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Journal of Water Process Engineering 6 (2015) 193–202

Contents lists available at ScienceDirect

Journal of Water Process Engineering

journa l h om epage: www.elsev ier .com/ locate / jwpe

tatistical optimization of Basic Blue 41 dye biosorption byaccharomyces cerevisiae spent waste biomass and photo-catalyticegeneration using acid TiO2 hydrosol

our Sh. El-Gendy a,∗, Radwa A. El-Salamony a, Salem S. Abu Amr b, Hussein N. Nassar a

Egyptian Petroleum Research Institute, Nasr City, Cairo 11727, EgyptSchool of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, Malaysia

r t i c l e i n f o

rticle history:eceived 29 November 2014eceived in revised form 11 April 2015ccepted 14 April 2015

eywords:iosorptionhoto-catalytic degradationasic Blue 41accharomyces cerevisiae

a b s t r a c t

Factorial experiments with five factors; stirring rate, process time and temperature, initial dye con-centration and biosorbent dosage at three levels were conducted based on central composite design ofexperiments to investigate their effect on Basic Blue 41 dye biosorption onto bioethanol fermentationspent waste biomass of Saccharomyces cerevisiae. A highly statistically significant quadratic model at 95%confidence level (p < 0.0001,R20.9612andR2

adj0.9386) was developed to charcterize the influence of thesevariables on biosorption effeciency. Response surface methodology was employed to optimize the pro-cess, recording maximum biosorption % of ≈94% (23.5 mg/g) under static condition, within 14 h at 20 ◦Cusing 0.6% biosorbent in an initial dye solution of 150 mg/L. Approximately 92% of adsorbed dye was des-orbed by elution with self-clean acid TiO2 hydrosol (pH 2) and the regenerated biosorbent was employed

cid TiO2 hydrosol for four successive cycles. The photo-catalytic degradation of the desorbed dye under ultraviolet illumi-nation (8 W) followed the pseudo first order kinetic model (R2 0.9687) with apparent rate constant Kapp

of ≈0.0043 min−1. The proposed integrating biosorption with self-clean desorption and photo-catalyticdegradation process, resulted in no secondary pollution in the form of any concentrated wastes, thus hasimportant environmental and economic aspects.

© 2015 Elsevier Ltd. All rights reserved.

. Introduction

Discharge of industrial effluents contaminated with dyestuffsn the waterways is a matter of concern for both toxicologicalnd esthetical reasons [1]. Azodyes are the mostly used colorants60–70%), the chemical groups constructing these dyes; azo group

N N ), aromatic rings and auxo-chromes ( OH, SO3, etc.) makeheir bioremediation from effluents a sophisticated matter [2].

The conventional methods for treating dye-containing wastew-ter; flocculation, coagulation, precipitation, membrane filtration,zonation, elctrochmical techniques and biodegradation are usu-lly ineffective for complete removal of these compounds [3]. Theost widely used and effective method is adsorption using acti-

ated carbon, zeolite, grapheme oxide and nano-porpus silica, etc.,

ut its running costs are expensive [4,5]. Implementation cost ofn effluent treatment plant is of prime consideration influencingecision-making; this led many researchers to search for alterna-

∗ Corresponding author. Tel.: +20 1001443208.E-mail address: nourepri@yahoo.com (N.Sh. El-Gendy).

ttp://dx.doi.org/10.1016/j.jwpe.2015.04.007214-7144/© 2015 Elsevier Ltd. All rights reserved.

tive readily available and cheap biomass for biosorption application[6].

To minimize the process cost and the disposal problem ofpollutant-loaded biosorbents; priceless spent waste biosorbent asby-products of other industrial processes can be applied in biosorp-tion technique and used biosorbents should be regenerated andreused repeatedly. Regeneration of cationic dye-loaded biosorbentswith dilute acid solution is well known [7], but these regenera-tion methods leave concentrated solutions of dyes, which bring asecondary pollution to the environment that should be eliminated.

In this study; response surface methodology RSM as an assem-blage of central composite design of experiments and multipleregression-based methods was applied to study the influence ofstirring rate, process time and temperature, initial dye concentra-tion, and biosorbent dosage on the biosorption of a recalcitrantstable textile industry widely used water-soluble cationic azo-dye;Basic Blue-41 (BB-41). In an aspect of re-use of one of the waste

by-products of industrial production process to treat industrialwastewater effluent in a cost effective process; bioethanol fermen-tation spent waste biomass of Saccharomyces cerevisiae was usedas a biosorbent. A self-clean eluent-acid TiO2 hydrosol with a high

194 N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202

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Table 1Experimental range and levels of independent process variables for BB-41 removal.

