A statistical optimization of methylene blue removal from aqueous solutions by Agaricus Campestris using multi-step experimental design with response surface methodology: Isotherm, kinetic and thermodynamic studies

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A Statistical optimization of Methylene Blue removal from aqueous solutions by Agaricus campestris using multi-step experimental design with response surface methodology: Isotherm, Kinetic and Thermodynamic Studies

Mehmet Rıza Kivanc¸ Data curationWriting- Original draft preparationVisualizationInvestigationWriting- Reviewing a ¨ Vahap Yonten ConceptualizationMethodologySoftwareSupervisionValidation PII: DOI: Reference:

S2468-0230(19)30613-3 https://doi.org/10.1016/j.surfin.2019.100414 SURFIN 100414

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Surfaces and Interfaces

Received date: Revised date: Accepted date:

24 October 2019 19 November 2019 23 November 2019

Please cite this article as: Mehmet Rıza Kivanc¸ Data curationWriting- Original draft preparationVisualizationInvestig ¨ Vahap Yonten ConceptualizationMethodologySoftwareSupervisionValidation , A Statistical optimization of Methylene Blue removal from aqueous solutions by Agaricus campestris using multi-step experimental design with response surface methodology: Isotherm, Kinetic and Thermodynamic Studies, Surfaces and Interfaces (2019), doi: https://doi.org/10.1016/j.surfin.2019.100414

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A Statistical optimization of Methylene Blue removal from aqueous solutions by Agaricus campestris using multi-step experimental design with response surface methodology: Isotherm, Kinetic and Thermodynamic Studies Authors Mehmet Rıza KIVANÇa, Vahap YÖNTENb Affiliation a

Department of Chemistry, Faculty of Educational Science, Van Yüzüncü Yıl University, Van, Turkey, [email protected]

b

Department of Chemical Engineering, Faculty Engineering, Van Yüzüncü Yıl University, Van, Turkey, [email protected] *Corresponding Authors Mehmet Rıza Kıvanç and Vahap Yönten e-mails: [email protected]; [email protected] Tel & Fax: +90(432) 225 17 01-05 and +90 (432) 486 54 13

ABSTRACT In this study, a multi-step experimental design of Response Surface Methodology (RSM) was applied to optimize the medium conditions for the maximum removal of Methylene Blue from aqueous solution by a novel fungi A. campestris as a biosorbent. In first step, the effect of factors (initial dye concentration, temperature, contact time, pH, agitation speed and adsorbent dosage) was obtained using Plackett Burman Design (PBD). Then Steepest Ascent (SAD) used to predict the optimum region of effective factors in the second step. Central Composite Design (CCD) was utilized to evaluate the optimum medium conditions of effective parameters for the removal of cationic dye on last step. RSM indicated that optimum conditions of initial dye concentration, agitation speed and medium temperature for maximum removal of MB (95%) were achieved as 130.90 mg L−1, 125 rpm and 41.87 °C, respectively. The activation energy (Ea) was determined as 149.1, -178.6, 154.5 and 382.3 kJ/mol for 20, 50, 100 and 200 mgL-1 respectively. The characterization of adsorption process was confirmed by Scanning Electron Microscope (SEM) and Fourier Transform Infrared 1

Spectroscopy (FTIR). Adsorption isotherm was used to describe the adsorption equilibrium studies at different temperature99999s. Langmuir isotherm shows better fit than Freundlich and Temkin isotherms. Thermodynamic parameters like the enthalpy 15 kJ/mol (ΔHo), entropy 66.59 J/molK (ΔSo) and Gibbs free energy -4.47 kJ/mol (ΔGo) were evaluated and also, ΔGo shows a negative values indicating that the adsorption process was spontaneous and endothermic in nature. The results show that a multi-step statistical optimization designs is successful applied to experiments and novel and endemic biomass of Agaricus Campestris is an appropriate biosorbent and has a specific affinity for removal of Methylene Blue at under optimal conditions. Keywords: A. campestris, Methylene Blue, Optimization, Removal, Water treatment

