Valorization of spent coffee grounds through pyrolysis as adsorbent for the removal of Vivizole Red 3BS dye from aqueous solution

As compared to other organic pollutants in wastewater, dyes are the least biodegradable organic pollutants and the textile industry is the major contributor to the contamination of the aquatic environment with dyes. Research findings have shown that dyes are carcinogenic compounds that have detrimental effects on the health of aquatic life and mammalian population (Núñez et al. 2019). Most of the dyes are not degraded during the conventional wastewater treatment processes, especially those that are classified as reactive, direct, basic, and acidic, since they have high solubility in water (Hassan & Carr 2018) and also they contain complex aromatic molecular structures (Zhang et al. 2016).

Diverse technologies are available to treat textile wastewater, and among them adsorption is effective to remove dyes from textile wastewater using activated carbon. Commercially available activated carbon is very effective to remove dyes; however, its high price limits its use, especially in developing countries for treatment of textile wastewater (Topare & Bokil 2020). Currently, researchers are interested in developing new adsorbent materials with low cost, various compositions, and selective functionalities (Danish et al. 2018), and hence, the need to develop low-cost adsorbent from agricultural by-products are increasing in demand (Pagalan et al. 2020).

Coffee is regarded as one of the most popular beverages throughout the world, and its consumption is constantly increasing. According to the International Coffee Organization (ICO), global coffee consumption was about 166.346 million 60 kg bags in 2020/21 and in 2019/20 164.202 million 60 kg bags. The trend shows an increase by about 1.3% (ICO 2021). It has been estimated that over 3.5 billion cups of coffee are served each day at the world level (Caetano et al. 2017), and more than 50 countries are involved in the commercial production of coffee (Rajesh Banu et al. 2020). During the processing of coffee, large amounts of solid residues are generated in the form of coffee pulp, coffee silver skin, spent coffee grounds (SCG), and coffee husk. SCG is an insoluble residue or waste product generated after milling and brewing during coffee beverage production (Rajesh Banu et al. 2020). On average, about 650 kg of SCG is generated from one tonne of green coffee beans, and approximately 2 kg of wet SCG is obtained during preparation of 1 kg of soluble coffee (Murthy & Naidu 2012). Therefore, globally, the amount of SCG generated from green coffee beans is approximately 6.5 million tonnes.

SCG causes problems during disposal at the landfill site as it has high oxygen demand during decomposition and contains toxic substances such as tannins, polyphenols, and caffeine, so disposal into the environment causes significant contamination (Rajesh Banu et al. 2020). It has been estimated that during the decomposition of one tonne of SCG, about 340 m³ of methane gas is emitted to the environment (Ferreira and Ferreira 2019). Moreover, SCG are rich in organic components such as polysaccharides, oligosaccharides, lipids, lignin, proteins, alkaloids, amino acids, aliphatic acids, melanoidins, trigonelline, and volatile compounds. Thus direct disposal of SCG in landfills can create ecotoxicological concerns and environmental problems (Atabani et al. 2021).

Waste valorization is perceived as the process of transformation of wastes or agricultural by-products into value-added products by making use of processing technologies such as acid/alkaline hydrolysis, pyrolysis, fermentation, and anaerobic digestion (Lin et al. 2020). Therefore, one of the benefits of valorization is to implement efficient waste management as a fundamental solution for sustainable development. Pyrolysis can be described as a thermochemical process in which biomass is thermally decomposed to form its chemical constituents under inert or very limited oxygen supply. As compared to combustion, it is more efficient and results in less pollution (Tripathi et al. 2016).

Research findings have shown that chlorides of transition metals such as FeCl₃, ZnCl₂, etc. can modify the thermo-chemical behavior of certain hydrocarbon compounds through a coordination effect and result in the formation of crosslinking and carbonization of biomass (Sun et al. 2013). The use of FeCl₃ during carbonization of biomass significantly increases the degradation of both cellulose and hemicellulose, and its catalytic activity was higher than the chlorides of alkali metals and alkaline earth metals (Liu et al. 2009). Iron(III) salt enhanced the formation of aromatic structure during biomass carbonization process and hence provided adsorption sites for both polar and nonpolar organic contaminants (Han et al. 2021).

