	<0.0001
	pH(Q)	16282.95	1	16282.95	27135.83	<0.0001
	C(L)	108.24	1	108.24	180.38	<0.0001
	C(Q)	305.85	1	305.85	509.70	<0.0001
	D(L)	441.74	1	441.74	736.16	<0.0001
	D(Q)	778.14	1	778.14	1296.78	<0.0001
	pH(L)*C(L)	10.26	1	10.26	17.10	0.0002
	pH(L)*C(Q)	19.23	1	19.23	32.05	<0.0001
	pH(Q)*C(L)	95.45	1	95.45	159.07	<0.0001
	pH(L)*D(L)	1063.00	1	1063.00	1771.51	<0.0001
	pH(Q)*D(L)	91.17	1	91.17	151.93	<0.0001
	C(L)*D(L)	1439.18	1	1439.18	2398.42	<0.0001
	Error	21.00	35	0.60
	Total	30933.83	47

• *p<0.05 indicates statistical significance. Conditions: [TAR]=10.0 mg L^-1; D=10.0 g L^-1; pH=2.0; V=10.0 mL; T=25 °C; agitation=90 rpm.

According to Table IV, the ANOVA analysis revealed that all regression models exhibited significant F-values. The high F-values suggest that the regression equations explain a substantial portion of the response variation. With a p-value of approximately 0.0001, which is less than 0.05, the model terms are statistically significant at a 95% confidence level. Any factor or interaction of factors with a p-value less than 0.05 is also considered significant.

Eqs. 16 and 17 present the regression coefficients from the polynomial model describing TAR removal by SCB and RHW, respectively. In these equations, the variables are presented in coded form.

$$\mathrm{\%}RE{M}_{SCB}=49.65(\pm 0.24)-10.53(\pm 0.27)\bullet pH-29.34(\pm 0.27)\bullet pH2-8.98(\pm 0.27)\bullet C-3.22(\pm 0.27)\bullet C2+15.44(\pm 0.27)\bullet D-1.56(\pm 0.27)\bullet pH\bullet C+8.17(\pm 0.39)\bullet pH2\bullet C+4.55(\pm 0.39)\bullet pH\bullet C2-2.34(\pm 0.27)\bullet pH\bullet D-14.60(\pm 0.38)\bullet p{H}^2\bullet D$$ (16)

$$\mathrm{\%}RE{M}_{RHW}=79.59(\pm 0.22)-19.91(\pm 0.22)\bullet pH-36.84(\pm 0.22)\bullet p{H}^2-3.00(\pm 0.22)\bullet C-5.05(\pm 0.22)\bullet C^2+6.06(\pm 0.22)\bullet D-8.05(\pm 0.22)\bullet D^2+0.92(\pm 0.22)\bullet pH\bullet C+3.99(\pm 0.32)\bullet p{H}^2\bullet C-1.79(\pm 0.31)\bullet pH\bullet C^2-9.41(\pm 0.22)\bullet pH\bullet D-3.99(\pm 0.32)\bullet p{H}^2\bullet D+10.95(\pm 0.22)\bullet pH\bullet D^2$$ (17)

In Eqs. 16 and 17, the higher value of the intercepts for RHW (79.59) indicates a greater intrinsic adsorption capacity of TAR compared to SCB (49.65). Therefore, it can be observed that RHW was qualitatively more efficient than SCB for TAR adsorption. In these mathematical models, negative coefficients imply that an increase in the variable decreases the adsorption of TAR by the biosorbent, and vice versa. In other words, there is an inverse relationship between the variable and adsorption efficiency. Analysis of the coefficients of the independent variables confirms the highest significance of pH², as evidenced by the Pareto charts (Figure 5).

Regression analysis was conducted to fit response functions to the experimental data. Figure 6(a) and Figure 6(c) demonstrate that the majority of data points are well-distributed around the straight line, indicating an excellent correlation between experimental and predicted response values. Both proposed models exhibit R² and adjusted-R² (R²_adj) values very close to unity. For SCB, R² = 0.9978 and R²_adj = 0.9872, while for RHW, R² = 0.9993, and R²_adj = 0.9991. Despite both R² and R²_adj indicate the proportion of the dependent variable’s variance explained by the model, R²_adj penalizes the inclusion of unnecessary variables, providing a more accurate assessment of the model’s quality (Sahu et al. 2018). In Table IV, the errors correspond to the sum of lack of fit and pure error, and for both biosorbents, they were not significant.

