microbialdecolorizationoftriazodye,directblue71:an...

Research ArticleMicrobial Decolorization of Triazo Dye Direct Blue 71 AnOptimization Approach Using Response Surface Methodology(RSM) and Artificial Neural Network (ANN)

Khairunnisarsquo Mohd Zin 1 Mohd Izuan Effendi Halmi 1 Siti Salwa Abd Gani2

Uswatun Hasanah Zaidan3 A Wahid Samsuri 1 and Mohd Yunus Abd Shukor 3

1Department of Land Management Faculty of Agriculture University Putra Malaysia Serdang 43400 Selangor Malaysia2Department of Agricultural Technology Faculty of Agriculture University Putra Malaysia Serdang 43400 Selangor Malaysia3Department of Biochemistry Faculty of Biotechnology and Biomolecular Sciences University Putra Malaysia Serdang 43400Selangor Malaysia

Correspondence should be addressed to Mohd Izuan Effendi Halmi m_izuaneffendiupmedumy

Received 10 July 2019 Revised 21 September 2019 Accepted 3 October 2019 Published 18 February 2020

Academic Editor Alfieri Pollice

Copyright copy 2020KhairunnisarsquoMohdZin et al0is is an open access article distributed under the Creative CommonsAttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

0e release of wastewater from textile dyeing industrial sectors is a huge concern with regard to pollution as the treatment of thesewaters is truly a challenging process Hence this study investigates the triazo bond Direct Blue 71 (DB71) dye decolorization anddegradation dye by a mixed bacterial culture in the deficiency source of carbon and nitrogen 0e metagenomics analysis foundthat the microbial community consists of a major bacterial group of Acinetobacter (30) Comamonas (11) Aeromonadaceae(10) Pseudomonas (10) Flavobacterium (8) Porphyromonadaceae (6) and Enterobacteriaceae (4) 0e richest phylumincludes Proteobacteria (7861) followed by Bacteroidetes (1448) and Firmicutes (308) 0e decolorization process op-timization was effectively done by using response surface methodology (RSM) and artificial neural network (ANN) 0e ex-perimental variables of dye concentration yeast extract and pH show a significant effect on DB71 dye decolorization percentageOver a comparative scale the ANN model has higher prediction and accuracy in the fitness compared to the RSM model provenby approximated R2 and AAD values 0e results acquired signify an efficient decolorization of DB71 dye by a mixedbacterial culture

1 Introduction

Utilization of dye by major textile industries in their pro-duction delivers a huge volume of dye effluent and this makesup about two-thirds of the total amount of dye production0e challenges in taking care of the textile effluent properly aregreater structure variability excessive concentration of colorand 10 being lost in coloration procedure as well as dyes willbe instantly released into the aqueous effluent about 2 [1]0e causes of turbidity an awful image and bad smell of waterare due to the colloidal matter found besides colors and oilyimpurities in the dye as the photosynthesis process is disrupteddue to the penetration of sunlight being blocked [2] 0e most

severe effect of textile waste towards marine creatures causes alack of dissolved oxygen in the water Eventually the self-purification process of water is stopped [3]

Additionally decreased soil efficiency can be seen oncethese effluents pass in the fields and clog the soil poresSolidified soil causes the penetration of roots to be held back0e sewerage tubes become rusted and encrusted due to thewastewater which runs inside the drains causing the changesin the grade of drinking water in hand pumps rendering itnot fit for human uptake [3]

Incorporation of significant amounts of water andchemicals in the staining operations of the textile industryleads to environmental pollution and the formation of a

HindawiBioMed Research InternationalVolume 2020 Article ID 2734135 16 pageshttpsdoiorg10115520202734135

complex toxic and recalcitrant residual [4] 0e textileeffluent needs high chemical oxygen demand and colorationwith the occurrence of dyes pigments and additionalchemicals which make the effluent require particular han-dling [5] Innovative methods were explored to attenuate theenvironmental harm that may induce the removal of textiledyes within industrial effluents Azo dye made up of one ormore -NN- double bond amounts to 60ndash70 of dyeproduction [6 7] Physical and chemical methods includingflocculation adsorption and photochemical oxidation aregreat options for the decolorization of printing and dyeingwastewater (PDW)

Several physical and chemical methods have been ap-plied to treat Direct Blue 71 dye including Fentonrsquos oxi-dation [8] ozonation [9] ultrasound [10] and adsorption[11] 0e release of secondary pollutants and large operatingcosts have become the main drawbacks of this method 0ebiological practice offers the aspects of cost-effective andsimple operations in contrast to physical and chemicalmethods and is currently widespread in the dye treatmentmethod [12] Microorganisms are being intensively studiedregardless of physical and chemical options available [13]Several works with the applied of fungi and bacteria havealready been designed to develop bioprocesses meant fortreating textile effluents

At the moment the majority of isolated bacteria requireanaerobic growing conditions to degrade azo dyes [14]Nonetheless the functional group associated with the azo dyemakes up a complex composition and influences the capa-bilities of the bacteria 0erefore choosing the best azo dyedecolorizing bacteria is critical Often the deposition of toxicmutagenic and carcinogenic aromatic amines is coupled withdecolorization of the azo dyes which are tolerant to degra-dation in anoxic conditions and disrupt the food chain as itaccumulates [15 16] 0us a key for the complete removal ofazo dye from the environment is the azo dye full degradationbesides only the elimination of color [17]