In this study, we developed an adsorbent prepared via one-step catalytic carbonization of spent coffee ground where iron chloride (FeCl₃) was used as a catalyst under limited air supply. The purpose of this study was management of SCG waste by valorization of SCG through pyrolysis, and the biochar obtained was used as adsorbent for the removal of textile dye (Vivizole Red 3 BS). The synthesized biochar composite adsorbent was characterized using different techniques to propose the removal mechanism of the dye, and the study would optimize the dye removal by making use of varied combination of the process parameters. To the best of our knowledge, the use of SCG biochar to remove Vivizole Red 3BS has not been tested before as such to offer both the management of the SCG waste and the textile wastewater treatment. Moreover, isotherm, kinetics, and thermodynamic studies were conducted for the adsorption of the dye by SCG biochar.

The molecular structure of Vivizole Red 3BS dye is shown in Figure 1. The stock solution of the dye was prepared (1,000 mg/L) by dissolving the dye in deionized water. The concentrations used in each run were obtained by dilution of the stock solution prepared.

SCG was obtained from ROBERA PLC, a coffee and roasted coffee processing company (Addis Ababa). With some modification, the procedure for one-step activation and synthesis of biochar was adopted (Wen et al. 2019), where iron (III) chloride serves as a catalyst for the carbonization of SCG and also result in the formation of magnetic biochar. The collected SCG was pre-treated with hot water, washed thoroughly with distilled water to remove impurities such as dust and water-soluble substances from the surface of SCG, and dried at 105 °C for 24 hours. The dried fraction was pulverized and 10 g of SCG was added into 40 ml deionized water and 2 g of FeCl₃ was added and then soaked for about 30 min, followed by magnetic stirring at 80–85 °C for 120 min to vaporize water. The solid was dried at 70 °C in vacuum oven to get a constant mass carbon composite ready for the process of pyrolysis. The process of carbonization was carried out in a carbolite furnace (ELF 11/14B, UK). Samples were carbonized at the temperature of 750 °C under limited air at a heating rate of 10 °C min⁻¹ and withholding time of 60 min. The sample was taken out of the furnace and cooled to room temperature. Without the addition of FeCl₃ salt, SCG was activated by following the above procedure and referred to as SCG0. The biochar samples were washed with deionized water several times and stored in drying oven at 30 °C and ready for further use.

To identify functionalities present within the SCG biochar, Fourier transform infrared (FTIR) (Spectrum 65, PerkinElmer) spectra were used. The total surface area of SCG biochar was determined by the Brunauer-Emmett-Teller (BET) method. Powder X-ray diffraction (Rigaku MiniFlex 600 Benchtop) was used to determine crystalline structure of the biochar sample. The pH at point of zero charge (⁠⁠) of the prepared adsorbents was measured using the drift method (Tran et al. 2016).

The effects of the most influencing factors were analyzed by making use of analysis of variance (ANOVA) to determine the possible combinations of the factors that would give the best model for the adsorption of the dye by SCG biochar. The best result of numerical optimization was validated through experimental runs in the laboratory. In the analysis, the significant factors (p ≤ 0) affecting the adsorption of the dye by SCG biochar were determined and the most fitted model was given as surface quadratic equation. The effects of the significant factors were analyzed and presented through contour plots and 3D models generated by Design Expert 11.0 software.

Batch adsorption experiments were carried out by shaking a known amount of adsorbent in 50 mL of dye solution with different concentration in a 100 mL conical flask. At a predetermined adsorption time, each dye sample was withdrawn from the adsorption solution and centrifuged for 20 min at 4,000 rpm to separate the used adsorbent from the dye solution, and the amount of each dye in the supernatant was determined by measuring the absorbance at the ⁠. using UV/Vis spectrophotometer (1,600 Series Single Beam) (⁠for Vivizole Red 3BS is 540 nm).