1. Introduction The using of dyes was increased day by day linked with increasing of humans‟ populations, technology and industry. These materials were used in textile, papers, plastic, food and cosmetic industries etc. Among these areas especially the textile industry is a large sector and is considered the greatest creator of liquid effluents in the form of pollutants. According to the reported study, about one thousand tons of the textile dyes per year are discharged in the form of industrial effluent. Discharged wastewater from these industries usually contains different types of un-reactive dyes [1]. The some wastewater especially textile wastewater has caused some problems about environmentally and healthy. Therefore removal of dyes from wastewater is very important. All of them can be toxic, cancer genic, mutagenic and teratogenic [2-4]. In spite of toxic reactive dyes, MB belongs to dye classification of thiazine. This dye is cationic dye as it form positively charged molecules, when dissolved in water. MB is not only used as a dyestuff in textile industry but also as medicine. Methemoglobinemia [5], psoriasis [6], West Nile virus [7] and Duck hepatitis B [8] are some of diseases/conditions that use MB in the treatment. The negative effects of acute exposure to MB may include increases heart rate, nausea, Heinz body formation, headache and gastritis [9-11]. Removal of dye such as MB is critical for human. Because it‟s application must be cheap and easy. Some methods were used to remove of dyes from aqueous solutions. Various methods such as anaerobic/aerobic biological treatments [12], coagulation/flocculation [13], membrane filtration [14], oxidation [15], photo catalysis [16] and sonolysis [17] have been used for treatment of wastewater containing reactive dyes. However, these processes have disadvantages and limitations, such as high cost, generation of secondary pollutants and poor 2

removal efficiency [18]. Adsorption is inexpensive, flexible and simple design and convenient in use among these methods. It is also known to be superior to other techniques due to its ability to recover wastewater [19]. Activated carbon, peat, chitin, silica, fly ash, clay and others were used as sorbents, but the dye sorption capacity of these sorbents is not effective. Therefore, to enhance the dye sorption performance new sorbents are still under investigation [20]. Present study addresses optimization of removal conditions of MB by adsorption method on by naturally powdered fungi as a novel biosorbent in batch experiments. For optimization three steps PBD, SAD, and CCD were applied to optimize the medium conditions for the maximum response from aqueous solution. The characterization of adsorption process was confirmed by Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). In addition, some adsorption isotherm was used to describe the adsorption equilibrium studies at different temperatures. Thermodynamic parameters like the enthalpy (ΔHo), the entropy (ΔSo) and Gibbs free energy (ΔGo) were evaluated and ΔG o and ΔHo shows a negative values indicating that the adsorption process was spontaneous and exothermic in nature. 2. Material and methods 2.1. Materials MB was purchased from Sigma (Germany). The different solutions of MB were prepared by dissolving the sufficient amount of MB in distiller water. NaOH and HCl that diluted were used to adjust the pH values. 2.2. Preparation of Fungal biomass Indigenous white rot fungus A. campestris as biosorbent was collected from the province of Van from Turkey. The fungal sample was washed twice with distilled water to remove contaminants and then dried at room temperature. The dried fungal biomass was ground in a porcelain mortar to obtain a fine powder. It was then dried at 70 oC in an oven for 24 h to assess the complete death of the dried cells. For viability testing, the cells were inoculated to SDA medium at 27 oC for 24 h. The absence of mycelian A. campestris indicated positive results and the powder cells stored desiccators until further use. 2.3. Adsorption Experiment For the investigation of the medium factor‟s effects batch runs were conducted in 50 mL 3

Erlenmeyer flasks. The adsorbent and synthetic dye solution was taken in incubator for shaking. It was adjusted to 94 rpm. Samples were filtered by centrifugation prior to analysis; the analyses of MB in the supernatant solutions were determined by UV spectrophotometer (Thermo, Genesys 10S, USA) at its maximum wavelength of 663 nm wavelength. The removal rate of MB was calculated using following equation; ⁄