Range and levels

Independent variables −1 0 +1

Stirring rate (A, rpm) 0 50 100Process time (B, h) 4 12 20Process temperature (C, ◦C) 15 25 35

Fig. 1. Chemical structure of Basic Blue 41 dye.

hoto-catalytic activity was prepared and firstly used as an elu-nt to desorb BB-41 from the sorbent, and then its photo-catalyticegradation capabilities on the desorbed dye was studied, in anttempt that, the hydrosol could be continuously used in desorptionnd photo-degradation process, which consequently would econ-mize large volume of the eluent and would not bring secondaryollution.

. Materials and methods

.1. Biosorbent

Fermentation spent waste biomass of S. cerevisiae, purchasedrom Petroleum Biotechnology lab, Egyptian Petroleum Researchnstitute was used in this study.

The biomass was washed three times with distilled water, driedvernight at 60 ◦C, then grinded in a mortar, sieved to constant size0.07–0.08 mm) and stored for further use.

.2. Adsorbate

The dye used in this study was, C.I. Basic Blue 41 (Syn-nyms: Panacryl; Blue X-GRL; Maxilon Blue GRL 300%; Sevronlue GR; Kayacryl Blue GRL; Anilan Blue GRL; Synacril Blue; Abcol Blue GRL 900%; Aizen Cathilon Blue GRHL; Basacryllue Z-3GL or Astrazon Blue FGGL) is a cationic azo tex-ile dye, and was purchased from Ciba Specialty Chem-cals Inc. [color index C.I. number: 11,105, C.B. number:B0499107, molecular formula: C20H26N4O6S2, molecular weight:82.57, IUPAC name: 2-[N-ethyl-4-[(6-methoxy-3-methyl-1,3-enzothiazol-3-ium-2-yl)diazenyl]anilino]ethanol;methyl sulfate.

t is also named as benzothiazolium, 2-((4-(ethyl(2-hydroxyethyl)mino)phenyl)azo)-6-methoxy-3-methyl-, methyl sulfate (salt).he structure of Basic Blue-41 (BB-41) is illustrated in Fig. 1.

A stock solution of BB-41 with a concentration of 200 mg/L wasrepared in distilled water, which was further diluted according tohe experimental conditions.

.3. Analysis of dye

The �max 608 nm of BB-41 was determined on a double-eam JASCO UV/Vis/NIR spectrophotometer model V-570 (JASCOnalytical instruments, 8649 Commerce Drive, Easton, Maryland1,601-9903, USA). Standard curve for different concentrations ofhe dye solution (2.5–100 mg/L) was established as the concentra-ion range of BB-41 adhering to Beer’s law within 0–100 mg/L andistilled water was used as the blank.

.4. Batch biosorption experiments

Each batch adsorption experiment was conducted by contacting

00 mL (pH 7) of different initial concentrations of BB-41 (adsor-ate) with known dose of dried biomass as an adsorbent in 250 mLrlenmeyer flasks closed with PARAFILM ‘M’ to prevent evapora-ive loss and placed in a rotary shaking incubator set at different

Initial dye concentration (D, mg/L) 100 150 200Biosorbent dosage (E, % w/v) 0.2 0.4 0.6

speeds and temperatures according to the experimental conditions.Adsorbent was separated from the solution at predetermined timeintervals by centrifugation at 2000 rpm for 15 min, and then col-lected for the regeneration experiments. The absorbance of thesupernatant solution at 608 nm was measured to determine theresidual dye concentration and to calculate the percentage of dyeremoval. Negative controls (with no biosorbent) were carried outto ensure that any sorption effect of dye onto the wall of the conicalflasks is ruled out. The dye concentration of the control was used asthe initial concentration (Co mg/L) for calculations of dye removal,using the following equation:

Dye removal =(Co − CtCo

)× 100 (1)

where, Ct mg/L is the residual dye concentration at different timeintervals (t,h). All experiments were conducted in triplicates andthe listed data are the average of the obtained results, with a stan-dard deviation SD of ±0.5.

2.5. Factorial design of experiments

Response surface methodology (RSM) was used to optimize andinvestigate the influence of different process variables on the batchbiosorption process. The central composite design CCD was applied.The experimental runs were carried out according to a 25 full fac-torial design for five independent variables; stirring rate (A,rpm),process time (B,h), process temperature (C, ◦C), initial dye concen-tration (D, mg/L), and biosorbent dosage (E, % w/v), with low (−1)and high (+1) levels. The total number of experiments is given bythe following formula:[50 = 2k + 2 (k) + 8], where k is the numberof independent variables (k = 5), this includes; 32 factorial pointfrom 42 full factorial CCD augmented with 8 replicates at the cen-ter point to assess the pure error. The response selected, was thepercentage dye removal. The levels were selected, based on prelim-inary study results. The design factors with low (−1), high (+1) andcenter (0) levels for the experimental design are listed in Table 1.