Kinetic studies help to predict the adsorption mechanism and to determine the potential steps which control the adsorption rate, such as chemical reaction and mass transport processes (Anastopoulos et al. 2018). Several kinetic models are available; however, pseudo-first- and pseudo-second-order kinetic equations are the most commonly used in adsorption studies (Awad et al. 2020). Pseudo-first- and second-order models were applied for this particular study. Moreover, to find out whether the process of adsorption is controlled by diffusion or not, an intraparticle diffusion kinetic model (Benjelloun et al. 2021) was also used.

The FTIR spectra analyses can provide some valuable information about identification of chemical species of the SCG biochar. Functional groups on the surface of biochar can influence the adsorption performance of the biochar formed with and without addition of FeCl₃. Even without the addition of iron(III) salt, increasing pyrolysis temperature would decrease hydrogen content (H%); however, the carbon content (C%) of biochar increases with increasing pyrolysis temperature (Xiao et al. 2018). Therefore, the addition of iron(III) salt significantly decreases the H/C ratio and it has been suggested that iron(III) facilitates the formation of aromatic structure. The presence of Fe-O bond vibration of Fe₃O₄/Fe₂O₃ can be corroborated by FTIR analysis; however, the characteristic peaks of Fe-O bond depend of the sources of biochar and its preparation methods (Shin et al. 2021; Zeng & Kan 2021). Generally, as observed in Figure 2, the presence of metal oxides such as Fe-O and Fe-O-Fe on the surface of biochar is indicated in the ranges of 730–500 cm⁻¹ (Liang et al. 2021). Whereas in the case of pristine SCG biochar (Figure 2) no visible peak is observed in the aforementioned range.

The broad band between 3,500 cm⁻¹ and 3,300 cm⁻¹ represents O-H stretching vibration of hydroxyl groups of alcohols (Peng et al. 2018). However, as depicted in Figure 2, the O-H stretching vibration band was very low and this suggests that oxygen was removed significantly during carbonization process and phenolic-aromatic structures were converted to form carbon materials. Moreover, the process of activation and thermal treatment promoted the conversion to graphitic structure (Tomin et al. 2021). The C = C band of aromatic rings appeared in the range between 1,650 and 1,470 cm⁻¹ (Shin et al. 2021). There is also a peak at 879 cm⁻¹, which confirmed the out-of-plane bending of the = C-H in the aromatic ring (Yao et al. 2020). The peak at 1,150–1,050 cm⁻¹ shows the C – O stretching of ethers (Shin et al. 2021) and the characteristic vibration band around 1,000 cm⁻¹ is assigned to C – O – C (Liang et al. 2021).

The XRD technique is used to indicate the structure of crystal and phase composition of the materials. The existence of Fe-O bond is further indicated by XRD (S 1). Two strong peaks at around 30.16° and 35.52° were assigned to Fe₃O₄ and which are related to the (220) and (311) planes, respectively (Dong et al. 2017).

The point of zero charge pH (pH_pzc) can be described as the pH value at which the surface charge of the adsorbent is zero and it shows the comprehensive effects of the different functional groups on the biochar surface.⁷³ The pH_pzc of biochar prepared without addition of iron(III) salt was 8.1, whereas pH_pzc of biochar obtained from the addition of iron(III) salt was 3. The yield of SCG-Fe biochar was 20%, whereas the biochar obtained from pristine SCG was 22%. Yield reduction for SCG-Fe biochar is due to addition of iron(III) salt (Han et al. 2021) since it facilitates the process of graphitization (Tomin et al. 2021). The BET area of pristine SCG biochar was 128 m²/g, whereas the BET value of SCG-Fe biochar was 89 m²/g. Reduction in BET value could be attributed to the closing or blockage of pores by metal oxide on the surface of biochar (Nguyen et al. 2021).

The second-order polynomial regression equation generated by CCD was validated by considering results in actual runs, as shown in S2. The ANOVA was used to determine the reliability of the model developed (Table 3). The model F-value of 76.06 implies that the model is significant and there is only a 0.01% chance that an F-value this large could occur due to noise. The result of ANOVA showed that the following parameters and an interaction effect were significant model terms: A, C, AC, A², and C², and all parameters and their interaction effects were not significant. According to the F-value (Table 3), two parameters had a significant effect on the removal of the dye. The F-value of dose (C) and initial dye concentration (A) were higher at about 517 and 482, respectively, and this implied the two had most significant influence on the percent of dye removal (Bayuo et al. 2020).

