Experimental Design and Optimization of Decolourization of Reactive Black-5 Dye Using Cloud Point Extraction

Ramachandran, Sathish Kumar; Tolesa, Leta Deressa; Abdulkadir Aba Gissa, Mohammedsani; Dhanabalan, Duraimurugan Alias Saravanan

doi:https://doi.org/10.1155/2022/2376597

Journal of Chemistry

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments Supplementary Materials References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 2376597 | https://doi.org/10.1155/2022/2376597

Experimental Design and Optimization of Decolourization of Reactive Black-5 Dye Using Cloud Point Extraction

Sathish Kumar Ramachandran,¹Leta Deressa Tolesa,²Mohammedsani Abdulkadir Aba Gissa,³and Duraimurugan Alias Saravanan Dhanabalan³

Academic Editor: Gomaa A. M. Ali

Received07 Dec 2021

Revised20 Apr 2022

Accepted23 Aug 2022

Published26 Sept 2022

Abstract

A two-phase separation method called cloud point extraction (CPE) does not use hazardous or flammable organic solvents. The efficient removal of the dye Reactive Black-5 (RB-5) from an aqueous solution using Triton X-114, a nonionic surfactant, is described in this study. Three-level factorial design and response-surface methods were used to quantify the impact of process variables on the CPE process, such as operating temperature and surfactant concentration. Investigations were conducted into how these process variables affected the ratio between the phase volumes, the concentration of dye in the surfactant-rich phase, and the residual amounts of dye in the diluted phase. As a result, ANOVA was used to create and validate mathematical models. The findings demonstrated that the correlation coefficients (R²) exceeded 0.98. The acquired findings showed that the suggested extraction process is efficient, and the proposed CPE approach removes 98% of the RB-5 dye under optimal conditions.

1. Introduction

The delicate environment is threatened by significant amounts of colored organic effluents from diverse textile companies. The remaining dyes make up 1–15% of the colored wastewater that is dumped into water bodies [1]. Nearly 10,000 dyes and coloring chemicals are used exclusively by the textile industry [1, 2]. These substances and pigments are still present in the ecosystem and have a negative impact on nature and all living beings in different ways [3, 4]. They reduce the diversity of aquatic creatures and plants by preventing sunlight from accessing the water bodies [5, 6]. Due to their brightness and durability, reactive dyes outperform other dyes in the dyeing of cellulose fibers [7]. Reactive Black-5 (RB-5) is a dye that belongs to the diazo category. It is broadly applied in the paper, leather, and textile industries. 15% of the dyes used in the textile industry are lost in the dyeing process’s effluent [8]. The most significant pollutants in textile wastewater are dyes, as well. As a result, RB-5 was chosen as the model dye for textile effluent in this study.

Utilizing a variety of techniques, including nanofiltration [9], adsorption on solid agricultural waste [10], micellar-enhanced ultrafiltration [11], adsorption with bentonite clay [12], ozonation [13], oxidation [14], activated carbon adsorption [15], and surfactant impregnated with aluminum-rich montmorillonite clay [16], among others, the decolorization of textile effluents is accomplished. Every one of the aforementioned techniques has benefits and drawbacks. Both ultrafiltration and nanofiltration have excelled in their respective fields. However, due to membrane fouling, which drastically decreases permeate flux and membrane life, their use is restricted [8, 10]. Due to the fact that dyes are not easily biodegradable, typical wastewater treatment procedures are unable to effectively remove them [17].

Because it does not require organic solvents for liquid-liquid extraction, a wastewater treatment technique based on an aqueous micellar solution has recently received a lot of interest. The aqueous micellar solution approach is a surfactant-based separation technique [18]. Surfactants are nontoxic, nonflammable, and nonvolatile, and they are needed in lower quantities for the aqueous micellar solution process than organic solvents [19]. Surfactants are also amphiphilic compounds that have favorable interfacial characteristics and are frequently used in a variety of industrial separation processes. Nonionic surfactants, in general, do not ionize in an aqueous solution and, at a specific temperature, stimulate the separation of two immiscible aqueous phases [20]. The coacervate phase, which is a surfactant-rich phase, and the diluted phase, which has a reduced surfactant content, are two immiscible aqueous phases, respectively [21]. Figure 1 shows a schematic illustration of CPE.