2.6. Statistical analysis

Once the experiments were preformed, the next step was to per-form a response surface experiment to produce a prediction modelto determine curvature, detect interactions among the design fac-tors (independent variables), and optimize the process, that is,determine the optimum values of independent variables leading tohigh biosorption percentage. The model used in this study to esti-mate the response surface is the quadratic polynomial representedby the following equation:

Y = ˇ0 + �ni=lˇixi + �n−1i=1 �

nj=i+1ˇij

xixj + �ni=lˇiix2i (2)

where, Y is the dye percentage removal, n is the number of factors,ˇ0 is the intercept term, ˇi, ˇij and ˇii are the linear, interactive and

quadratic coefficients, respectively, while, xi ′s are the levels of theindependent variables (factors) under study.

The statistical software Design Expert 6.0.7 (Stat-Ease Inc., Min-neapolis, USA) was used for design of experiments, regression and

N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202 195

Table 2The central composite design matrix for five coded independent variables along with experimental and predicted BB-41 dye removal percentage.

Runnumber

AStirring rate (rpm)

BTime(h)

C Temperature(◦C)

DInitial dyeconcentration (mg/L)

E Biosorbentdosage % (w/v)

Dye removal %

Experimental Predicted

1 −1 −1 +1 −1 +1 58.49 62.162 −1 −1 +1 +1 −1 46.25 49.843 −1 0 0 0 0 92.00 90.254 0 0 0 0 0 88.33 87.135 +1 +1 0 +1 +1 93.11 90.196 0 +1 0 0 0 84.50 85.507 0 0 0 0 0 88.00 87.138 −1 −1 −1 −1 +1 74.37 71.889 −1 −1 +1 +1 +1 72.67 68.2010 −1 −1 −1 +1 −1 71.49 75.0611 −1 +1 −1 −1 −1 83.84 80.9212 +1 +1 −1 −1 +1 72.87 73.1313 +1 +1 +1 +1 −1 82.48 85.6514 −1 −1 +1 −1 −1 59.22 57.4015 0 0 0 0 +1 86.10 89.0216 +1 +1 −1 +1 −1 87.50 84.8617 +1 −1 +1 −1 +1 61.41 62.0118 +1 0 0 0 0 95.69 96.2419 +1 −1 −1 +1 +1 91.56 92.6120 0 0 0 0 −1 84.62 83.9821 +1 +1 +1 -1 −1 88.86 91.0922 −1 +1 −1 -1 +1 77.78 78.6823 0 0 +1 0 0 78.44 80.0524 +1 −1 −1 -1 +1 65.35 66.0625 0 0 0 -1 0 79.45 82.2326 +1 −1 +1 +1 +1 77.85 79.6827 +1 −1 +1 -1 −1 61.29 63.2928 0 0 0 0 0 88.23 87.1329 +1 +1 +1 -1 +1 90.14 89.4330 +1 −1 +1 +1 −1 68.0 67.3531 −1 +1 +1 +1 +1 86.33 85.8532 −1 −1 −1 -1 −1 73.57 73.7433 0 0 0 0 0 88.13 87.1334 0 0 0 +1 0 87.48 86.9835 −1 +1 +1 +1 −1 69.56 67.8736 0 0 0 0 0 88.48 87.1337 0 −1 0 0 0 71.39 72.9538 +1 +1 −1 −1 −1 77.66 81.4139 −1 −1 −1 +1 +1 85.81 86.8140 0 0 0 0 0 88.33 87.1341 −1 +1 +1 -1 +1 91.71 89.3042 0 0 0 0 0 88.33 87.1343 −1 +1 −1 +1 +1 83.19 84.1144 −1 +1 −1 +1 −1 76.14 72.7545 +1 −1 −1 -1 −1 74.70 73.9646 0 0 0 0 0 88.36 87.1347 −1 +1 +1 -1 −1 88.12 84.9348 +1 +1 +1 +1 +1 95.32 97.6049 +1 −1 −1 +1 −1 84.16 86.90

gt

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50 0 0 −1

raphical analyses of the data obtained, and statistical analysis ofhe model to evaluate the analysis of variance (ANOVA).