(1)

Where Ci is the initial MB concentration (mg L-1) and Co is the final MB concentration in solution (mg L-1). Adsorption capacity qt (mg/g) was computed by follow in equation: ⁄

(2)

Where mads is adsorbent dose (g), V is volume of solution (L), Ci is initial dye concentration (mg/L) and Ce is the residual concentration of the dye (mg/L) at different time intervals 2.4. Experimental design and statistical analysis Experiments were designed via three-stage response surface methodology. 2.4.1. Plackett-Burman Design Plackett-Burman design outputs with a CCD were employed in RSM to optimize the significant factors influences in % removal of MB by A. campestris. Initial screening of the most significant in depended parameters affecting removal of MB by naturally powdered fungi was performed by PBD. Determination of the factors that significantly influenced the specific response was evaluated by this technique. The technique is based on the first-order polynomial model as in follow equation. ∑

(3)

Where Y is the response (removal of MB), ??0 is the model intercept and ??i is the linear coefficient and Xi is the level of the independent factor [21]. Six real and five dummy factors, initial dye concentration, medium temperature, contact time, pH, agitation speed and adsorbent dosage, were examined to evaluate the key factors significantly affecting the removal of MB. Based on PBD, each factor was prepared in two levels: –1 for low level while +1 for high level. Table 1 represents the experimental design matrix with six factors as well as the response. 4

2.5. Steepest ascent method This procedure was used to find the maximum increases in the response. For optimization, it is more difficult to find points at the upper limit. SAD shows the relative amounts that should vary for the maximum yield of the direction and factors at right angles to the contour lines representing equal efficiency. The effects of three significant factors determined by PBD were screened to locate the optimum region with respect to response by applying SAD experiments [22]. Table 2 illustrates the experimental design of SAD and corresponding responses. 2.5.1. Response surface methodology Optimization is a process that considers the interaction between dependent parameters and their targets. It is usefully on the adsorption system using combination mathematical and statistical techniques for optimizing the processes and evaluating the relative significance of several factors even in the presence of complex interactions. In Table 3, coded and actual values of independent factors were given as seen in this table. RSM involves design and experimental response surface modeling through regression and optimization. The objective is to determine the optimum operational conditions of the process or to determine a region that satisfies the operating specifications [22, 23]. CCD with three factors at five levels was conducted in this work. The total number of experiments was 20 = 2k + 2k + 6, where k is the number of factors. Fourteen experiments were augmented with six replications at the center points to evaluate the pure error. Table 4 shows experimental design and results of CCD. In the optimization process the response can be related to selected factors in quadratic models. A quadratic model is supposed to be as follows.

∑

∑

∑∑

Where Y is the response, ??0 is the constant coefficient, Xi (i = 1–3) are variables, ??i are the linear, and ??ii are the quadratic, and ??ij (i and j = 1–3 ) are the second-order interaction coefficients. Data were processed using the Design-Expert 7.0 program (trial version) and an analysis of variance (ANOVA) test was calculated to obtain the interaction between the process variables and the response. The quality of fit of the polynomial model was expressed by the coefficient determination R2, and its statistical significance was checked by the F-test. 5