Beyond the cloud point, many researchers have studied the phase separation of nonionic surfactants. They claimed that micelle attraction, micelle growth, and dehydration of the nonionic surfactant’s outer layer’s micelles might all contribute to phase separation [22]. The dye is soluble in the micelle’s outer layer and core because of the dyes’ polarity. The increase in micelle aggregation causes the nonpolar molecules to become solubilized in the micelle core. Due to the dehydration of the polyoxyethylene chains in the mantle, polar molecules are solubilized [22, 23].

The experimental design is typically widely used to optimize a variety of processes, some of which are listed below. (a) Semmoud and Ma [24] carried out factorial design research to improve the Red Bemacid dye concentration during the CPE process by employing ionic liquids. (b) Uranium extraction using Triton X-100 and D2EHPA (2-(2-ethylhexyl)phosphoric acid [25]. (c) A CPE approach with surfactants to effectively eliminate the dye Direct Blue 71 (DB71) from an effluent [26]. (d) Improving the cobalt aluminate synthesis [27]. (e) Predicting the surface stress values of all fluids with a power-law tendency [28].

The process of cloud point extraction (CPE) is often influenced by surfactant concentration and temperature. Therefore, it is important to assess how temperature and surfactant concentration affect the elimination of RB 5. As they enable the consistent optimization of several variables, multivariate approaches have generally been chosen to optimize process parameters like temperature and surfactant concentration [28, 29]. The CPE procedure was previously optimized by altering one parameter while maintaining the values of the other parameters. As a result, the previous optimization method eventually demanded more time and effort and required increased chemical usage to evaluate each parameter. Response surface methodology (RSM) is frequently used to optimize the process variables that directly impact the desired outcome in order to solve this issue. The link between process characteristics and responses is demonstrated by RSM [30, 31]. The RSM method produces findings much more quickly and cheaply than other methods. Additionally, a small number of trials based on factorial designs are used to examine a number of process parameters using RSM. Compared to analytical investigations, RSM has the benefit of being less expensive and time-consuming [32].

To the best of our knowledge, the TX-114-based CPE procedure for RB-5 was limited, and there is no literature on the optimization process parameters used for RB-5. So, in this investigation, the nonionic surfactant TX-114 was used in the cloud point extraction (CPE) method to extract the RB-5 dye. As a result, evaluating the effects of surfactant concentration and temperature on RB-5 removal, optimizing surfactant concentration and temperature for the extraction of the RB-5 dye from an aqueous solution by using RSM, and estimating the phase volume, RB-5 concentration in the dilute phase, and the rich phase after separation, as well as extraction efficiency, are issues that are frequently discussed.

2. Materials and Methods

2.1. Materials

Reactive Black-5 (RB-5) dye (molecular formula, Na₄O₁₉S₆C₂₆H₂₁N₅) was obtained from Sigma-Aldrich, India. Triton X-114 (t-Octylphenoxy polyoxyethylene ether) was procured from Sigma-Aldrich, India. Chemical structures of RB-5 dye and Triton X-114 are depicted in Figures 2 and 3, respectively. All reagents were used without any further purification.

2.2. Cloud Point Extraction of RB-5 Dye

In textile effluent, color concentrations typically range between 10 and 25 mg·L⁻¹. As a result, different concentrations of Triton X-114 (0.01 M to 0.1 M) and dye (25 ppm, 50 ppm, and 75 ppm) were used to prepare aqueous micellar solutions for this study. Aqueous micellar solutions were kept in a thermostatic bath for 30 minutes as prepared (40, 50, and 60°C, respectively) depending on the cloud point temperature (CPT). (Model: High Precision Thermostatic Water Bath (with Stirrer), The Precision Scientific Co., Chennai, India) [32]. The volumes of the surfactant-rich phase and the diluted phase were recorded after the creation of the heterogeneous clear phases. Then, using a UV-visible spectrophotometer, the concentrations of RB-5 in diluted phases were calculated (Shimadzu, UV-2600).