Figure 6
Plots of predicted versus actual TAR adsorption and histograms of standardized residuals for (a, b) SCB and (c, d) RHW, respectively.

To validate the model’s ability to accurately capture variations in responses, it is crucial to ensure that local residuals are not excessively large. Figure 6(b) and Figure 6(d) demonstrate that the distribution of residuals is centered around the mean and follows a normal distribution, which indicates that the residues of both models are reliable. Despite the slightly larger area under the curve for the SBC model residuals, both models demonstrate acceptable levels of residual distribution.

Figure 7 and Figure 8 depict response surface and contour plots that visualize the influence of pH, adsorbent dosage and dye concentration on TAR adsorption by SCB and RHW, respectively. The three-dimensional curves were generated by plotting the response (% removal) on the Z-axis against two input variables while maintaining the third variable at a fixed value of zero.

Figure 7
Response surfaces and contour plots for %TAR removal by SCB.

Figure 8
Response surfaces and contour plots for %TAR removal by RHW.

From Figure 7 and Figure 8, both biosorbents exhibited higher TAR adsorption at higher dosages and near the central pH. Both materials exhibited maximum adsorption at region of pH 2, suggesting that, at this pH, the protonation of adsorbent functional groups and the ionization of adsorbate molecules favor the formation of adsorbate-adsorbent complexes. Analysis of response surfaces revealed that while SCB exhibited a more complex adsorption behavior, while RHW displayed a more predictable behavior with a well-defined adsorption maximum. The concave downward curvature of the surfaces suggests that the effects cooperate synergistically for the increase in TAR adsorption efficiency. The response values obtained for RHW were higher than those obtained for SCB, indicating that RHW is a more effective adsorbent for TAR removal.

The optimal conditions for TAR adsorption by SCB and RHW were determined by the critical valued of mathematical models. According to the limits of the conducted experiment, the optimal values of pH, C, and D for TAR adsorption with SCB were 1.9, 6.2 mg L^-1, and 15.0 g L^-1, respectively. And, for RHW, these optimal values were 1.7, 9.9 mg L^-1, and 11.3 g L^-1, respectively. Under these conditions, the maximum TAR removal achieved were 60.1% for SCB and 83.1% for RHW. The results demonstrate a superior TAR removal capacity of RHW compared to SCB, indicating that RHW is a more effective adsorbent.

To analyze possible adsorption mechanisms, it is important to evaluate the chemical structures of the TAR and the components of the adsorbents (Yagub et al. 2014). As already mentioned, agro-industrial wastes contain large amounts of solid materials such as cellulose, hemicellulose and lignin, as can be seen in Table III. Although SCB and RHW are lignocellulosic adsorbents, the cellulose and hemicellulose contents represent around 60% of the mass composition of each material, therefore being their main constituents. The TAR dye presents three pKa values, at 2.0, 5.0, and 10.86, corresponding to the groups: -SO₃H, -COOH and azo group (Klett et al. 2014). Thus, at pH 2, which was commonly the pH found to be optimal for the adsorption process (Micheletti et al. 2023), it is estimated that the -COOH and azo groups are protonated and the -SO₃H group is 50% protonated and 50% deprotonated, as the pH is equal to the pKa of that group. In this aspect, the two -SO₃H groups can establish a large number of hydrogen bonds with cellulose/hemicellulose surface and dipole-dipole interactions with the cellulose/hemicellulose and lignin structures, therefore being the main interaction mechanisms of biosorbents with the dye. In more acidic media, there may be hydrolysis of cellulose bonds, damaging the adsorption process which explains the reduction in adsorption efficiency at pH lower than 2 (Sun et al. 2024).

Adsorption kinetics studies

The kinetic curves of TAR adsorption by SCB are shown in Figure 9 (a) and RHW in Figure 9 (b). It can be observed that the adsorption of TAR by SCB reached equilibrium in 30 min, while equilibrium occurred in 60 min with RHW. Therefore, the adsorption by SCB is faster than with RHW. The parameters calculated by the adsorption kinetic models are presented in Table V.

Figure 9
Graphs of the PFO and PSO kinetic models applied to the study of the kinetics of TAR adsorption by (a) SCB and (b) RHW biosorbents. Conditions: [TAR]: 10.0 mg L^-1; D: 10.0 g L^-1; pH: 2.0; V: 10.0 mL; T: 25 °C; agitation= 90 rpm.

