Optimization of pyrocatechol violet biosorption by Robinia pseudoacacia leaf powder using response surface methodology: kinetic, isotherm and thermodynamic studies

Synthetic dyes are extensively used in industries such as textile, paper, plastic, printing, pharmaceuticals and dye manufacturing. Dyes have been the subject of much interest in recent years because of increasingly stringent restrictions on the organic content of industrial effluents (Abou Taleb et al. 2009). Dye-containing effluents are highly problematic wastewaters because they affect aesthetic merit and reduce light penetration and photosynthesis (Da Silva et al. 2011; De Menezes et al. 2012). Dyes can cause allergy, dermatitis, skin irritation and also provoke cancer and mutation in humans (Brookstein 2009; Carneiro et al. 2010). Therefore, the removal of dyes from water and wastewater is very important for environmental safety.

Different physicochemical methods like adsorption (Dural et al. 2011; El Haddad et al. 2013), precipitation (Lee et al. 2011), electroflotation (Essadki et al. 2008), coagulation/flocculation (Verma et al. 2012), solvent extraction (Lee et al. 2000), membrane filtration techniques (Alventosa-deLara et al. 2012), irradiation (Paul et al. 2011), chemical oxidation (Türgay et al. 2011), amongst others, have been used for the treatment of dye-containing effluents. However, these techniques have high sludge production and high cost. Therefore, the development of efficient, low-cost and environmentally friendly technologies to reduce dye content in wastewater is necessary. Among the numerous techniques for dye removal from wastewaters, biosorption is one of the most efficient for removal of different types of dyes (Wang 2010). It is considered an economical and eco-friendly practice in comparison with other available methods. The use of low-cost biosorbents for dye removal has been widely reviewed (Gupta & Suhas 2009). Some important biosorbents which have recently been studied for dye removal include pineapple leaf (Chowdhury et al. 2011), tamarind fruit shell (Saha et al. 2010), sugar beet (Malekbala et al. 2012), rice husk (Safa & Bhatti 2011), Thuja orientalis cone powder (Akar et al. 2013), Luffa cylindrica fiber (Demir et al. 2008), coffee bean (Baek et al. 2010), kenaf (Sajab et al. 2011), jackfruit leaf powder (Saha et al. 2012) and tree leaves (Deniz & Saygideger 2010). The main advantages of this process are simple operation, rapidity, potential cost effectiveness, selectivity and reusability of some biosorbents, and the low quantity of sewage sludge disposed of.

The biosorption process might be influenced by some variables such as pH, the biosorbent amount, sorbate concentration, contact time, and temperature. The aim of this study was to investigate the biosorption potential of Robinia pseudoacacia tree leaves to treat wastewaters contaminated with pyrocathechol violet dye. R. pseudoacacia tree leaves are in abundant supply, inexpensive and easily available in countries such as Iran. The experimental conditions were performed using multivariate optimization techniques. The multivariate chemometric approach is advantageous for the reduced number of laboratory experiments, the increased possibilities for evaluating interactions among the variables, and the relatively low cost compared to traditional univariate approaches.

Statistical methods of experimental design and system optimization, such as factorial design and response surface analysis, have been applied to different adsorption systems because of their capacity to extract relevant information from systems while requiring a minimum number of experiments. Response surface methodology is a collection of statistical tools for designing experiments, model development, evaluating the effects of factors and searching for optimum conditions of factors for desirable responses (Box et al. 1978). Box–Behnken statistical design is one type of response surface methodology design, which is an independent, rotatable or nearly rotatable, quadratic design having the treatment combinations at the midpoints of the edges of the process space and at the center (Govender et al. 2005). The Box–Behnken design is a very efficient model, since it requires a small number of runs and, therefore, is an important alternative avoiding time-consuming experiments.

A four-factor Box–Behnken experimental design was used to investigate and validate the initial concentration of used dyes, pH, contact time and temperature of the aqueous solution influencing the removal of pyrocatechol violet (PCV) by Robinia. The data was analyzed by fitting to a second-order polynomial model, which was statistically validated by performing analysis of variance (ANOVA) and a lack-of-fit test to evaluate the significance of the model. The kinetic, isotherm and thermodynamic biosorption studies were conducted to evaluate the dye removal ability of Robinia.

The R. pseudoacacia tree leaves were gathered from twigs into clean plastic bags, washed with ion-free distilled water and then dried at 80 °C for 24 h. The dried biomass was powdered with a mortar and pestle. The powdered biomass was sieved to pass through a 50 mesh sieve (0.29 mm) to obtain uniform particle size and then stored in plastic bags before being used as a biosorbent in batch studies. The mesh size sieve was examined in the range 30–70, and no significant effect was observed on adsorption. Modification of biomass with NaCl increased the amount of dye absorbed. This could be due to activation of the internal biosorbent surface and production of more binding sites for the dye. Modified biosorbent was treated with 0.1 mol L⁻¹ NaCl for 60 min and excess NaCl was removed by washing several times with distilled water. The final modified biosorbent was dried on a clean table and used as biosorbent for removal of dye.