2.3. Experimental Design

Three levels of factorial design were used: low, medium, and high. The graphs, analysis of variance, and effects computation were all performed using the DESIGN EXPERT 7.0 software program (StateEase, Minneapolis, USA). Various levels of experimental design are depicted in Table 1.

Temperature (X3), dye concentration (X2), and surfactant concentration (X1) were used as parameters, both at the three levels. The mean value served as the experimental response, and the trials were carried out in triplicate. A total of 15 trials were carried out, including one repetition at the central point and 14 connected to the experimental design matrix. Table 2 shows the design matrix and the experimental findings.

Temperature and surfactant concentrations were chosen so that they were above the surfactant’s turbidity curve, which is shown in Figure 4. Visual observation was used to estimate the cloud point. It was noted that the RB-5 dye plot displayed lower cloud point values. This is caused by how organic molecules interact with the polar head group of surfactants [33].

2.4. Cloud Point Extraction

Experiments with the CPE were carried out, as mentioned in section 2.2. Table 2 lists the experimental data, including the percentage of dye extracted (E), the phase volume ratio (R_v), the concentration of the dye RB-5 in the diluted phase following separation (X_s,d), and the RB-5 concentration in the surfactant-rich phase following separation (X_s,r).

2.5. Regression Analysis

A mathematical model was generated using regression analysis. The quadratic polynomial equations that demonstrate the relationship between each response and important variables and iterations are the result of the mathematical model [32]. The models below were derived from experiments.

3. Results and Discussion

3.1. Properties of Dilute Phase and Coacervate Phase

In the supplemental information, the characteristics of the coacervate phase and the dilute phase were covered. The parameters of the diluted phase were determined to be similar to those of water, as given in the additional material in STable 1. The surfactant micelle in the coacervate phase solubilizes the dye that is present in the solution. As a result, the predominant component of the diluted phase is water, and only a very small amount of dye and surfactant are present.

3.2. Statistical Assessment

3.2.1. Analysis of Variance Test

Analysis of variance (ANOVA) was used to determine the validity of the dye extraction model, and the results are shown in Table 3. The correlation measure was also used to calculate the dye extraction model’s coefficient of determination (R²). As can be seen in Table 3, the model demonstrated statistically significant regression with a 95% confidence level (F_calculated greater than F_tabled) and an R² of 0.9922, revealing that the model represented 99.22% of the variation in the experimental data. It’s noteworthy to note that the model’s F-value was 140.72, indicating that it was significant and predictive and that there was only a 0.01% possibility that noise was to blame for such a high “model F value.”

As demonstrated in Figure 5(a), the temperature had no impact on the dye removal, but the concentration of surfactant had a satisfactory impact. Additionally, Figure 5(a) shows that the interaction between the surfactant and the dye mostly accounted for the fact that the concentration of the surfactant had a stronger impact on the dye extraction efficiency than the temperature. The hydrolytic breakdown of the RB-5 dye is influenced by the ethylene oxide (EO) chains of the surfactant, which form intramolecular or intermolecular hydrogen bonds with dye molecules [34]. Furthermore, the surfactant concentration was higher when the coacervate phase formed, which tends to attach the dye to it regardless of temperature.

(a)

(b)

(c)

The Pareto chart illustrates how the process parameters affected the dye extraction process as presented in Figure 5(b). The surfactant concentration alone had a substantial impact on the model within the 95% confidence interval. This property applies to both the linear and quadratic components. The agreement between the value calculated by the model and the experimental value is shown in Figure 5(c).

Since it directly affects extraction efficiency, surfactant concentration is a crucial factor in CPE [35, 36]. The extraction is only partially effective when the concentration is low, but raising the concentration could affect the outcome of the analysis [37].