Table V
Parameters calculated for the PFO and PSO kinetic models for the adsorption of TAR by SCB and RHW biosorbents.

Kinetic model	Parameters	SCB	RHW
PFO	qe (mg g-1)	1.03 ± 0.01	2.00 ± 0.01
	k1 (min-1)	0.20 ± 0.01	0.18 ± 0.01
	R2	0.9879	0.9924
	RSS	0.0110	0.0264
	χ2	0.0011	0.0026
PSO	qe (mg g-1)	1.07 ± 0.01	2.08 ± 0.02
	k2 (g mg-1 min-1)	0.37 ± 0.03	0.17 ± 0.01
	R2	0.9964	0.9960
	RSS	0.0033	0.0137
	χ2	0.0003	0.0013

• Conditions: [TAR]: 10.0 mg L^-1; D: 10.0 g L^-1; pH: 2.0; V: 10.0 mL; T: 25 °C; agitation= 90 rpm.

Based on the parameters of highest R², lowest RSS and lowest χ², in Table V, the PSO model was identified as the best fit for SCB and RHW biosorbents. This model is based on the capacity of adsorption in the solid phase and reports the process behavior over the entire contact time range (Largitte & Pasquier 2016). The kinetic parameters of the PSO model adjustment for SCB indicated a PSO adsorption rate constant (k₂) value of 0.37 ± 0.03 g mg^-1 min^-1 for SCB and 0.17 ± 0.01 g mg^-1 min^-1 for RHW, indicating that adsorption by SCB was faster, despite RHW achieving the highest adsorption capacity.

Adsorption isotherms

Graphs of q_e versus TAR concentration (Ce) at the equilibrium time (120 min) are shown in Figure 10 (a) for SCB and in Figure 10 (b) for RHW. The five isotherms models: Langmuir, Freundlich, Temkin, Brouers-Sotolongo, and Sips were fitted to the data. The calculated parameters for the best-fit isotherm models are presented in Table VI.

Figure 10
Isotherm model fits for TAR adsorption by (a) SCB and (b) RHW. Conditions: D: 10.0 g L^-1; pH: 2.0; V: 10.0 mL; t: 120 min; T: 25 °C; agitation: 90 rpm.

Table VI
Calculated parameters for isotherm models of TAR adsorption by SCB and RHW.

Model	Parameters	SCB	RHW
Langmuir	qmax (mg g-1)	2.96 ± 0.06	3.95 ± 0.08
	KL (L mg-1)	0.05 ± 0.01	0.14 ± 0.02
	R2	0.9956	0.9904
	RSS	0.0232	0.1048
	χ2	0.0025	0.0116
Freundlich	KF (mg-1/n L1/n g-1)	0.52 ± 0.08	1.59 ± 0.22
	n	2.89 ± 0.33	5.25 ± 0.98
	R2	0.9676	0.9542
	RSS	0.1712	0.5023
	χ2	0.0190	0.5582
Temkin	KT (L g-1)	0.48 ± 0.08	4.50 ± 3.27
	bT (J mol-1)	3764.33 ± 206.36	3979.47 ± 548.95
	R2	0.9887	0.9664
	RSS	0.0595	0.3687
	χ2	0.0066	0.0409
Sips	qmax (mg g-1)	2.80 ± 0.06	3.86 ± 0.04
	Ks (L mg-1)	0.16 ± 0.09	17.34 ± 1.03
	ms	2.31 ± 1.03	94.78 ± 0.01
	R2	0.9976	0.9954
	RSS	0.0115	0.0440
	χ2	0.0014	0.0055
Brouers-Sotolongo	qmax (mg g-1)	2.45 ± 0.03	3.55 ± 0.02
	αBS	0.93 ± 0.04	0.96 ± 0.04
	KBS (L mg-1)	0.06 ± 0.01	0.10 ± 0.01
	R2	0.9980	0.9987
	RSS	0.0092	0.0125
	χ2	0.0011	0.0015

• Conditions: D: 10.0 g L^-1; pH: 2.0; V: 10.0 mL; t: 120 min; T: 25 °C; agitation: 90 rpm.