Presently both response surface methodology (RSM)and artificial neural network (ANN) tools are used for theoptimization and modeling of environmental researchRelationship determination among experimental variablesand responses use response surface methodology (RSM)Besides that this great technique is able to depict the mainand interaction effects Particular experimental designmixtures are used in RSM to build mathematical modelswith linear quadratic and interaction terms by a providednumber of elements and response factors to get optimumoperation [18] Azila et al [19] and Amini et al [20] havereported studies in environmental issues in applying theRSM technique

One of the best tools for modeling nonlinear andcomplex conditions is undoubtedly artificial neural net-works (ANNs) out of various multivariate statistical tech-niques [21] It is a strong modeling tool because of itsflexibility to extend and learn the response of all nonlinearand complex processes ANN can be effectively used in themodeling of many operations as applied by Prakash et al[22] and Yetilmezsoy and Demirel [23] A major cut in thenumber of experiments as well as the information on

singular or combination effects related to the independentvariables is possible by using multivariate statisticalmethods [20] 0is leads to the optimization and growth ofthe operating system noticeably lowering the expense oftrials 0e response surface methodology (RSM) and arti-ficial neural network (ANN) are the most frequently utilizedmethods in research on dye decolorization literature 0esetwo are strong data modeling tools concerning independentvariables and responses of the system to gain and depictcomplex nonlinear interactions

Different kinds of optimization for the handling ofenvironmental pollution have implemented RSM and ANNtechniques due to its utmost precision [24 25] Predictionmodels received from RSM and ANN have effectively op-timized the lead removal from industrial sludge leachate[26] and Singh [27] used the ANN practice for methyl reddecolorization in 24 hours by optimizing the design pa-rameters of a bacterial isolate ITBHU01

Abd ElndashRahim et al [28] worked on optimization ofmetal ion concentration pH amount of adsorbent andtemperature using RSM and ANN which is designed tooptimize the chromium (VI) abatement by working withcyanobacteria Similarly Astray [29] worked on optimizingthe capabilities of cyanobacteria in optimum conditionpredicted by RSM and ANN to degrade and decolorizedistillery spent wash Furthermore RSM and ANN ap-proaches were also tremendously employed in diverse fieldsfor example a simulation model of ventilated room withthermal effects [30] predicting the suitability of sugar beetpulp to make oligosaccharides [31] optimizing the copperremoval from synthetic wastewater by using electro-coagulation system [32] modeling the quality parameters ofspray-dried pomegranate juice [33] predicting cold watertemperature in a forced draft cooling tower [34] and cre-ating a stable oil-in-water for emulsification process as wellas decreasing the number of trials time and cost [35]Comparison between RSM and ANN techniques was alsodone by Ravikumar [36] and Sen [37] for their predictionand optimization abilities

In former studies the mixed bacterial cultures weregrown in rich media supplemented with a carbon source inanaerobic conditions to get the highest decolorization ac-tivity [38] Yet these complex laboratory substrates wouldnot be suited for in situ application It was discovered thatANN has a higher prediction ability with minimum error[36] and higher accuracy compared to the RSM model [37]Even so a method for mixed bacterial decolorization ofDirect Blue 71 dye by using both RSM and ANN techniqueshas not been discovered yet Hence the main motivationbehind this study is to optimize the bioremediation of DB71dye decolorization by using both RSM and ANN techniquesdespite the absence of carbon and nitrogen source for thedecolorization process

2 Materials and Methods

21 Dyes Chemicals and Culture Media Direct Blue 71 dye(CI 34140 dye content 50) used in this study was pur-chased from Sigma-Aldrich Chemical Co USA 10 g of dye

2 BioMed Research International

powder was added to 1 L of deionized water for the stocksolution of dye Only analytical grade chemicals or reagentswere used in this study 0e isolation of dye-degradingmixed bacterial culture used minimal salt medium (MSM)(gL (NH4)2SO4 04 KH2PO4 02 K2HPO4 04 NaCl 01Na2M0O4 001 MgSO47H2O 01 MnSO4H2O 001Fe2(SO4)3H2O 001 and yeast extract 1) whereas glucose(1 wv) was used for the test of carbon source

22 Soil Sampling Samples were collected randomly frompolluted and nonpolluted water soils and sludge sites inMalaysia Water and soil samplings were collected in March2018 from a depth of 8ndash10 cm from the water soil and sludgesurfaces 0e samples were stored in 50mL centrifuge tubesand capped on during the transfer from the site to the lab-oratory All samples were then stored at minus 20degC0e location ofthe samples was recorded based on the map coordinatesprovided by a global positioning system (GPS) locator

23 Isolation of Direct Blue 71 Dye-DegradingMixed Culture0e soil or sludge (1 g weight) or water (1mL) samples weremixed in 50mL minimal salt medium (MSM) under threedifferent mediums in which MSM was added with glucose as acarbon source or without glucose and ammonium sulfate orMSM only with all the mediums supplemented with 50mgLDirect Blue 71 dye All three mediums were exposed at roomtemperature in two different conditions which were incubatedon a rotary shaker (150 rpm) and also a static condition for fourdays All the different conditions were necessary to find out thebest conditions that fit with the mixed bacterial culture 0en1 of the culture was transferred into 20ml of the freshmedium of the same conditions as before in a universal bottleuntil decolorization was observed After 7 times subculturing1ml cultures were taken out aseptically and underwent serialdilutions 0en 50μl of the diluted aliquots was spread on theDirect Blue 71 dye agar in Petri dishes based on the conditionthat the mixed cultures preferred referring to the addition ofglucose or MSM only or without glucose and ammoniumsulfate0e plates were incubated for one to three days until thebacterial colonies were visible