Percentage of dye extracted (E) (%):

3.2.2. Phase Volume Ratio (R_v)

Table 3 indicates the analysis of variance (ANOVA) computations to demonstrate the relevance of the suggested model. 0.9979 was the calculated coefficient of determination. With the appropriate degrees of freedom, the value of the F_calculated test was compared to that of the F_tabled test for the F distribution at a 95% confidence level. The proposed model is statistically significant and represents the responses as a function of the examined variables, according to the model F-value of 1014.07. The volume percentage of the coacervate phase increases with an increase in surfactant concentration, according to the response surface depicted in Figure 6(a) [38].

(a)

(b)

(c)

Because the surfactant is less hydrophilic at higher temperatures and creates even more concentrated coacervate in the surfactant, the volume fraction decreases in the temperature scenario. The variables’ effects on the coacervate phase volume fraction are depicted in Figure 6(b). The coacervate phase volume fraction is significantly influenced by temperature and surfactant content. Silva et al. [33] showed a similar result for the CPE method’s removal of phenol.

It is clear from the concentration of the surfactant that a rise in surfactant concentration will result in a rise in the volume fraction of the coacervate phase. The observations of Ji et al. [35] about similar outcomes during the extraction of phenolic acid from dandelion were also made. Additionally, when the concentration of surfactant rises, more micelles are produced for the CPE process, improving phase separation [39]. Temperature, on the other hand, has a negative impact, suggesting that an increase in this variable would cause a decline in reaction, or a decrease in the volume fraction of the coacervate phase.

Phase volume ratio:

3.2.3. Dye Concentration in the Dilute Phase after Separation (X_s,d)

With an R² of 0.9880 in Table 3, the suggested model for dye concentration in the diluted phase following separation demonstrated significant regression at a 95% confidence level. The model is suggested to be significant and predictive by the Model F-value of 91.85. A “Model F-value” this large could only happen in 0.01% of cases due to noise, proving the significance and predictability of the suggested model. Figure 7(a) displays the response surface graph made by the suggested model for dye concentration in the separated diluted phase (X_s,d). This is connected to the extraction efficiency, where the extraction will be lower with less surfactant supplied to the system [35]. The temperature will therefore have no impact on the amount of residual dye present in the aqueous phase following separation because it has no effect on dye extraction. The surfactant, which also serves as an extracting agent in this process, creates the coacervate phase with a substantially equal amount of surfactant once the cloud point (turbidity) is reached. This phase is in charge of removing dye from the aqueous phase. Surfactant concentration (linear component) is the significant variable within the 95% confidence interval, as shown by the Pareto chart in Figure 7(b) [33]. On either the linear or the quadratic component, the sole effect of temperature was less significant. The correlation between the experimental data and the values determined by the model is depicted in Figure 7(c).

(a)

(b)

(c)

Dye concentration in the dilute phase after separation (X_s,d):

3.2.4. Dye Concentration in the Surfactant-Rich Phase after Separation (X_s,r)

After phase separation, the residual dye concentration in the surfactant-rich phase is a crucial factor in determining the feasibility of the operation. Even though the extraction efficiency is high, the procedure is no longer practical due to the rising dye concentration since the loss of surfactant in the treated effluent leads to cost escalation and effluent contamination. The analysis of variance (ANOVA) was used to determine the model’s applicability in predicting residual dye concentration, and the results are shown in Table 4 with an R² value of 0.9858. With the appropriate degrees of freedom, the value of the F_calculated test was compared to that of the F_tabled test for the F distribution at a 95% confidence level. The model is suggested to be significant and predictive by the model F-value of 77.33. A “Model F-value” this large might happen owing to noise only 0.01% of the time. Response surface plots are depicted in Figure 8(a), showing the impacts of temperature and dye concentration. Within the examined range, as temperature rises, there is a propensity to obtain progressively lower dye concentrations in the surfactant-rich phase. The concentration of the surfactant-rich phase rises as the dye concentration does as well. The hydrophilicity of the surfactant decreases with temperature, which decreases its concentration in the aqueous phase. The initial dye concentration and temperature were shown to have significant impacts at a 95% confidence interval, according to the Pareto chart shown in Figure 8(b), which was produced by statistically analyzing experimental data. As a result, after separation, larger dye concentrations result from an increase in concentration in the surfactant-rich phase. After separation, the surfactant-rich phase has lower dye concentration values due to the rise in temperature. It is mostly caused by a rise in temperature because it intensifies micellar interactions [38, 40], and [41]. The consistency of the findings is demonstrated in Figure 8(c) by the correlation between experimental data and model predictions.