The best models for adsorption isotherms were those that presented higher R², values, lower RSS values and lower χ² values. For SCB, the Brouers-Sotolongo isotherm provided the best fit, with a q_max value of 2.45 ± 0.03 mg g^-1. The adjustment parameters are detailed in Table VI, where the R², RSS and χ² values were 0.9980, 0.0092 and 0.0011, respectively, indicating the best fit of the coefficients to the obtained data. Table VI also shows that Brouers-Sotolongo isotherm provided the best fit for the RHW adsorption data, with R², RSS and χ² values of 0.9987, 0.0125 and 0.0015, respectively. The q_max value found was 3.55 ± 0.02 mg g^-1, which is 44.8% higher than that of SCB. The Brouers-Sotolongo isotherm model, proposed in 2005, builds upon some of the theoretical assumptions of the Langmuir isotherm (Brouers et al. 2005). The model assumes that the adsorbent surface is heterogeneous, with different types of adsorption sites, each with its own adsorption energy. This isotherm model posits that the adsorbent exhibits a distribution of adsorption energies that adheres to Lévy’s theory of stable distributions (Majd et al. 2022).

Some adsorbents from AIW have been studied for the adsorption of TAR. For peach palm and cassava waste, both in natura, used to treat food industrial effluents containing TAR at pH 2.0, the q_max values were 2.52 mg g^-1 and 1.56 mg g^-1, respectively (Dos Santos et al. 2021). The Langmuir isotherm was the most appropriate model for the adsorption data of peach palm waste, whereas the Freundlich isotherm was more suitable for cassava waste. Characterization of these biosorbents revealed that cellulose was the primary component and that both materials had porous surfaces.

Sawdust waste from a local wood factory in Allahabad, India, was also used for TAR adsorption (Banerjee & Chattopadhyaya 2017). The characterization of this material revealed the presence of lignocellulosic fibers. The q_max was 4.71 mg g^-1, and the data were best fitted to the Dubinin-Radushkevich isotherm. The study demonstrated that adsorption equilibrium was reached after 70 min, at pH 3.0. The reaction kinetics were best described by the PSO model.

It is also common to encounter studies that employ chemical or thermal treatments to modify the structure of plant-derived residues in order to enhance their adsorption capacities. For example, recent studies have demonstrated the adsorption of TAR using sweet potato residue-derived activated carbon with q_max of 298.17 mg g^-1 (Diao et al. 2024), lanthanum chloride enriched aminosilane-grafted mesoporous carbon with q_max of 210.31 mg g^-1 (Goscianska & Ciesielczyk 2019), activated carbon nanosorbent from Delonix regia with q_max of 147.06 mg g^-1 (Joshiba et al. 2022), orange peels with H₂SO₄ chemical activation with q_max of 122.25 mg g^-1 (Honorine et al. 2023), and hydrochars from Pinus caribaea with NaOH chemical activation with q_max of 23.01 mg g^-1 (Andrade et al. 2023).

The complex physicochemical processes involved in adsorption necessitate an evaluation of adsorbent reusability to mitigate the potential environmental and economic burdens associated with their production and disposal (Gkika et al. 2022, Saha et al. 2024). Adsorbent reuse offers a promising strategy to minimize the generation of harmful chemical residues and reduce overall process costs, particularly considering the often-high cost of adsorbent materials (El Messaoudi et al. 2022, Das & Debnath 2023). Although SCB and RHW are derived from low-cost AIW, their reusability may be limited by factors such as regeneration costs, effluent generation, and the need for specific treatment conditions to maintain adsorbent integrity. Given the nature of AIW as biosorbents, the reusability of SCB and RHW was not deemed practical for TAR adsorption applications.

Adsorption thermodynamics

The Brouers-Sotolongo model was selected to fit isotherms for the TAR biosorption onto SCB and RHW based on the R², χ², and RSS values, at the three different temperatures. From Eq. 13, the dimensionless thermodynamic equilibrium constants Kº were calculated from the adsorption equilibrium constants of the Brouers-Sotolongo isotherms (K_BS). The Van’t Hoff plots are presented in Figure 11(a) and Figure 11(b) for SCB and RHW, respectively, and the thermodynamic parameters are in Table VII.

Figure 11
Van’t Hoff plots for TAR adsorption by (a) SCB and (b) RHW. Conditions: D: 10.0 g L^-1; pH: 2.0; V: 10 mL; t: 120 min; agitation: 180 rpm; T: 30 °C, 40 °C and 50 °C.

Table VII
Thermodynamic parameters of TAR adsorption by SCB and RHW based on equilibrium constants provided by Brouers-Sotolongo model.