24 Screening of Direct Blue 71 Dye Mixed Culture MSM of50mgL Direct Blue 71 dye was used to grow the isolate withor without glucose as a carbon source or without bothglucose and ammonium sulfate and then exposed to twoconditions which were static and shaken conditions for atleast 4 days to screen for the highest degradation of DirectBlue 71 dye After 4 days 1ml aliquots of culture fluid werepipetted out and centrifuged Suspended particles and cellswere eliminated from withdrawn samples by centrifuge(12000 rpm) for 10 minutes to avoid absorbance readingserror 0e decrement in absorbance was measured from thesupernatants obtained after centrifugation with uninocu-lated dye serving as a control relative to the dye relevantwavelength UV-visible double-beam spectrophotometer at587 nm wavelength was used to monitor the absorbance ofthe original and treated samples and the blank was the

uninoculated medium without the dye All tests were per-formed in triplicate and calculation involved the averagevalues obtained from the entire test 0e decolorizationpercentage of DB71 was done as follows

decolorization() Ab0 minus Ab1( 1113857

Ab01113890 1113891 times 100 (1)

where Ab0 is the dye solutionrsquos initial absorbance and Ab1 isthe dye solutionrsquos final absorbance after decolorizationNext testing with different dye concentrations ranging from50 ppm to 150 ppm was done for secondary screeningIsolates with the highest percentage of decolorizationconstant degrading of the dye for all subculturing candegrade high concentration of dye and possessed specialcharacteristics were chosen from the isolates that requiredneither glucose nor ammonium sulfate as their carbon andnitrogen source to live and degrade the Direct Blue 71 dye

25 Metagenomics Analysis Metagenomics of microorgan-isms applies towards discovering collections of genomes out ofa mixed population of microbes in non-culture-driven meth-odology [39] Studies regarding variations in bacterial and viralcommunities coming from diverse ecosystems were a successthrough genomic studies in which total DNA extraction forsamples of selected mixed bacterial culture was prepared toamplify the microbial sequences [40] 0e extracted DNA canthen be analyzed for metagenomics to determine the microbialcommunities in the sample that is accountable to degradeDirect Blue 71 dye Metagenomics analysis of the mixedbacterial culture was carried out by Apical Scientific Sdn Bhd

26 Optimization of Decolorization Using Response SurfaceMethodology (RSM) Apractical design by RSMwas proposedfor modeling and analysing tasks holding different parametersassociated with mathematical and statistical methods as well asto optimize amethod with a practical utilization of the resourceelements [41] It is also designed for projecting the functionalrelationship involving experimental parameters and the re-sponse [42] 0e optimum levels of three significant param-eters including dye concentration yeast extract and pH wereidentified through the RSM approach besides determining therelationship concerning the input parameters and the responsefunctions Design-Expert 608 was used to evaluate the ac-quired outcomes Model evaluation was done by comparingthe RSM predicted values with the experimental values [43]Evaluation of the experimental results was done through anumber of regressions and the F test was calculated to obtainthe significance of the regression value [44] Values of R2(coefficient of determination) and adjusted R2 must be near to10 for a good relationship for the model which concerns boththe predicted and experimental values [45] 0e regressioncoefficients obtained from the regression model are helpful forresponse surface plotting [46]

27 Optimization of the Significant Parameter UsingBoxndashBehnken Design 0e BoxndashBehnken design was used tooptimize the decolorization of Direct Blue 71 dye in RSM 0e

BioMed Research International 3

process includes the combination of treatment at themidpointsand center which are unbiased rotatable or almost rotatablequadratic design [47] Contrary to the other RSM models thisdesign requires less experimental tests and a shorter period[48] 0erefore it gives an extra economical approach Nextstatistical analysis of the acquired outcomes was done by usingDesign-Expert 6010 Stat-Ease Inc Minneapolis USA [49]Dye concentration (A) yeast extract (B) and pH (C) were theindependent variables and the design generated 17 total ex-periments in which the percentage of decolorization is theresponse An ideal model ought to have insignificant lack of fitand a significant model0e significant terms were determinedfor every response With respect to three parameters (n 3) asshown in Table 1 and limits which were an upper limit and alower limit the total number of the test was 17 0e responsewas determined as the percentage of dye decolorization Inorder tominimize the variability which was uncontrollable as aresult from the observed responses the experiment was done ina randomized method [50] 0e model was evaluated fromstatistical significance obtained from analysis of variance(ANOVA) and response surface plot and regression equationwas analyzed to acquire the optimal values

28 Optimization of Direct Blue 71 Decolorization UsingArtificialNeuralNetwork (ANN) Several learning algorithmswere used to train the networks by using NeuralPower version25 Only one hidden layer was applied to identify the optimalnetwork topology and several networks were established byconstant identification of the transfer function and thenumber of neurons in this layer 0e network was trained

until the network root of mean square error (RMSE) waslower than zero the average determination coefficient (DC)and average correlation coefficient (R) were 1 Training startedby random values and over the training process it was ad-justed to reduce network error [51] Table 2 shows two setswhich were unbold training dataset and bold testing datasetand the validating set was the experimental and predictedvalues of ANN at optimum conditions