3. Result and discussions White rot fungus is a thick, hard and complex biomass consisting of cell walls, polysaccharide, protein, lipid and polyphosphate layers. The cornerstone of this structure is chitin consisting of N-acetylglucosamine residues. Amberlite XAD-4, which forms a composite with this biomass, is a white water-insoluble polymeric resin. It is a non-ionic cross linked polymer which drives its adsorptive properties from its patented macro reticular structure (containing both a continuous polymer phase and continuous pore phase), high surface area, and the aromatic nature of its surface. This structure gives resin polymeric adsorbent excellent physical, chemical and thermal stability. Because of these reasons, fungi both physically and chemically interact with polymeric resin surface. This might be explained by the mechanism of fungi binding to XAD-4 which involves surface adsorption to functional groups. 3.1. Characterization of the adsorbents FTIR peaks for the utilized adsorbent agent are given in Fig. 1 (1) and (2). Wavelengths were examined before and after adsorption between 450 and 4000 cm-1. Some significant bands at 3271, 2923, 2360, 1629 and 1300-400 nm which indicated the bonds,-OH groups, NH stretching, -C=C- bonding, SO group and C-O, C-C and C-Functional groups were appeared respectively. The peaks observed at 2923 cm-1 can be assigned to stretching vibrations of the C-H alkyl groups for MB. The peak around 1629 cm-1 is due to the C=C aromatic or may be asymmetric and symmetric stretching C=O vibration for MB. Comparison of the spectra of A. campestris before and after adsorption found in the figure demonstrated that certain oscillations disappeared and others varied as a result of adsorption. Oscillation peaks on 400 and 1128 cm-1wavelength that were present before adsorption could be C-O, C-C and C-H functional structures. These structures disappeared by reacting with certain present groups in MB parallel to the adsorption mechanism. In both cases, oscillation frequencies of certain functional groups were analyzed in specific wavelengths. Carboxyl and hydroxyl groups are important in MB suppression process. As understood from Fig. 1 (2) a peak at 2312 was occurred at after adsorption. Because the SO group of MB was immobilized on the adsorbent by disappear of C-O, C-C and C-H functional structures. Therefore this stretching vibration is SO groups in Fig. 1 (2). As shown in Fig. 2 the before and after adsorption SEM images of A. campestris. Fig. 2 (a) demonstrates before adsorption images of fungi after bonding with MB 6

at 10 µm values, and these images are more dense and MB particles hold on to the surface when compared to the after adsorption images shown in Fig. 2 (b). The simplicity observed in before adsorption is apparent in after adsorption images, proving the occurrence of adsorption in the current study. 3.2. Screening process with a Plackett–Burman Design The effects of the most significant and independent factors in statistical analysis have recently been performed by many researchers [24-26]. The relative significances of six factors of this study, which are initial dye concentration, medium temperature, contact time, pH, agitation speed and adsorbent dosage, were explored by PBD to determine the most significant factors. The minimum and the maximum values of the variables and their effects on removal of MB were given in Table 1. Based on the PBD results, pH (A), contact time (B), dye concentration (C), adsorbent dosage (D), medium temperature (E) and agitation speed (F) were determined the most effective factors on the response. The following quadratic models expressed an empirical relationship between response and input variables in encoded values in eq 5, % Removal of MB = +33.07 + 0.53 A + 1.81 B + 28.97 C+1.46 D - 2.45 E -3.16

(5)

3.3. Path of steepest ascent PBD is a valuable tool for screening parameters that significantly affect the response, but it is unable to predict the optimum region of the parameters. Based on the obtained firs order model equation and the three important medium parameters, When the initial estimate of the optimum operating conditions is a logway fromthe actual optimum, the steepest ascent method is a simple and economically efficient experimental procedure for getting into the general vicinity of the optimum [27]. The way of SAD was determined to find the proper direction of changing variables by increasing the initial concentration (C), decreasing temperature (E) and the shaking ratio (F). Seven experiments were conducted to locate the plateau of the response. The path of the SAD and the obtained results are given in Table 2.The plateau of the response was obtained as % 86.2 removal of MB when initial dye concentration, temperature and shaking ratio were 112 g/L, 41 ºC and 96, respectively. It was concluded that the optimum points were in that region. 3.4. Pareto Graphics

7

The effects of parameters on the response are valuable for regression analysis because the positive sign increases the response, while the negative sign decreases the response [4, 26]. The adsorbent amount, temperature, interaction between adsorbent amount and pH, interaction between adsorbent amount and initial concentration have positive effects and interaction between pH and initial concentration have negative effects. The results of experiments were expounded by Pareto analysis since it will give more significant information. The following equation was used for calculation of percentage of factors on removal dyes [28, 29]. ⁄∑