According to the second-order polynomial model, when the value of the initial dye concentration (A) increased, the percent dye removal decreases. The percent removal of the dye decreases with increase of initial dye concentration if the active sites on the surface of the biochar are saturated (Yu et al. 2021). The interaction between initial dye concentration and dose (C) and the square of dose have negative coefficients in the range tested for the removal of VR 3BS dye and have negative effect on the removal of dyes. However, the dose alone and the squared value of initial dye concentration favored the percent removal of the dye. The lack of fit p-value of the model is insignificant, and the value of regression coefficient (R²) was high (0.986). Moreover, the values of adj R² and pred R² were well within 20% of each other (Table 2) and provide 94.92% of variability to predict new observations as compared to approximately 97.31% variability in the original data (Rai et al. 2016). Based on the result of the ANOVA, the F-value for the model was significant, and there was a good correlation between the response and the independent variables of the model and also the sum of squares (SS) values of the variables were high (Table 3), implying the importance of the variables (Jaafari et al. 2020). Adequate precision is used to measure the signal to noise ratio and compares the ranges of predicted values of the design point in comparison to the average predicted error. A ratio greater than 4 is desirable, and for this particular work the ratio is 31.392 indicating an adequate signal, and this model can be used to navigate the design space. The coefficient of variation (CV = 6.78) and standard deviation (SD = 3.78) reflect the degree of precision of the measurements. The low values of SD and CV show that the measurements conducted are adequate (Mourabet et al. 2017), thus the model was efficient in predicting the percent removal of VR 3BS dye, so the model can justify 98.6% of data variation (Pagalan et al. 2020).

The ANOVA result showed that among the available interactive effects; only the initial dye concentration (A) and adsorbent dose (C) have significant interactive effects on the percent removal of VR 3BS dye with SCG biochar. Therefore, the interactive effect of AC was the one only described (Figure 3).

According to the 3D response surface plot indicated in Figure 3(a) and 2D contour shown in Figure 3(b) for the interaction of dose of adsorbent and initial dye concentration, low dose at higher dye concentration resulted in the decreased percent removal of VR 3BS dye. At lower initial dye concentration, the percent removal of the dye increased when dose of adsorbent was higher. This was mainly because as the ratio of the number of dye molecules to the available active sites on the adsorbent is low, there is a greater possibility of interaction between the molecules of the dye and available active sites on the biochar (Gebreslassie 2020). As the ratio of a number of dye molecules to the number of surface-active sites on the biochar increases, the active sites on the biochar saturated, and the percentage removal of the dye decreases.

Graphs can be used to validate CCD model by evaluating correlation between actual and predicted values and the nature of residual distribution. As indicated in S3 (a), in the relationship between actual and predicted values, there were minimum divergence of points from the straight diagonal line (Jawad et al. 2020). As shown in S3 (b), the normal probability of residuals values pattern also follows along the straight line and thus indicating ideal distribution and independence of residuals (Sharifpour et al. 2020). In S3(c), all the experimental data points were within the residual limits (±4) (Ghoreishian et al. 2019), and indicating that the proposed model was adequate and satisfied the constant variance assumption. Therefore, the RSM-CCD model equation can be used to sufficiently describe the interaction of the independent variables under study.

This study employed input variables with specific ranged values whereas the response, i.e., percent removal of VR 3BS dye, was assigned to achieve maximum value. The suggested optimum values of CCD were 10 ppm initial concentration of VR3BS dye, 1 g per 100 ml adsorbent dose, and contact time of 101 min with optimum predicted dye removal of 99%. Validation of experimental runs was carried out using the optimum conditions in triplicate and the average value of percent removal of VR3BS was 97.2% (Table 4). However, the removal efficiency of biochar obtained from pristine SCG is only 60%.