.7. Preparation of acid TiO2 hydrosol

The TiO2 hydrosol was prepared according to Wang et al.8]; 1 mL titanium iso-propoxide was dissolved in 10 mL absolutethanol, and then the solution was added drop-wise into 30 mLoubly distilled water under a vigorous stir. After that, the sus-ension was kept stirring under 70 ◦C for about 45 min to ensureomplete hydrolysis. Then, 100 mL 0.04 mol/L HNO3 was added,nd the mixture was continuously stirred at 70 ◦C for 4 h in anir proof condition. Transparent hydrosol was obtained, and it was

iluted with water to 100 mL. The pH value of the hydrosol wasbout 2. The obtained hydrosol was examined by a double-beamASCO UV/vis/NIR spectrophotometer model V-570 and transmis-ion electron microscopy (TEM, Jeol Jem 2100F, 80 to 200 kV,

0 0 83.85 84.52

Japan). The phase identification was done using dispersive Ramanspectrometer (BRUKER-SENTERRA, Germany) equipped with anintegral microscope (Olympos). The particle size of TiO2 was deter-mined using dynamic light scattering DLS technique (Zetasizerversion 6.32, Malvern Instruments Ltd., UK).

2.8. Biosorbent regeneration

The dye-loaded biomass was added into 20 mL of the acid TiO2hydrosol. After stirring at room temperature for 20 min, most ofthe adsorbed dyes were desorbed and transferred into the acidTiO2 hydrosol solution system. The biomass was then separatedfrom the hydrosol by centrifugation, and washed in turn with NaOH

solution (0.01 M) and distilled water. The regenerated neutralizedbiomass was then used in the next adsorption experiment. TheTiO2 hydrosol containing the desorbed dye was used in the nextphoto-degradation experiment.

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96 N.Sh. El-Gendy et al. / Journal of Wa

.9. Photo-degradation of BB-41

The photo-catalytic reaction was carried out in a cylindricalyrex reactor. The TiO2 hydrosol containing BB-41 was irradiatedirectly under UV 254 nm (8 watt) VILBER-LOURMAT T-8C lamp atoom temperature 25 ± 1 ◦C. Samples were collected at prescribedime intervals to determine photo-degradation rate of the dye by

easuring the absorbance at �max 608 nm.

. Results and discussion

.1. Elucidation and validation of regression model

The CCD has the advantage that it permits the use of relativelyew combinations of variables for the determination of complexesponse function [9]. The statistically designed experiments weresed to obtain a quadratic model consisting of 32 trials plus 8 cen-er points involving five independent important variables; stirringate rpm (A), process time h (B), process temperature ◦C (C), ini-ial dye concentration mg/L (D), and biosorbent dosage wt%, w:vE), affecting the percentage dye removal. The objective of theata analysis is to fit a regression model equation, specify regres-ion coefficients and recognize the significant model terms, andnally determine the factors optimum levels which would lead to aaximum response, i.e. maximum percentage of dye removal. The

btained experimental results were evaluated and a second orderolynomial regression model equation relating the percentage dyeemoval and process parameters was developed and representeds follows:

= 87.82 + 2.19A + 7.18B − 2.46C + 2.44D + 2.75E + 6.48A2

−4.78B2 − 6.22C2 − 3.90D2 − 2.00E2 + 0.050AB + 1.22AC

+ 3.02AD − 1.31AE + 5.28BC − 2.49BD − 0.29BE − 2.51CD

+1.64CE + 3.69DE (3)

here, positive sign in front of the terms indicates synergetic effect,hereas negative sign indicates antagonistic effect.

Table 2 illustrates the statistical combinations of variables withhe experimental and predicted response values.

It was observed that within the studied variables range, the per-entage dye removal increased with increase of adsorbent dosage.his may be due to the increase in the available active surface sitesf the adsorbent, i.e. increase in active surface area. At low biosor-ent concentration; <0.4 wt%, the biosorption percent decreasedith increase of initial dye concentration, due to the saturation of

orption sites on biosorbent. But at higher biosorbent concentra-ion; >0.4 wt%, the biosorption percent increased with increase inhe initial dye concentration, this might be attributed to the largeumber of available vacant sites on the biosorbent surface for BB-1 molecules, which consequently would increase the driving forcef the concentration gradient between adsorbate in solution anddsorbate in the adsorbent [10,11]. The increase in stirring ratencreased to some extent the dye uptake especially at higher ini-ial dye concentration, indicating more effective contact betweeniomass and dye solution at higher stirring rate (100 rpm). Ong et al.12] reported that, a boundary layer is surrounding the biomassnd a decrease in its effect is observed with increasing the stirringate. The adsorption of BB-41 on spent waste biomass of S. cere-isiae decreased as the temperature increased from 25–35 ◦C, thisndicates that with rise in temperature, affinity of the binding sitesn the biomass for the dye decreases and the process seems to be

xothermic in nature.