(6)

Pareto graphic analysis was shown in Fig. 2. Among variables, quadratic effect of initial dye concentration of dye (28.7 %), temperature (2.45%) and shaking ratio (3.16 %), are effective parameters on dye removal by A. campestris. 3.5. Response surface plots The effect of each parameter on MB removal and interaction between three variables and effect of parameters were illustrated in Fig. 3. Depending on the quadratic model, 3D response surface were arranged. To study the impact of temperature and initial concentration on the yield efficiency, some experiments with valuables that are temperature (38-44 ºC) and initial concentration (76-148 mgL-1) fixed amounts of shaking time (94 rpm) were designed in Fig. 3 a) In this figure MB removal has strongly been affected by both factors. Any increment in initial concentration towards from 76 to130 mgL-1 significantly increased % MB removal up to 95%. After 130 mgL-1 the response do not increase and it decrease from 130to148 mgL-1. The optimum initial concentration was observed at 130 mgL-1 .The effect of temperature is similar to effect of initial concentration. Any minimal increment in temperature towards from 38 to 40°C significantly increased % MB removal up to 95%. After 41 °C value, response does not increase and it decrease from 41 °C to 44 °C. The same trend was obtained in some works. Maximum 130 mgL-1 MB was adsorbed on the novel nanogels based on hydroxypropyl cellulose–poly (itaconic acid) [30]. In a same study, optimum dye concentration was found 130 mgL-1on Shoe Soles Waste [31]. The optimum temperature was performed at 39°C in our work. In other studies, optimum temperature was found 45 °C to optimize decolorization of dyes by the laccase-mediator system [32] and the optimum 8

temperature was found 30°C for the decolorization of simulated dye effluent using Aspergillus fumigatus fresenius [33]. Fig. 3 b) shows the 3D surface plot showing the initial concentration and shaking ratio on the MB removal onto A. campestris. % Removal of MB simultaneously inclined with an increment in shaking ratio from 63 rpm to 125 rpm and initial concentration achieved maximum about 95% MB removal at shaking ratio of 125 rpm and initial concentration of 112 mgL-1 approximately and decreased at higher values of initial concentration. In a study, the maximal dye removal efficiency of 99.9 % was achieved at agitation speed 106 rpm [33]. The changes in the MB removal with an increment in initial MB concentration could be credited to the changes in the vacant active sites quantity for the adsorption process. With a constant amount of adsorbent, the ratio of available active sites is high at low concentration of MB and thus provides enough surface sites for the adsorption process [34, 35]. However, the adsorption process hardly to be increased by increasing the MB concentration upon reached the saturated point, which can be claimed on the insufficient vacant active sites. Removal process when being compared with the initial MB concentration, which in agreement with the result reported by Pareto graphic (Fig. 3 d) showing the higher t-value of initial concentration (C) than the shaking ratio (F). As seen in Fig.3 c), the effect of interaction of temperature and shaking ratio was given. The % removal of MB increases with increasing of temperature from 38 to 41 ºC. After this point the yield decreases with increasing of the temperature from 41 to 44 ºC. The maximum temperature was mainted at 41ºC approximately for the optimum yield [32, 36]. This can be explained by exothermicity and naturallity of the adsorption process and the weakening of bonds between dye molecules and active sites of the adsorbent at high temperatures. The influence of solution temperature is diminished and rectified by higher pH [37]. Graphs show that the maximum 95.93 % Removal occurs at the initial dye concentration of 130.90 mgL-1 and at temperature of 41.87 ◦C and shaking ratio 125 rpm, which is in accordance with the model given in Fig. 4. Confirmatory tests were performed under the specified conditions for the control of maximum parameters. Design and experiment‟s results showed that efficiency of MB removal was 95.93 %. The conformity between the reexperiment results and the optimum results, which indicates that the multi-step optimization could be used effectively for evaluating and optimizing the effects of the adsorption independent variables on the removal of MB from the aqueous solutions using A. campestris, was high [38].