(a)

(b)

(c)

The response surface approach and experimental design were crucial for optimizing and examining the impact of temperature and surfactant concentration on RB-5 dye extraction. The outcome of the current investigation demonstrated that the efficiency of the process is influenced by both the temperature and concentration of the surfactant.

Dye concentration in the surfactant-rich phase after separation (X_s,r):

4. Conclusion

In this study, the experimental design and RSM were used to assess the impact of surfactant concentration and temperature on the dye extraction parameters. The obtained results showed that the process efficiency is highly influenced by both parameters, such as temperature and surfactant concentration. In this instance, surfactant concentration increases the efficiency of the extraction process, the phase volume ratio, and the dye concentration of the surfactant-rich phase while decreasing the dye concentration in the diluted phase. Temperature, however, has no impact on the effectiveness of the extraction process or the dye concentration in the diluted phase following separation. Only the volume ratio of the coacervate phase is affected by temperature, and as the temperature rises, the volume ratio of the coacervate phase decreases. Overall, the findings of the present investigation showed that 0.1 M surfactant at 60°C produces successful dye extraction with 98% removal (see Table 4).

Data Availability

The excel data will be available on request from the corresponding author. The excel data will be available on request from the corresponding author.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

Dr. Sathish Kumar Ramachandran and Dr. Duraimurugan alias Saravanan Dhanabalan thank the Department of Chemical Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India, for laboratory support in conducting this study.

Supplementary Materials

S1. Physical properties of surfactant dilute phase and surfactant-rich phase. Table S1. Physical properties for 75 ppm (RB-5- TX-114) of surfactant dilute phase and surfactant-rich phase at 313.15 K, 323.15 K, and 333.15 K. Table S2. Physical properties for 50 ppm of surfactant dilute phase and surfactant-rich phase at 313.15 K, 323.15 K, and 333.15 K. Table S3. Physical properties for 25 ppm of surfactant dilute phase and surfactant-rich phase at 313.15 K, 323.15 K, and 333.15 K. S1.1. Variation of refractive index with TX-114 concentration. S1.2. Variation of density with TX-114 concentration. S1.3. Variation of viscosity with TX-114 concentration. (Supplementary Materials)

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Journal of Chemistry

Experimental Design and Optimization of Decolourization of Reactive Black-5 Dye Using Cloud Point Extraction

Abstract

1. Introduction

2. Materials and Methods

2.1. Materials

2.2. Cloud Point Extraction of RB-5 Dye

2.3. Experimental Design

2.4. Cloud Point Extraction

2.5. Regression Analysis

3. Results and Discussion

3.1. Properties of Dilute Phase and Coacervate Phase

3.2. Statistical Assessment

3.2.1. Analysis of Variance Test

3.2.2. Phase Volume Ratio (Rv)

3.2.3. Dye Concentration in the Dilute Phase after Separation (Xs,d)

3.2.4. Dye Concentration in the Surfactant-Rich Phase after Separation (Xs,r)

4. Conclusion

Data Availability

Conflicts of Interest

Acknowledgments

Supplementary Materials

References

Copyright

3.2.2. Phase Volume Ratio (R_v)

3.2.3. Dye Concentration in the Dilute Phase after Separation (X_s,d)

3.2.4. Dye Concentration in the Surfactant-Rich Phase after Separation (X_s,r)