Biosorption capacity may be significantly influenced by several factors, such as pH, initial concentration of dye, temperature, contact time, and speed of shaking. In this study, initial dye concentration, pH, temperature and contact time are taken as independent variables, while the other variables like the mass of biosorbent and shaking speed were kept constant. Screening techniques such as factorial design allow the analyst to select which factors are significant and allow subsequent optimization. With a factorial design, it is possible to determine the main effects as well as the interactive effects of the selected factors. The full factorial design was performed for screening the main variables and their interactions, which reduced the large total number of experiments. Two-level full factorial design was applied with a number of runs equal to 2^k+n, where k is the number of factors and n is the number of center points. The range and levels used in the experiments, determined from the preliminary experiments, are listed in Table 1. The experiments were run at random to minimize errors due to possible systematic trends in the variables. Thus, 19 experiments including three at the center point were performed.

After performing a screening of factors using a full 2⁴ factorial design, a Box–Behnken response surface design was carried out according to experiments described in Table 4. A significant advantage of Box–Behnken statistical design is that it is a more cost-effective technique compared with other techniques such as central composite design, three-level factorial design and D-optimal design, which require fewer experimental runs and less time for optimization of a process.

To further validate the model, the quality of the fitted model was evaluated by the coefficients of determination (R²). Normally, a regression model with R² > 0.90 is considered to have a very high correlation (Haaland 1989). The high R² value (0.9924) suggested an excellent correlation between experimental and predicted values; 99.24% variability of the response could be explained by the model, and only about 0.76% of the total variation cannot be explained by this model. Also, an acceptable agreement with the adjusted determination coefficient is necessary. The adjusted R² value (0.9836) was found to be very close to R². The lack of fit test measures the failure of the model to represent experimental data in the experimental domain at points which are not included in regression analysis (Sharma et al. 2009). Lack of fit was found to be non-significant (p = 0.798) and it indicated that the model equation was valid for the biosorption PCV onto Robinia powder.

Biosorption kinetics is one of the most important parameters that significantly depict the features of a biosorbent. Adsorption kinetics shows a strong dependence on the physical and/or chemical characteristics of the sorbent material, which also influences the sorption mechanism (Ofomaja 2010). In the present study, the commonly used models, pseudo-first and pseudo-second order models were chosen to describe the kinetic biosorption data.

The sorption system will follow a specific kinetic model if the R² value exceeds 0.98 and the calculated q_e value is comparable to that of the experimental value (Farooq et al. 2010). The correlation coefficients (R²) for the pseudo-first and pseudo-second order kinetic model were higher than 0.99 (Table 7 and Figure 4). Moreover, the experimental q_e value (4.288 mg g⁻¹) were very close to the theoretical q_e value (4.308 mg g⁻¹) calculated from the pseudo-second-order kinetic model. These results indicated that the biosorption of PCV on R. pseudoacacia can be well described by the pseudo-second-order kinetics. The pseudo-second-order model is based on the assumption that the rate-determining step may be a chemical sorption involving valence forces through sharing or exchange of electrons between biosorbent and sorbate (Bayramoglu et al. 2009). This model implies a chemisorption mechanism for PCV biosorption.

In this study, the potential of R. pseudoacacia tree leaves as a natural biosorbent was investigated for removal of PCV from aqueous solution. The full factorial design was used to screen variables affecting the biosorption process, to estimate the main effects and interaction effects of different variables. Based on the results of the ANOVA test, main factors such as dye concentration, contact time, pH and temperature were determined as effective factors and should be optimized. According to the normal probability plot and Pareto chart, the initial dye concentration and pH are the most significant factors in the response. As a specific amount of biosorbent must be used for removal of PCV from aqueous solution, and by considering the biosorption capacity, the amount of dye was essentially an important factor. pH may be of great importance as the dye structure is changed by changing the pH, which affects the acidic and basic groups present in the structure of the dye.

Box–Behnken experimental design, as a powerful response surface methodology, was utilized for optimization. A second-order polynomial model successfully described the effects of variables on the PCV dye removal. The experimental data indicated that biosorption of PCV dye onto R. pseudoacacia powder was spontaneous and exothermic in nature and the kinetic data were best described by the pseudo-second order model. The equilibrium data could be well fitted by the Freundlich isotherm models. The results of the present study suggest that R. pseudoacacia tree leaves could be used as an effective, low cost and eco-friendly biosorbent for the removal PCV from aqueous solutions.