9

3.6. Analysis variance (ANOVA) It is quite difficult to optimize the removal of dyes on some adsorbents in economical cost and less time cases. Therefore, the using of RSM eliminated these problems and was applied to build up an empirical model for modeling MB removal by analyzing the significant factors. By applying multiple regression analysis on the experimental data, the following second-order polynomial equation was established to explain MB removal in terms of the medium factors, which are initial concentration, temperature and shaking ratio eq. 7. Y = 95 + 0.498 C – 1.249 E+ 2.268 F + 0.040 CE–0.844 CF – 0.404EF –1.807 C2 – 1.769 E2 – 0.149 F2

(7)

In this equation; C, E and F are the initial concentration (mg L-1), temperature (oC) and shaking ratio (rpm), respectively. The analysis of variance test was conducted with experimentally observed data to test the significance of the second-order polynomial equation eq. 5 and the test results were presented in Table 5. The model F-value of 3.99 implies that the model is significant. The fit of model was checked by the coefficient of determination R2, which was calculated to be 0.78, indicating that 78% of the variability in the response could be explained by the model. It indicates a good agreement between experimental and predicted values and implies that the mathematical model is reliable for MB removal. The value 0.0004 of “Prob > F”, which is less than 0.05 indicates that the model terms are significant. According to the results of the statistical design and by application of eq. 3 and 4, the optimum values of tested factors were evaluated as follows, 36.3 mg L-1the initial concentration, 2 pH, 38.7 oC temperature and 0.304 g amount adsorbent. Under the optimized conditions, maximum RBBR removal was predicted to be 98% respectively. Values of "Prob > F" less than 0.050 indicate model terms are significant. In this case, F, C2, and E2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. The “Lack of Fit F-value” of 282.93 implies the Lack of Fit is significant. There is only a 0.01% chance that a “Lack of Fit Fvalue” this large could occur due to noise. 3.7. Adsorption isotherms

10

Adsorptions models were used to describe the system. Due to the shape of the isotherms, the sorption data were calculated according to Freundlich, Langmuir and Temkin Ishoterms given in below (8-9-10) equations [39-41]. (8) (9) ……………..…………………..(10) Where qe is the amount of the dye per unit weight of P. eryngii immobilized on Amberlite XAD–4 (mg g-1), Ce the equilibrium concentration of the dye (mg L -1), while KF, KL, bT and n are constants that give estimates of the adsorption capacity and intensity, respectively. KL is a direct measure of the intensity of the adsorption process (L mg −1), and qm is a constant relating to the surface area occupied by a monolayer of the dye, reflecting the adsorption capacity (mg g-1). Based on the data of qe from the fittings of the pseudo-second order adsorption rate model, q m and KL can be determined from its slope and intercept from a typical plot of 1/qe versus 1/Ce. In Eq. (7), The slope n-1, ranging between 0 and 1, is a measure for the adsorption intensity or surface heterogeneity. KF is a constant for the system, related to the bonding energy. KF can be defined as adsorption or distribution coefficient and represents the general capacity of the dye adsorbed on to fungi for a unit equilibrium concentration. The results of isotherms were fitted by using the data of adsorption capacity from the regression of Eq. (8). AT is the isotherm bonding constant (L/g). AT and bT can be determined from its slope and intercept from a typical plot of q e versus In Ce. As indicated in Table 6, the Freundlich models yields a somewhat better than Langmuir and Temkin models on adsorption of dye on fungi as reflected with correlation coefficients (R2). Comparison of adsorption capacity observed in this work with other adsorption capacities in the literature was given in Table 7.