29 ResidualAnalysis (ErrorAnalysis) Comparison betweenpredicted response from the RSM and ANN models wasdone to assess the dependability of the estimation potentialof the applied methods [52] A modelrsquos accuracy cannot bedependent only on R2 as calculated using equation (2)0erefore the implementation of AAD analysis was re-quired as an immediate practice for explaining the devia-tions by using equation (3) R2 and AAD were calculatedrespectively by using the following equations

R2

1 minus1113936

ni1 model predictioni minus experimental valuei( 1113857

2

1113936ni1 model predictioni minus experimental valuei( 1113857

2

(2)

AAD 1113936

p

i1 yiexp minus yical1113872 1113873yiexp1113872 1113873

p

⎧⎨

⎩

⎫⎬

⎭ times 100 (3)

where yiexp were the experimental responses and yical was themeasured responses the number of experimental runs wasdenoted by p and the number of the experimental data wasdenoted by n Accuracy of the chosen model was confirmed

Table 1 Lower limit and upper limit of BoxndashBehnken Design

Parameters Unit Lower limit Upper limitDye concentration ppm 50 150Yeast extract gL 05 3pH 6 75

Table 2 BoxndashBehnken matrix for experimental design and predicted response using RSM and ANN

Run A dye conc (ppm) B yeast extract (gL) C pH (gL) Decolorization () Predicted RSM () Predicted ANN ()1 100 175 675 9097 8984 893562 100 175 675 8715 8984 893823 50 3 675 949 941 920764 150 3 675 8986 9209 898625 150 175 6 7703 7487 77036 150 05 675 5443 5523 581187 100 3 6 8869 8862 886898 100 05 75 6618 6625 66189 150 175 75 7701 7614 7849910 100 175 675 8857 8984 8934711 100 3 75 898 8844 8979912 50 05 675 7906 7683 790613 100 05 6 5531 5667 6154714 50 175 6 8238 8325 8238115 100 175 675 9072 8984 8932816 100 175 675 9177 8984 9035617 50 175 75 8922 9138 89222ANN training datasetmdashunbold numbers ANN testing datasetmdashbold numbers


through R2 and AAD value analysis in which R2 is close to 1and a small AAD value is obtained [53 54] 0is implies thatthe actual behavior of the system was successfully describedby the model equation with the best values of R2 and AADvalues [53]

210 Determination of Optimum Point Desirability functionmethod was used for identifying the predicted optimum stateby RSM and ANN Each parameter requires desires andpriorities to be involved in this method to develop a processfor identifying desirability of the responses and the rela-tionship between the percentage of dye decolorization on eachparameter [48] Variables of dye concentration yeast extractand pH were fixed at maximums at the trial level [55 56]

3 Results and Discussion

31 Isolation Screening and Identification of Mix CultureUsing Metagenomics Analysis 0is study was conducted toisolate and screen a potent Direct Blue 71 dye decolorizingmix culture from Malaysian soil samples All thirty samplesthat were collected were tested with three different mediasupplemented with DB71 dye in which the carbon sourcecame from the glucose while the nitrogen source came fromthe ammonium sulfate before being put under static andshaking conditions Mix culture from the soil from Tasik SriSerdang (N 3deg00prime158Prime E 101deg42prime489Prime) has been selectedas the most promising culture that is able to degrade DirectBlue 71 dye because of its constant decolorization even afterseven times subculturing and can degrade higher concen-tration of dye compared to the other samples Furthermorethis chosen mix culture shows an excellent ability to survivewithout both carbon and nitrogen sources in static condi-tions leaving yeast extract in MSM as the sole organic ni-trogen source for the mixed culture Numerous azo dyedecolorization research studies were achieved in the exis-tence of supplemental carbon and nitrogen sources Nev-ertheless the added carbon source was chosen by the cellover the dye compound which made the decolorization bysupplementing carbon source turn out to be much less ef-fective [57] Ultimately this study successfully isolated mixbacterial culture that effectively uses the dye as a carbonsource and decolorizes the dye efficiently without externalcarbon and nitrogen sources

0e receiver of reduced electron carriers between oxygenand azo compounds in aerobic conditions caused compe-tition between those two compounds and it might be thefactor for successful decolorization in static conditions 0isreaction was the same as Handayani et al [58] who pub-lished the decolorization by Enterococcus faecalis for Re-active Red 2 and Acid Red 27 in a batch system Disruptionin the complex form of enzyme molecules from mechanicalshake was so much that deactivation arises Mechanicalfrailtyrsquos trait of enzymes in shaken condition causes theincrement in oxygen transfer rates and substrate masstransfer [59] Hence dye decolorization in the anaerobiccondition is a serendipitous process as the electron acceptorwould be the dye [60]

0e mixed culture was adapted to high dye concentra-tions as they were collected from contaminated sites near thelake at Taman Tasik Sri Serdang Similarly Chen et al [38]reported the isolation and screening of microorganismsfrom sludge samples that are able to decolorize several azodyes 0e samples were obtained from a wastewater treat-ment plant and lake mud Microbial decolorization of toxicdyes relies upon the adaptability as well as the reaction of thechosenmicroorganisms towards dyes [28] Oxygen curbs theazoreductase enzyme responsible for microbial decoloriza-tion of dye because of the match between electron acceptorfor the oxidation of NADH which are the oxygen and azogroups [61] Just a small quantity of oxygen is transferredmost likely on the broth surface in static incubation leadingto decolorization in anaerobic conditions being carried outby the cells deposited towards the bottom of the flasks [62]