The validity of the fitted model was evaluated and its statisti-al significance was controlled by F-test. The analysis of varianceANOVA) for the response surface full quadratic model is given in

cess Engineering 6 (2015) 193–202

Table 3. It can be indicated that the model is highly statistically sig-nificant at 95% confidence level, with F-value of 36.3 and very lowprobability p-value of <0.0001, i.e., there is less than 0.01% chancethat this error is caused by noise. The values of the determinationcoefficients, R2andR2

adjwhich measure the model fitting reliabil-ity for model (Eq. (3), were calculated to be 0.9612 and 0.9386,respectively. This suggests that, approximately 96.12% of the vari-ance is attributed to the variables and indicated a high significanceof the model. Thus, only 3.88% of the total variations cannot beexplained by the model which ensures the good adjustment of theabove model to experimental data. Confirmation of the adequacyof the regression model was reflected also by the good agreementbetween experimental and predicted values of response variablesas shown in Table 2. Where, the actual experimental biosorptionpercentage ranged from 46.25 to 95.69% and its corresponding pre-dicted values are 49.84 and 96.24%, respectively. “Adeq Precision”measures the signal to noise ratio. A ratio greater than 4 is desirable[13]. The ratio of 28.42 indicated an adequate signal. This model isreliable and can be used to navigate the design space. The standarddeviation SD and coefficient of variance were low recording 2.8 and3.46, respectively.

The relationship between the predicted and experimental val-ues of biosorption efficiency (% dye removal) is shown in Fig. 2a. Itcan be seen that there is a high correlation (R2 = 0.9701) betweenthe predicted and experimental values indicating that the pre-dicted and experimental values were in reasonable agreement. Itmeans that the data fit well with the model and give a convinc-ingly good estimate of response for the system in the experimentalrange studied.

Fig. 2b shows the normal probability plots of the standard-ized residuals for biosorption efficiency. The standardized residualsmeasure the number of standard deviations separating the actualand predicted values. A normal probability plot indicates if theresiduals follow a normal distribution, in which case the points willfollow a straight line. Since some scattering is expected even withthe normal data, as shown in Fig. 2b, it can be assumed that thedata is normally distributed. Thus, indicates a good validity for theapproximation of the quadratic regression model.

Fig. 2c shows standardized residual versus predicted values for% dye removal. In this research, points of observed runs were scat-tered randomly within the constant range of residuals across thegraph. Thus, it revealed no obvious pattern and unusual structure.i.e., the model is adequate and there is no reason to suspect anyviolation of the independence or constant variance assumption inall runs. The standardized residuals versus run plot represented inFig. 2d, shows randomly scattered points ranged between ±3; theerrors were normally distributed and insignificant.

Analysis of variance (ANOVA) of the regression model wascarried out to find the statistical significance of the main and inter-acting effects of different studied parameters on the biosorptionprocess at 95% confidence level. The significance of each coeffi-cient was determined by F-values and p-values (Table 3). The largerthe magnitude of the F-value and the smaller the p-values, themore significant is the corresponding coefficient. This implies thatthe main effect of stirring rate, process time, initial dye concen-tration and biosorbent dosage have a highly positive statisticallysignificant effect on dye removal (p < 0.0001), i.e., high dye removalefficiency occurs at high levels of those factors. But process tem-perature has a highly negative statistically significant effect ondye removal (p < 0.0001), i.e., a reduction in dye removal efficiencyoccurs at high temperatures. It is difficult to explain clearly theadsorption process with respect to a single factor, hence, overall

interactive effects of various studied parameters were consideredin the model to determine the dye removal response and opti-mization of multiple interacting factors was done to improve the

N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202 197

dation

baadtitlpotptotan

Fig. 2. Vali

iosorption process. The positive interactive effect of stirring ratend process time and the negative interactive effect of process timend biosorbent dosage have a statistically non-significant effect onye removal at 95% confidence limits (p > 0.1). The positive interac-ive effect of stirring rate and process temperature and the negativenteractive effect of stirring rate and biomass dosage have a statis-ically possible-significant effect on dye removal at 95% confidenceimits (p < 0.1). The positive interactive effect of the process tem-erature and biosorbent dosage has a statistically significant effectn dye removal at 95% confidence limits (p < 0.01). But the posi-ive interactive effect of stirring rate and initial dye concentration,rocess time and temperature and that of initial dye concentra-ion and biomass dosage have highly statistically significant effect

n dye removal (p < 0.0001). While the interactive effect of processime and initial dye concentration and that of process temperaturend initial dye concentration have highly statistically significantegative effect on dye removal (p < 0.0001).

of model.