3.8. Thermodynamic parameters of adsorption of MB In Fig. 5 the effect of initial concentration and temperature on the MB adsorption was illustrated. While temperature increases, adsorption of dye increases. This also indicates that the adsorption is exothermic [36,38,42,43]. The change of thermodynamic parameters like standard free energy (ΔG°), enthalpy (ΔH°) and entropy (ΔS°) in the adsorption process were observed from the following equation: 11

…………………………………..

(11)

In this formula; R is the gas constant (8.314 J/mol K),Kd is the equilibrium constant and T is temperature. The Kd value is observed from follow equation. ⁄

………………………………..

(12)

Where, Qe and Ce are the equilibrium concentration of dye ions on adsorbent (mg/L) and in the solution (mg/L), respectively. Standard enthalpy (ΔH°) and entropy (ΔS°) of adsorption can be estimated from Van‟t Hoff equation given in: ⁄ -

⁄

(13)

Van‟t Hoff linear form has a slope and intercept and they are ΔH°ads/R and ΔS°/R, respectively. As seen in Fig. 4 this plot for the adsorption of MB onto fungi was given. Thermodynamic parameters obtained are shown in Table 8. The values of ΔH° for MB is less than 40 kJ mol-1 so it was suggested system is physical in nature [44-46].The endothermic nature of process is well explained by positive value of the enthalpy change. Generally, the positive value of H ° indicates that the adsorption proceeds endothermically. The negative value of free energy suggests that the adsorption process is spontaneous and the affinity of the adsorbent for the dye is indicated by the positive value of entropy [38, 43, 47-49]. 4. Conclusions In present study, the multi-step design was applied for the removal of MB. PB, SAD and CCD were used as a multi-step design. Optimum conditions of initial dye concentration, agitation speed and medium temperature for maximum removal of MB (95.9%) were achieved as 130.90 mg L−1, 125 rpm and 41.87 °C, respectively. Langmuir, Freundlich and Temkin isotherms were used to describe the adsorption equilibriums. Langmuir Isotherms is better fit than other. Thermodynamic parameters were evaluated. Gibbs free energy shows negative values indicating that the adsorption process was spontaneous in nature. In our knowledge, indigenous A. campestris was an appropriate and novel adsorbent for the removal of Methylene blue from aqueous systems. References [1] M.A. Kamboh, I.B. Solangi, S.T. Sherazi, H.S. Memon, A highly efficient calyx 4 arene based resin for the removal of azo dyes, Desalination. 268 (2011) 83-89. https://doi.org/10.1016/j.desal.2010.10.001. 12

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Nomenclature ANOVA : Analysis of variance ??0

: Constant coefficient of eq 1

??I

: Linear coefficient of eq 1

??ii

: Quadratic coefficient of eq 1

??ij

: Interaction coefficient of eq 1

CCD

: Central Compozite Design

FTIR

: Fourier transform infrared

MB

: Metyhlene Blue

PBD

: Plackett- Burman Design

RSM

: Response Surface Methdology

R2

: Coefficient of determination

R1

: Response (Removal)

SAD

: Steepest Ascent Design

Sym

: Symbol

Temp

: Temperature

Figure Captions Fig. 1. FT-IR spectra of A. campestris. 1) The spectrum of before adsorption 2) The spectrum of after adsorption. Fig. 2. SEM image of process a) pre-adsorption b) post- adsorption. Fig. 3. a), b) and c) Three-dimensional response surface plots showing the effect of the parameter‟ ratio on the percent % Removal of MB, d) the effect of parameters on the response with Pareto Analysis. Fig. 4. Desirability graph for numerical optimization of three independent variables, initial concentration, temperature and shaking ratio. Fig. 5. Van‟t Hoff plots of MB adsorption onto A. campestris.At 50 mgL-1 dye concentration. 18

Table 1 PBD matrix for determining the factors influencing Removal of MB. * % Removal of dye

Run

pH (A)

Contact Time (B)

Con. (C)

Adsorbent Dosage (D)

Temp. (E)

Shaking Time (F)