Metagenomics analysis of themixed bacterial culture showsAcinetobacter was the dominant bacterial group followed byComamonas Aeromonadaceae Pseudomonas FlavobacteriumPorphyromonadaceae and Enterobacteriaceae as representedin Figure 1 respectively 30 11 10 10 8 6 and 40e most abundant phylum was Proteobacteria (7861) fol-lowed by Bacteroidetes (1448) and Firmicutes (308) Astudy by Ghodake et al [63] reveals that Acinetobacter wasidentified to decolorize 20 diverse textile dyes of various classes

30

11

1010

8

6

4

4

2

2 2

2 1

1

1

1

11 1

Microbial community

AcinetobacterComamonasAeromonadaceaePseudomonasFlavobacteriumPorphyromonadaceaeUnassignedEnterobacteriaceae

ComamonadaceaeGammaproteobacteriaPseudomonadaceaeClostridiumRhodocyclaceaeProteobacteriaPseudomonadales

Figure 1 Microbial community found in the mixed bacterialculture


Some Acinetobacter strains as a biocatalyst has been applied toremediate several environmental pollutants along with bio-technological uses reported by Abdel-El-Haleem [64] 0e ef-fluent-adapted strain of Acinetobacter has the potentiality forcolor removal and strains of Acinetobacter and Pseudomonasexhibit chemical oxygen which demands removal actions[64 65] Besides that the Comamonas strain retains amazingreusability and endurance traits in continuous decolorizationprocesses [66] Additionally the genus of Pseudomonas in-cluding Pseudomonas putida and Pseudomonas alcaligenes has alot of metabolic diversity many of which were capable ofmetabolizing different chemical contaminants [67] Also it isreported that Proteobacteria was a dominant part of the mixedbacterial culture in anaerobic-baffled reactors useful to deal withindustrial dye wastewater [68]

32 Optimization of Direct Blue 71 Dye Decolorization UsingRSM In this study response surface methodology (RSM) wasused to optimize the decolorization of DB71 dye 0e responsesurface equation fromDesign-Expertreg can be optimized for thebest result considering a range of process variables 0e effectbetween the two parameters was shown on response plots andthe relationships were shown on contour by maintaining otherparameters specified at their best optimal conditions [69] Fromthese contour plots the relationship of one variable to the nextcould be seen Lastly the humpon the contour plots determinedthe optimum condition for every variable [70]

Optimization of anaerobic mixed bacterial culturedegrading DB71 dye with the absence of carbon and nitrogensource using RSM is a novel approach Bacteria are seldomable to decolorize azo compounds inMSM lacking in nitrogensource as stated by You and Teng [71] A lacto bacteriumdegradation performance is very slow when MSM-lackednitrogen source is used compared to the rapid degradation ofReactive Black 5 obtained only in one day within a mediumwith external nitrogen addition [71] However in comparisonwith a previous study the chosen mix culture for this study isextra applicable for in situ application as it does not needammonium sulfate that acts as the nitrogen source for anefficient DB71 dye decolorization

0e combined effect of significant parameters that in-cludes dye concentration (A) yeast extract (B) and pH (C) fordecolorization of DB71 dye was studied and these parameterswere optimized to acquire the highest decolorization per-centage using a BoxndashBehnken design with 17 experimentalruns Table 2 shows the experimental and predicted responsesby using RSM and later the responses were analyzed usingDesign-Expert 60 Stat-Ease Inc Minneapolis USA Table 2shows that dye decolorization by mixed bacterial cultureranged from 5443 to 949 at run 6 and run 3 respectively0emaximumdye decolorization (949) was obtained at dyeconcentration (A 50 ppm) yeast extract (B 3 gL) and pH (C675) in run 3 0e minimum dye decolorization (5443)obtained at dye concentration (A 150 ppm) yeast extract (B05 gL) and pH (C 675) was observed in run 6

33 Final Equation in terms of Coded Factors Analysis ofvariance (ANOVA) for the predicted RSM model is shownin Table 3 for DB71 dye decolorization 0e equation for theregression model was as follows

decolorization +8984minus 590lowastA + 1353lowastB + 235lowastC

+ 489lowastAB minus 172lowastAC minus 244lowastBC

minus 1930lowastA2minus 8340lowastB2

minus 649lowastC2

(4)

Table 3 shows a significant model for optimizing DB71dye decolorization with low probability value (F lt 00001)and F value of 4715 Only 001 chance of error due tonoise could occur to this F value Model terms were sig-nificant when pgt F values were less than 005 and modelterms were not significant when values were higher than010 [72] 0erefore in this study A B C AB BC B2 andC2 were significant model terms Adequate precisionshows an adequate signal for the model which was 21101PRESS shows the goodness of the model to predict theresponses in new experimentation which was 43507for this model 0e model fits the data as the value of 235for lack-of-fit F test was statistically not significant

Table 3 Analysis of variance (ANOVA) for the fitted quadratic polynomial model for optimization of Direct Blue 71 dye decolorization