Equilibrium isotherm studies can be used to get information about the distribution of adsorbate molecules at the solid/liquid interface. In this study, the equilibrium data of VR3BS dye adsorption with the optimized SCG biochar were fitted to Langmuir, Freundlich, Temkin and Redlich–Peterson models, and the resulting isotherm parameters values calculated from the fitting are shown in Table 5. The Langmuir isotherm was the most fitted isotherm model with the adjusted R² value of 0.986 (Figure 4(a)) and followed by Toth isotherm Redlich – Peterson isotherm and then Freundlich isotherm. The adsorption of VR3BS dye follows the Langmuir isotherm model. Adsorption of dyes by both physically and chemically activated SCG also followed the Langmuir isotherm model (Lim et al. 2016; Wirawan et al. 2020). Since the Langmuir isotherm model assumes a monolayer type of adsorption on homogeneous surfaces, there are limited number of adsorption sites (Rangabhashiyam et al. 2014). Therefore, the adsorption capacity of SCG biochar activated with iron(III) salt was 2.07 mg/g.

The thermodynamic parameters of the adsorption process of VR3BS dye by SCG biochar activated with iron(III) salt were investigated at three different temperature values and the calculated values of ⁠, and are listed in Table 6.

The obtained values for Gibbs free energy change (⁠⁠) are negative, which indicate that the adsorption process is spontaneous and thermodynamically favored (Theydan & Ahmed 2012). The strength of the adsorption process increases with temperature (Ma et al. 2021a). The calculated enthalpy and entropy values were both positive and confirmed that the process of adsorption of VR3BS dye by the SCG biochar composite is endothermic (Liu et al. 2019). Based on the ranges of free energy (⁠⁠) values, adsorption process can be categorized as physisorption and chemisorption, and the range for physisorption is between −20 and 0 kJ/mole, whereas for chemisorption it is between - 80 and - 400 kJ/mol (Abdelwahab & Amin 2013). Therefore, the value of for the adsorption of VR3BS dye by SCG biochar was in the range of physisorption. However, the enthalpy of adsorption for physisorption is the range of 2.1–20.9 KJ/mol, whereas for chemisorption it falls within the range of 80–200 KJ/mol (Nnadozie & Ajibade 2021). The enthalpy of adsorption of VR3BS dye on SCG biochar composite was 169.21 KJ/mol and it has been suggested that the adsorption process was neither purely physisorption nor chemisorption. The values of indicates that there is randomness on the solid-solution interface and this implies that the molecular structure of adsorbate and the adsorbent surface had changed to some extent during the process of adsorption (Ma et al. 2021b). Therefore, thermodynamic study revealed that the adsorption process was spontaneous, favourable, endothermic and physicochemisorption in nature.

Kinetic study provides a better insight to elucidate the process of adsorption phenomena. To determine the potential steps which control the adsorption rate and type, the fitting of the process to pseudo-first, pseudo-second and intraparticle diffusion kinetic models were evaluated (Benjelloun et al. 2021; Grisales-Cifuentes et al. 2021). Optimum operating conditions with contact times of 30, 55, 80, 105, and 155 min were used. The parameters of kinetic data analysis for the adsorption of VR3BS dye on SCG composite biochar are shown in Table 7 and the plots are given in Figure 5(a)–5(c).

Adsorption kinetics was better described by the pseudo-second-order model, and it was suggested that the rate of adsorbate–adsorbent interaction is dependent on both the amount of dye adsorbed at the biochar surface and the adsorbed amount at equilibrium (Grisales-Cifuentes et al. 2021).

Intraparticle diffusion model was used to describe the nature of adsorption mechanisms influencing the adsorption of VR3BS dye onto the biochar. In Figure 5(c), the regression line is not linear from the origin and thus intra-particle diffusion is not the only rate limiting step (Yao et al. 2020). As indicated in Figure 5(d), two distinct linear segments with different slopes were observed. The first phase of adsorption, i.e., from t = 0 to 105 min, is faster and corresponds to liquid film diffusion and the second phase, from t ≥ 105 min, is controlled by intra-particle diffusion and is delayed phase. It was pointed out that the diffusion resistance across the liquid boundary layer is smaller than the resistance during the pore diffusion phase (Sumalinog et al. 2018).