The perturbation plot Fig. 3 shows the comparative effects ofall independent variables on the percentage dye removal (biosorp-tion efficiency). The curvatures show that within the studied rangeof parameters, the sensitivity of the dye removal toward the posi-tive synergetic effect of independent variables can be ranked in thefollowing decreasing order process time > biomass dosage ≈ initialdye concentration ≈ stirring rate. But high levels of process tem-perature have antagonistic effect on dye removal.

3.2. Response surface methodology to study the interactve effectsof process variables

Three-dimensional response surface graphical diagrams of theregression Eq. (3) were plotted to understand the interactive rela-tionship between the independent variables and % dye removal (i.e.biosorption %).

198 N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202

Table 3Analysis of variance of the fitted quadratic regression model Eq. (3).

Source SSa dfa MSa F-value p-value Remarks

Model 6.00E + 003 20 300 36.3 <0.0001 Highly significantA 164 1 164 19.8 0.000116 Highly significantB 1.75E + 003 1 1.75E + 003 212 <0.0001 Highly significantC 206 1 206 24.9 <0.0001 Highly significantD 202 1 202 24.5 <0.0001 Highly significantE 257 1 257 31.1 <0.0001 Highly significantA2 104 1 104 12.6 0.00135 SignificantB2 56.5 1 56.5 6.84 0.0140 Possibly significantC2 95.7 1 95.7 11.6 0.00196 SignificantD2 37.6 1 37.6 4.55 0.0414 Possibly significantE2 9.94 1 9.94 1.20 0.282 Non significantAB 0.0810 1 0.0810 0.00981 0.922 Non significantAC 47.7 1 47.7 5.78 0.0229 Possibly significantAD 291 1 291 35.3 <0.0001 Highly significantAE 55.1 1 55.1 6.67 0.0151 Possibly significantBC 893 1 893 108 <0.0001 Highly significantBD 198 1 198 23.9 <0.0001 Highly significantBE 2.72 1 2.72 0.329 0.570 Non significantCD 202 1 202 24.5 <0.0001 Highly significantCE 85.7 1 85.7 10.4 0.00314 SignificantDE 437 1 437 52.9 <0.0001 Highly significantResidual 239 29 8.26Pure error 0.16 7 0.022Corrected total 6240.74 49

a SS: sum of squares, df: degree of freedom, MS: mean square.

ip0pedttwbtt

time and temperature 100 rpm, 12 h and 30 C, respectively. The

Fig. 3. Perturbation plot for percentage dye removal.

Fig. 4a represents the interactive effect of stirring rate rpm andnitial dye concentration mg/L on the dye removal, at constantrocess time, temperature, and biomass dosage of 12 h, 25 ◦C and.4%, respectively. It is obvious from the contour lines and the RSMlot that it is a highly statistically significant positive interactiveffect. Where, under static conditions, the biosorption was low andecreased at high initial dye concentration. However, the biosorp-ion percentage increased with increase of stirring rate. Not onlyhis, but at high stirring rate; 100 rpm, the dye removal increased

ith increase of initial dye concentration, recording maximum

iosorption percentage of ≈ 97% at 200 mg/L. Fig. 4b representshe highly positive interactive effect of process temperature ◦C andime on dye removal at constant stirring rate, initial dye con-

centration and biomass dosage of 100 rpm, 150 mg/L and 0.4%,respectively. It is obvious from Fig. 4b that biosorption percentageincreased with increase in process time and temperature reach-ing its maximum ≈97% within 25–30 ◦C and 20 h, but it decreasedwith further increase in temperature. The rate of biosorption wasnearly sustained within 14–20 h, indicating equilibrium. Fig. 4crepresents the RSM plot and contour lines of the highly statis-tically significant negative interactive effect of process time andinitial dye concentration on biosorption process at constant stir-ring rate 100 rpm, process temperature 25 ◦C and biomass dosage0.4%, respectively. At short process time, the biosorption was low;however, it increased with time reaching equilibrium within 12 hand remained nearly sustained between 14–20 h. The maximumbiosorption percent ≈97% was achieved at initial dye concentrationof 150 mg/L within 12 h and remained nearly sustained thereafter,but further increment of initial dye concentration decreased thebiosorption percentage, even at long process time. The RSM plotand contour lines, Fig. 4d, confirm the highly statistically negativeinteractive effect of process temperature and initial dye concen-tration on biosorption % at constant stirring rate 100 rpm, processtime 12 h and biomass dosage 0.4%. At low temperature 15 ◦C andinitial dye concentration 100 mg/L the biosorption was relativelylow recording ≈ 78%. But it increased with increase in temperaturereaching its maximum between 25 and 30 ◦C and decreased withfurther temperature increment. Generally, at high initial dye con-centration >150 mg/L the biosorption decreased. Fig. 4e illustratesthe positive interactive effect of process temperature with biomassdosage at constant stirring rate 100 rpm, process time 12 h andinitial dye concentration 150 mg/L. It is obvious that dye removalwas low at low biosorbent concentration and temperature, but,increased with the increase of temperature and biomass dosage,recording ≈97% dye removal at ≈25 ◦C and 0.4%, respectively. How-ever, the increase in temperature; >30 ◦C lower the biosorptionefficiency. Fig. 4f represents the interactive effect of initial dye con-centration and biosorbent dosage at constant stirring rate, process