Y*

1 2 11 12

3 11 11 3

75 75 75 75

0.15 75 0.15 75

0.7 0.1 0.1 0.7

45 25 25 25

150 150 300 150

4.5 68 4 72

Table 2 Experimental design of steepest ascent and corresponding response. Run

Initial concentration (mgL-1)

0 0 + 1Δ 0 + 2Δ 0 + 3Δ

40 76 112 148

Temperature

(°C)

Shaking Time (min)(rpm)

Y*

32 63 96 125

80.5 83.9 86.2 84.5

35 38 41 44

* % Removal of dye Table 3 Coded and actual values of independent factors. Variable Concentration Temperature Time

Sym. C E F

Coding -α

−1

0

1

+α

51.5 36 41.9

76 38 63

112 41 94

148 44 125

172.5 46 146.1

Table 4 Parameters, their intervals in the runs conducted in CCD and corresponding result. Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Initial Concentration (mgL-1) 148.0 112.0 112.0 148.0 112.0 112.0 112.0 51.5 112.0 112.0 112.0 76.0 148.0 112.0 76.0

Temperature (ºC) 44.0 41.0 46.0 38.0 41.0 41.0 41.0 41.0 41.0 36.0 41.0 44.0 44.0 41.0 44.0

19

Shaking Time (g) 125.0 94.0 94.0 125.0 94.0 146.1 41.9 94.0 94.0 94.0 94.0 63.0 63.0 94.0 125.0

Y* 93.9 95.5 86.7 95.0 95.0 95.7 94.7 89.9 95.0 94.5 95.0 86.1 88.5 95.0 93.7

16 17 18 19 20

148.0 76.0 172.5 76.0 112.0

38.0 38.0 41.0 38.0 41.0

63.0 63.0 94.0 125.0 94.0

89.2 85.7 91.1 96.1 95.0

* % Removal of dye Table 5 Analysis varians test for MB removal on A. Campestris. Source

Sum of Squares

DF

MeanSquare

F Value

Prob> F

Model C E F CE CF

186,3277 3,388609 21,31331 70,29995 0,013179 5,699671

9 1 1 1 1 1

20,70307 3,388609 21,31331 70,29995 0,013179 5,699671

3,987346 0,652635 4,104877 13,53955 0,002538 1,097739

0.0210 0.4380 0.0703 0.0042 0.9608 0.3194

Significant

Table 6 Parameters in the Langmuir and Freundlich adsorption isotherm models. Freundlich

Langmuir

Temkin

Temparature (K)

qm -1 (mg g )

KL -1 (Lm g )

R2

KF -1 (mg g )

n-1

R2

AT -1 (Lg )

bT

R2

298 308 318

28.5 -0.02 -

1.3 -1.97 -

07 72 -

1.5 5.9 1.86

0.7 0.4 0.8

82 87 82

1.5 5.9 1.86

0,39 0,59 0,57

65 86 89

Table 7. Comparison of the MB adsorption capacities of the various adsorbent. Adsorbent

Adsorption capacity, mg g

Orange peel Green algae Red mud Fly ash This study

-1

References

11.62 68.0 27.8 15.0 5.9

[50] [51] [52] [53] -

Table 8. Thermodynamic parameters for the adsorption of MB onto A. Campestris. Con. (mg/L)

50

Temp (K)

Kd

-∆G (kJ/mol)

∆H (kJ/mol)

∆S (J/mol K)

298

13.1

4.47

15

66.59

308

6.11

4.48

55

318

2.20

2.20

20

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author statement Vahap Yönten: Conceptualization, Methodology, Software Mehmet Rıza Kıvanç: Data curation, Writing- Original draft preparation. Mehmet Rıza Kıvanç: Visualization, Investigation. Vahap Yönten: Supervision. Vahap Yönten: Software, Validation.: Mehmet Rıza Kıvanç: Writing- Reviewing and Editing,

fig. 1

21

Fig. 2

Fig. 3

Fig. 4

22

fig. 5

23