Determination of the adsorption mechanism is very important in controlling the adsorption process. However, this is not an easy task since the adsorption mechanism is a complicated process governed by many factors such as the porosity, surface area, surface charge, pH, functional groups, carbon and aromatic content, and mineral composition of the biochar (Liu et al. 2018; Doan et al. 2021). Varieties of mechanisms were proposed for the adsorption of organic pollutants onto biochar. Pore-filling, hydrophobic, and electrostatic interactions and hydrogen bonding are considered the major mechanisms that are responsible for the adsorption between organic contaminants and biochar. Iron(III) employed as a catalyst during the carbonization of SCG has good graphitization efficiency and the onset of the graphitization by Fe takes place starting from 715 °C (Gomez-martin et al. 2021). Therefore, the aromatic structure in graphite provides adsorption sites for both polar and nonpolar organic contaminants. Among others, the possible adsorption mechanism is π – π electron donor–acceptor (EDA) interactions between the dye molecules and the aromatic group in the graphite structure of biochar (Wen et al. 2019; Liu et al. 2021). The results of FTIR analysis show that the surface of the biochar contains functional groups such as – OH, – CO, and – CH, which are responsible for the adsorption of VR3BS dye through π – π interaction and hydrogen bonding, etc. Since the addition of iron(III) enhanced the formation of the aromatic structure of biochar (Han et al. 2021), the dye with an aromatic structure could be adsorbed onto the biochar through π – π EDA interactions, and hydrogen bonding is formed between the N atoms on the dye molecule and the oxygen-containing functional groups of the biochar (Yao et al. 2020). The intraparticle diffusion model confirms that diffusion into adsorbent pores is not the only mechanism governing the adsorption process. Moreover, the results from the thermodynamic study suggest that the adsorption of VR3BS dye by SCG–Fe biochar involves both physisorption and chemisorption mechanisms.

Coffee consumption is increasing throughout the world, and a significant amount of solid residue generated in the form of SCG is disposed of in landfill sites and thus responsible for the environmental problems. Valorization of SCG through pyrolysis could be the solution to this challenge and the biochar obtained was used as an adsorbent for the removal of the textile dye–Vivizole Red 3 BS. Iron(III) salt was used as a catalyst during carbonization of SCG biomass and has a good graphitization efficiency and thus enhanced the formation of aromatic structures which provide adsorption sites for the dye. The yield of biochar was 20% when the SCG was pyrolyzed at 750 °C. A predictive model for the removal of the dye was investigated with RSM–CCD and the result of ANOVA showed that the two parameters, namely the dose of adsorbent and initial dye concentration had a significant effect on the removal of the dye. The suggested optimum values of the CCD were 10 ppm initial dye concentration, 1 g per 100 ml adsorbent dose, and contact time of 101 min with optimum predicted dye removal of 99%. However, the removal efficiency obtained from pristine biochar is only 60%. The Langmuir model was the most fitted isotherm model with an adsorption capacity of 2.07 mg/g. Adsorption kinetic equilibrium data was better described by pseudo-second-order model, and from the thermodynamic study, it has been suggested that the adsorption process was spontaneous, favourable, endothermic, and the nature of the removal involves both physical and chemical adsorption processes. The possible adsorption mechanisms governing the adsorption process of the dye with biochar are π – π electron donor-acceptor interaction and hydrogen bonding. Therefore, the biochar obtained from one-step catalytic pyrolysis of SCG waste can offer both the management of the SCG waste and for the treatment of textile wastewater.

The authors thank the Africa Centre of Excellence for Water Management. the Department of Chemistry, Addis Ababa University. The authors would like to extend thanks to Robera Plc, coffee and roasted coffee processing company, Addis Ababa, for the provision of SCG.

Admasu Adamu: conceptualization, methodology, visualization, investigation, writing original draft, review, and editing. Yonas Chebude: supervision, manuscript reviewing, and editing. Feleke Zewge: supervision.

The authors declare that there are no conflicts of interest.

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