Biosorbent	T (K)	K BS (10 -2 L mg -1 )	K º (dimensionless)	ΔG° (kJ mol-1)	ΔH° (kJ mol -1 )	ΔS° (J mol -1 K -1 )	R 2
SCB	303.15	12.96	69229	-28.09	-50.22	-72.59	0.9575
	313.15	8.01	42808	-27.76
	323.15	3.76	20100	-26.62
RHW	303.15	2.00	10680	-23.37	-10.39	42.91	0.9762
	313.15	1.79	9590	-23.87
	323.15	1.55	8270	-24.23

• Conditions: D: 10.0 g L^-1; pH: 2.0; V: 10.0 mL; t: 120 min; agitation: 180 rpm; T: 30 °C, 40 °C and 50 °C.

The ΔG° were negative for both biosorbents, indicating that the TAR adsorption process was spontaneous. For SCB, the increase in temperature led to less negative ΔG° values (endergonic adsorption), indicating a reduction in the spontaneity of the adsorption process. This behavior is similar to that observed in a study involving the adsorption of TAR onto activated carbon derived from boiler waste in sugarcane processing plants that were chemically treated with NaOH (Joshiba et al. 2022). On the other hand, for RHW, the ΔG° results became more negative with the increase in temperature, which indicates that the spontaneity of the adsorption process becomes favorable at high temperatures (exergonic adsorption).

The values of ΔH° were -50.22 kJ mol^-1 and -10.39 kJ mol^-1 for the SCB and RHW, respectively, and indicate that both adsorption processes were both exothermic. The value of ΔS° for SCB is -72.59 J mol^-1 K^-1 (Table VII) and indicates a decrease in disorder in the interaction on the surface of the biosorbent during the process of adsorption (Martini et al. 2018, Joshiba et al. 2022). Moreover, the ΔS° value of +42.91 J mol^-1 K^-1 (Table VII) of RHW indicates an increase in disorder at the solid-liquid interface during the TAR removal process (Habila et al. 2014, Mahmoud et al. 2020, Dawodu & Akpomie 2016). The comparison between the values of ΔH° and T ΔS° indicates that both adsorption processes are enthalpy-driven.

CONCLUSIONS

This research demonstrated the valorization of AIW, given its abundant availability in Brazil, by applying it to the sustainable adsorption process of TAR. It was observed that the pH emerged as the most critical parameter in the experimental design, with a pH of approximately 2 identified as the most favorable condition for TAR adsorption by SCB and RHW. For both adsorbents, the best kinetic model and isotherm for TAR adsorption were found to be the PSO and Brouers-Sotolongo models, respectively. The thermodynamic parameters indicated that the adsorption processes of TAR were spontaneous and exothermic for both adsorbents. It was noted that RHW is a more effective adsorbent than SCB for the removal of the anionic dye from aqueous solutions. However, considering that the production of SCB in Brazil is significantly higher than that of RHW and has potential for sustainable and economic utilization, its use as a biosorbent is also justified.

In the context of using AIW in large quantities for the treatment of industrial wastewater, the adsorption capacity of these biosorbents becomes significant, as their application promotes the efficient removal of pollutants, such as dyes, and provides a solution for the management of AIW, thereby contributing to the circular economy and mitigating environmental impacts. This study focused on using SCB and RHW without physical-chemical modifications, employing them in the TAR adsorption process. The approach was designed to preserve the original characteristics of the waste materials and minimize environmental impacts, avoiding energy-intensive treatments and the use of aggressive chemicals.

Thus, considering the feasibility of reducing pollutant loads in effluents and the valorization of waste that would otherwise be discarded, it is imperative to establish public policies that encourage research and the implementation of AIW valorization technologies. Such initiatives can significantly contribute to the development of innovative solutions that address the challenges of wastewater treatment, promoting sustainable management of water resources and environmental protection. The use of AIW as biosorbents in aqueous media implies a contribution to investigations regarding the optimization of pollutant removal processes under real operating conditions, such as those from pharmaceutical, textile, and food industries, taking into account engineering aspects and economic feasibility.

ACKNOWLEDGMENTS

The authors would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Araucária State of Paraná Research Foundation (Fundação Araucária - FA) and PIBIC (UEM/CNPq/FA), all of which are based in Brazil. The authors declare that there are no competing interests to disclose. Data are available on request.

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Publication Dates

• Publication in this collection
  09 Dec 2024
• Date of issue
  2024

History

• Received
  28 Nov 2023
• Accepted
  12 Oct 2024