Source Sum of squares df Mean square F value p value probgt FModel 244823 9 27203 4715 lt00001 SignificantA dye conc 27883 1 27883 4833 00002B yeast extract 14653 1 14653 25396 lt00001C pH 4418 1 4418 766 00278AB 9594 1 9594 1663 00047AC 1176 1 1176 204 01964BC 2381 1 2381 413 00817A2 1567 1 1567 272 01433B2 29316 1 29316 5081 00002C2 17772 1 17772 308 00009Residual 4039 7 577Lack of fit 2576 3 859 235 02138 Not significantPure error 1463 4 366Cor total 248862 16

R 2 09838Adjusted R2 09629


Determination coefficient (R2) was used to validatethemodelrsquos goodness of fit which was 0983 for this study andreasonable agreement with the predicted R2 (08252) indi-cated an adequate prediction for DB71 dye decolorization A

good prediction of amodel was implied by the closeness of theR2 value to 10 [73] Unreliable outcomes in the predictionanalysis could be prevented by having a fit model in anoptimization study

Color points by value ofdecolorization

949

5443

Internally studentized residuals

Nor

mal

p

roba

bilit

yNormal plot of residuals

ndash200 ndash100 000 100 200

1

5102030

50

70809095

99

(a)


949

5443

PredictedIn

tern

ally

stud

entiz

ed re

sidua

ls

Residuals vs predicted

ndash300

ndash200

ndash100

000

100

200

300

50 60 70 80 90 100

3

ndash3

0

(b)


949

5443

Run number

Inte

rnal

ly st

uden

tized

resid

uals

Residuals vs run

ndash300

ndash200

ndash100

000

100

200

300

1 3 5 7 9 11 13 15 17

3

ndash3

0

(c)


949

5443

Actual

Predicted vs actual

Pred

icte

d

50

60

70

80

90

100

50 60 70 80 90 100

(d)

Figure 2 Diagnostic plots showing (a) the studentized residuals plotted against the normal probability (b) the studentized residuals versuspredicted (c) the studentized residuals versus run and (d) the predicted response values plotted against the actual responses


A straight line of the studentized residuals plotted versusthe normal probability from Figure 2(a) shows that theexperimental data displayed a normal distribution No dataerror was found in the estimation of the model as all valueslie within the interval plusmn300 as shown in Figures 2(b) and2(c) Correlation coefficients (R2 and R2 adj) of 098 and 096correspondingly for decolorization of DB71 dye fit oneanother as is displayed through a plot of actual versuspredicted response values as shown in Figure 2(d) As aresult the established model was appropriate for predictingthe performance of dye decolorization within investigatedconditions

34 Optimization Using Artificial Neural Network A crucialselection of neural network topology was required for aneffective treatment 0erefore prediction of DB71 dye de-colorization requires the analysis of different neural networktopologies 0e best four ANN models were outlined basedon Table 4 One model from the list of models was chosen tolower the cost criterion of training a neural network model0e best option for the learning algorithm was batchbackpropagation (BBP) for the prediction of DB71 dyedecolorization (Table 4) Furthermore the neural networkrsquoslearning rate was influenced by the type of transfer functionused and the top model chosen acquired Tanh function attransfer function hidden and sigmoid for the transferfunction output

0e finest topology for the prediction of DB71 dye de-colorization consisted of 3-26-1 topology (Figure 3) andTable 4 shows that the best ANN model work was network

number 1 containing 26 nodes of optimization and Tanhtransfer function and required a multilayer normal feed-forward (MNFF) batch backpropagation (BBP) networkBoth training and testing datasets possessed R2 of 0999which presented reduced error as opposed to othernetworks

35DeterminationofOptimumPointbyUsingRSMandANN0e optimum level of various parameters obtained fromRSM optimization was DB71 dye concentration of 150 ppmyeast extract of 3 gL and pH of 6645 with an overall de-colorization of 922 as measured at 587 nm (Table 5) Tovalidate this the experiments were performed based on thepredicted optimum condition to compare the experimentaloutcomes with the predicted outcomes 0e average tripli-catersquos experimental value of decolorization was 8613compared to the predicted value of 922 decolorizationwith 658 deviation A previous study on DB71 dye de-colorization by solar degradation shows that greater dyeconcentration inhibits the dye decolorization and only about70 decolorization was obtained at 100 ppm [74] and only71 of DB71 color removal by P fluorescens D41 was ob-tained in the presence of glucose [75] In comparison themix bacterial culture was able to decolorize DB71 dye betterthan the conventional method and single bacterial isolatedue to the catabolic agreement between the microorganisms

0e optimum level of various parameters obtained fromANN optimization was DB71 dye concentration of 150 ppmyeast extract of 3 gL and pH of 6645 with an overall de-colorization of 899 For validation of optimum pointsusing ANN the average triplicatersquos experimental value ofdecolorization was 865 as compared to the predicted valueof 899 of decolorization with a deviation smaller thanRSM at around 378 0e ANN model could accurately fitwith the experimental data as the experimental value andANN predicted value show a close relationship Incrediblygood nonlinear fitting effects were obtained because themodel predicted values were very close to the actual values(Figure 4) R2 was close to 10 at 09859 and this shows thatANN gave a good prediction

36 =ree-Dimensional Analysis By keeping the value ofanother variable constant the impact of two variables atonce was evaluated via RSM 3D surface plot 0e contoursdepicted the optimal value of the variable that reveals theutmost DB71 dye decolorization (response) 0e effect ofyeast extract and pH on the percent of decolorization(Figure 5(a)) was highlighted through the 3D response