◦

RSM plot represents a highly statistically significant positive inter-active effect, which is also revealed by the contour lines. It is obviousthat at low biomass the biosorption was low, but with the increase

N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202 199

Fig. 4. Response surface and contour plots of biosorption process.

200 N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202

Table 4Optimum conditions solutions for BB-41 biosorption by spent waste biomass of Saccharomyces cerevisiae.

Factors Desirability Biosorption % Perecent error Standard deviation

Number of trials A(rpm) B(h) C(◦C) D(mg/L) E(wt%) Predicted Experimental

1 100 18 30 200 0.6 1 100% 98% 2% 0.0230942 0 14 20 150 0.6 1 96% 94% 2.08%2 0 18 25 150 0.5 1 96% 94% 2.08%

Fig. 5. UV–vis spectrophotometer scan (a), Raman spectrum (b), size distribution number (c), TEM image (d) of the TiO2 hydrosol and SEM image of the biosorbent (e).

N.Sh. El-Gendy et al. / Journal of Water Process Engineering 6 (2015) 193–202 201

Table 5Efficiency of spent waste biomass of Saccharomyces cerevisiae for biosorption of different dyes.

Dye Operation conditions Biosorption percentage References

Basic Blue 41 150 mg/L initial dye concentration, 0.6 wt%biomass dosage, 14 h process period, 20 ◦Cprocess temperature, initial pH 7, under staticcondition and contact solution of 100 mL.

Biosorption percentage 94%Biosorption capacity 23.5 mg/g

This work

Reactive Red 88 100 mg/L initial dye concentration, 0.6 wt%biomass dosage, 13 h process period, 15 ◦Cprocess temperature, initial pH 7, 90 rpm andcontact solution of 500 mL.

Biosorption percentage 62%Biosorption capacity 51.7 mg/g

[11]

Acid Red 14 120 mg/L initial dye concentration, 0.6 wt%◦

Biosorption percentage 69% [14]

ora

3

muotD“wpagpivluvrbw

rp

3

cspRw3rTbptm5itb

biomass dosage, 5 h process period, 25 Cprocess temperature, initial pH 7, 150 rpm andcontact solution of 100 mL.

f the biosorbent dosage; the percentage dye removal increasedeaching its maximum ≈97% at 200 mg/L initial dye concentrationnd 0.6% biosorbent dosage.

.3. Optimization of biosorption process

The optimization process was carried out to determine the opti-um value of maximum percentage of dye removal (biosorption %),

sing the Design Expert 6.0.7 software. According to the softwareptimization step, the desired goal for each operational condi-ion (A stirring rate, B incubation period, C process temperature,

initial dye concentration and E biomass dosage) was chosenwithin” the studied range. The response (biosorption percentage)as defined as maximum to achieve the highest performance. The

rogram combines the individual desirability into a single number,nd then searches to optimize this function based on the responseoal. Accordingly, the optimum working conditions with respectiveredicted biosorption percentage were established, and the exper-

mental results are illustrated in Table 4. The desirability functionalue was found to be 1.000 for these optimum conditions. Theaboratory experimental results agree well with the predicted val-es. The standard deviation and percent error were calculated foralidation of experiments. Recording average of ≈0.023 and 2.05%,espectively, indicating that process optimization by CCD was capa-le and reliable to optimize biosorption of Basic Blue-41 using spentaste biomass of S. cerevisiae.

The fermentation spent waste biomass of S. cerevisiae waseported as an effective biosorbent in different batch biosorptionrocesses (Table 5).