Table 4 0e effect of different neural network architecture and topologies on R2 and AAD in the estimation of DB71 dye decolorizationobtained in the training and testing of neural networks

Network Model Learningalgorithm

Connectiontype

Transfer functionoutput

Transfer functionhidden

Training setR2 DC Testing set

R2 DC

1 4261 BBP MNFF Sigmoid Tanh 0999 0980 0999 09502 4261 BBP MNFF Tanh Tanh 0990 0980 0980 08703 4261 BBP MNFF Tanh Sigmoid 0991 0982 0900 07904 4261 BBP MNFF Sigmoid Linear 098 097 099 0900

Input Output

Bias

Figure 3 Neural network topology 0e topology of multilayernormal feedforward neural network for the estimation of DB71 dyedecolorization


Table 5 Predicted and experimental value for the responses at optimum condition using response surface methodology (RSM) and anartificial neural network (ANN)

Model A dye conc (ppm) B yeast extract (gL) C pH RSMANN predicted (decolorization) Experimental validation DeviationRSM 150 30 6645 922 8613 658ANN 150 290 67 8990 8650 378

R2 = 09859

0

20

40

60

80

100

40 50 60 70 80 90 100

Pred

icte

d A

NN

Experimental

ANN

Figure 4 ANN predicted versus actual experimental data values for DB71 dye decolorization

Factor coding actualdecolorization ()

949

5443

663

6669

7275

051

152

253

40

50

60

70

80

90

100

Dec

olor

izat

ion

()

B yeast extract (gL)C pH (gL)

663

6669

7275

115

225

3

0

0

0


(a)

Figure 5 Continued


surface 0e result displayed that as the yeast extract in-creased the decolorization of dye also increased 0e pHshowed that decolorization increased as pH increased untilthe optimum condition was obtained Both surface plotsdemonstrated the optimum concentration of yeast extractand pH were obtained at 3 gL and pH 6645 respectivelyYeast extract was identified as an effective enhancer forpromoting higher decolorization performance 0e regen-eration of NADH during the reduction of azo dyes usingmicroorganisms releases the electron donors and yeastwhich is regarded as the organic nitrogen sources being a

vital media supplement in the process [76] As shown inTable 3 factor B (yeast extract) was a significant parametersince its p value was about 00001 which was much lowerthan 005

0e correlated effect of dye concentration and pH to dyedecolorization signified that as the dye concentration in-creased decolorization percent was likewise increased andas pH increased the decolorization increased as well beforeit reached the optimum point (Figure 5(b)) Both surfaceplots showed that the optimum concentrations of pH oc-curred at 6645 with the dye concentration of 150 ppm Any


949

5443

051

152

25

3

5070

90110

130150

40

50

60

70

80

90

100

Dec

olor

izat

ion

()

A dye concentration (ppm)

B yeast extract (gL)1

152

25

3

7090

110130

15

0

0

0

dye concentration (ppm)

B yeast extract (gL)

(b)


949

5443

Dec

olor

izat

ion

()

663

6669

7275

5070

90110

130150

40

50

60

70

80

90

100

A dye concentration (ppm)C pH (gL)

663

6669

7275

7090

110130

150

0

0

0

dye concentration (ppm)C pH (gL)

(c)

Figure 5 0ree-dimensional plots showing the effect of (a) pH and yeast extract (b)yeast extract and dye concentration and (c)pH and dyeconcentration and their mutual effect on the decolorization of Direct Blue 71 dye Other variables are constant pH (6645) yeast extract (3 gL)and dye concentration (150 ppm)


92

Dec

olor

izat

ion

()

90888684828078767472706866646260

05 0667 0833 1 117 133 15 167Yeast extract (gL)

183 2 217 23326725

283 3 50567

63370

767833

90967

Dye co

ncen

tratio

n (pp

m)

103110

117123

130137

143150

(a)

90

Dec

olor

izat

ion

()

898887868584838281807978

661

6263

6465

6667

pH68

69

74

771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued


adjustments in medium pH greatly affected some biologicalfunctions including enzymatic processes element transportover the membrane and the signaling pathways [77]Neutral initial pH was favored by the majority of bacteria forthe greatest growth and KMK48 bacterium can degradesulfonated azo dyes after only 36 hours in neutral pH [78]Maximum decolorization process at higher pH has also beenreported by halophilic bacteria out of genusHalomonas [79]and in contrast Georgiou et al [80] reported a slightly acidicpH of 66 for dye decolorization using acetate-consumingbacteria Accordingly bacterial cell metabolism and nutri-ents intakes were greatly affected by the surrounding me-dium pH

0e effect of dye concentration and yeast extract towardsDB71 dye decolorization was displayed on a three-dimensionalplot in which as the dye concentration and yeast extract in-creased the decolorization was also increased (Figure 5(c))Both surface plots indicated that the optimum concentrationsof dye concentration and yeast extract were gained at 150ppmand 3gL respectively 0e elliptical plot obtained showed thatthere is a relationship concerning these two parameters 0eincreased initial dye concentration caused a steady rise indecolorization capacity Dubin and Wright (1975) reported alack of any effect on decolorization rate due to different dyeconcentrations 0is observation was agreeable with a non-enzymatic decolorization process that was regulated by pro-cesses which were independent of the dye concentration [81]