.4. Characterization of prepared TiO2

The prepared hydrosol showed a sharp peak at �300 nm, indi-ating the purity of the prepared titania hydrosol (Fig. 5a). Ramanpectra of the hydrosol TiO2 sample (Fig. 5b) revealed majoreaks at 70, 465, 563, 778.7 and 800 cm−1 [15]. These display theaman spectra of anatase and rutile TiO2. The main bands of waterere also detected at 1095, 2380, 2414, 3242, 3384, 3427 and

464–cm−1 [16]. The average size of the prepared TiO2 hydrosolecorded ≈11 nm, as depicted from the statistical analyses of theiO2 hydrosol using DLS technique (Fig. 5c). This was also confirmedy the TEM image of the sol particles (Fig. 5d), where a good dis-ersibility of the crystalline particles was obtained and the size ofhe sol particles was in the range of 6–15 nm. The scanning electron

icroscope SEM image of the yeast using SEM, JEOL-model JSM-

3000, Japan (Fig. 5e), showed that its diameter size was ≈5 �m,

.e. it is approximately several hundred times greater than that ofhe sol particles. Thus it was very easy to separate and collect theiosorbent and the photo-catalyst after the regeneration step.

Biosorption capacity 13.8 mg/g

3.5. Regeneration and reuse of biosorbent

In the viewpoint of energy consumption, process time and cost,the biosorption batch process was performed under static condi-tion, using the following selected optimum conditions; 150 mg/Linitial dye concentration, 0.6 wt% biomass dosage, 14 h processperiod, 20 ◦C process temperature. The maximum biosorptionpercentage of ≈94% with biosorption capacity of 23.5 mg/g wasobtained. The prepared acid TiO2 hydrosol (pH 2) showed good des-orption capacity. The elution efficiency was about 92%, and mostof the loaded dye was desorbed from the biosorbent and trans-ferred from the biomass surface into the acid hydrosol solution.The functional groups on the surface of the spent waste biomassof S. cerevisiae are carboxyl, hydroxyl and amide [17]. The sur-face of yeast would become positively charged at low solution pH,thus increasing the electrostatic repulsive force between the BB-41dye and the biomass. The biosorbent was easily separated by cen-trifugation, washed by NaOH solution (0.01 M) then distilled waterseveral times, until neutralization, and then reused in the next cycleof adsorption experiment.

When applying the regenerated biomass for removal of theremaining dye from the first batch, approximately complete dyeremoval occurred of ≈99% with total biosorption capacity of24.75 mg/g.

Upon applying, the regenerated biomass for new three succes-sive adsorption-desorption cycles of new batches with initial dyeconcentration of 150 mg/L, biosorption % recorded, 94, 90%, and55% with biosorption capacity of 23.5, 22.5, and 13.75 mg/g, respec-tively.

3.6. Photo-degradation of desorbed BB-41 with acid TiO2hydrosol

A batch photo-catalytic-decolorization of the desorbed dye(131 mg/L) was performed, under UV-illumination. The dyewas gradually degraded, recording ≈56.76% decolorization, after120 min. The pseudo-first-order kinetic equation (Eq. (4) wasapplied to determine the photo-degradation rate of the collecteddesorbed dye solution in a UV/TiO2 process.

ln(CoCt

)= Kappt (4)

where Co and Ct denote the BB-41 concentrations at t = 0 and t = t,respectively, and Kapp is the apparent pseudo-first-order rate con-

stant.

The kapp value of ≈0.0043 min−1 was obtained from the slopein the linear regression analysis of the photo-decolorization curvewith time (R2 0.9687).

2 ter Pro

4

coa(tBra

whtdipii

iarpd

R

[

[

[

[

[

[

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Oxide-based Materials, in: M. Anpo, P.V. Kamat (Eds.), Springer, New York,2010, pp. 1Pp153–184.

02 N.Sh. El-Gendy et al. / Journal of Wa

. Conclusion

This study proved that factorial design analysis based on centralomposite deign CCD of experiments and response surface method-logy RSM are reliable and powerful tools for modeling, optimizingnd studying the interactive effects of five important parametersstirring rate, process time and temperature, initial dye concen-ration and biosorbent dosage) in a batch biosorption process forasic Blue 41 dye using spent waste biomass of S. cerevisiae, thatecorded, maximum adsorption capacity of 23.5 mg/g and percent-ge dye removal of ≈94%.

The experiments showed that integration of the biosorptionith photo-catalytic oxidation using self-clean eluent; acid TiO2

ydrosol would be interesting for practical application, but detailedechno-economic analysis should be performed. Firstly, the cationicyes would be removed effectively from aqueous effluents by read-

ly available, low cost biosorbent, then the dye was desorbed andhoto-degraded by the same eluent; acid TiO2 hydrosol under UV-

llumination. Thus, would economize great volume of solvents andt would not bring secondary pollution to the environment.

However, there are still more details needed to be investigatedn further study; the changes occurred on the biomass surfacefter regeneration, optimization of the photo-degradation process,eusability of the hydrosol and the applicability of this integratedrocess for biotreatment of polluted water with different types ofyes.

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