0e way that the factors of dye concentration yeastextract and pH corresponded with the DB71 dye decolor-ization was presented on 3D response surface plots by ANN(Figures 6(a)ndash6(c))0e effect of dye concentration and yeastextract on the decolorization of dye is displayed inFigure 6(a) Gradual increase of decolorization was ob-served as the dye concentration increased and yeast extract(gL) increased until it reached the optimal point 0e studyby Chang and Lin [82] also signified that an adequate supplyof yeast extract is critical for the Pseudomonas luteola strainto achieve stability in the fed-batch decolorization processes0e 3D plot from Figure 6(b) shows the effect of pH andconcentration of dye on dye decolorization Based on theresult given that dye concentration and pH increased thedecolorization percent increased as well until it achieved theoptimum point Higher pH causes the decrement in de-colorization and maximum decolorization was observed atpH 67 A similar result has been reported before by Kapdan[83] that used mixed bacterial consortium to decolorizetextile dye and higher dye concentration inhibits the mi-crobial decolorization Next Figure 6(c) shows that thedecolorization increased as yeast extract increased and pHincreased until it reached the optimum point for the highestdye decolorization Yu et al [84] reported a significant effectof pH on dye decolorization because of the highest activity ofPseudomonas sp 0e strain was observed at a pH range of7ndash8 and 50 decrement in activity was observed when thepH was varied

37 Residual Analysis (Error Analysis) 0e dependabilityand accuracy of RSM and ANN models were evaluatedthrough a relative study [85] by using R2 to analyze themodelrsquos precision ability [54] A good model ought to haveR2 close to 10 Table 6 shows that ANN displayed a larger

Table 6 Absolute deviation R2 adjusted R2 and AAD of RSM andANN models

Residual analysis RSM ANNR 2 0980 0990Adjusted R2 0978 0989Absolute average deviation (AAD) 0045 0040

050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)

Figure 6 AAN response surface for (a) dye concentration versus yeast extract (b) dye concentration versus pH and (c) yeast extract versuspH with dye decolorization as a response


value of R2 (0990) compared to RSM (0980) but the ef-ficiency of the regression model does not only depend on R2

because additional evaluation factors such as AAD werehighly recommended to validate several models [86] A smallvalue of AAD which is close to zero shows a less chance oferror in prediction and was displayed as a good model ANNmodel (004) possesses a smaller value of AAD compared tothe RSM model (0045) as shown in Table 6 0us thecritically higher predictive and accuracy potential of theANN model compared to the RSM model was obtainedbased on the relative R2 and AAD values

4 Conclusions

0e mixed bacterial culture shows an amazing ability todecolorize Direct Blue 71 dyersquos triazo bond without thepresence of carbon and nitrogen sources in anaerobiccondition rendering it more applicable for in situ appli-cation A successful optimization was shown by using RSMand ANN techniques 8613 and 865 of validated de-colorization were obtained in optimized condition predictedby RSM and ANN subsequently at a concentration of150 ppm Furthermore a relative study on RSM and ANNcould cover several cons of each technique as well as em-phasize the error in the experimental data Hence a highervalue of R2 (099) and a lower value of AAD (004) show thatthe ANNmodel holds a better prediction for optimization ofthe dye decolorization compared to the RSM model

Nomenclature

AbbreviationsDB71 Direct Blue 71RSM Response surface methodologyANN Artificial neural networkAAD Absolute average deviationPDW Printing and dyeing wastewaterMSM Minimal salt mediumDNA Deoxyribonucleic acidANOVA Analysis of varianceGPS Global positioning systemMNFF Multilayer normal feedforwardBBP Batch backpropagationRMSE Root mean square error

Symbols3D 0ree-dimensionalNADH Electron carrierR 2 Coefficient of determinationrpm Revolutions per minuteyi exp Experimental responsesyi cal Measured responsesp Number of experimental runsn Number of the experimental data wv Percentage weight per volumep value Probability valueTanh Hyperbolic tangent

Data Availability

0e response surface methodology (RSM) and artificialneural network (ANN) data used to support the findings ofthis study are available from the corresponding author uponrequest

Conflicts of Interest

0e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

0is project was financed by funds from Putra Grant (GP-IPM20179532800) and Yayasan Pak Rasyid Grant UPM(6300893-10201)

References

[1] K Singh and S Arora ldquoRemoval of synthetic textile dyes fromwastewaters a critical review on present treatment technol-ogiesrdquo Critical Reviews in Environmental Science and Tech-nology vol 41 no 9 pp 807ndash878 2011

[2] S S Muthu Assessing the Environmental Impact of Textilesand the Clothing Supply Chain Elsevier Amsterdam Neth-erlands 2014

[3] R Kant ldquoTextile dyeing industry an environmental hazardrdquoNatural Science vol 4 no 1 pp 22ndash26 2012

[4] J Dasgupta J Sikder S Chakraborty S Curcio and E DriolildquoRemediation of textile effluents by membrane based treat-ment techniques a state of the art reviewrdquo Journal of Envi-ronmental Management vol 147 pp 55ndash72 2015

[5] K Lokesh and R Sivakiran ldquoBiological methods of dye re-moval from textile effluents-a reviewrdquo Journal of BiochemicalTechnology vol 3 no 5 pp 177ndash180 2014

[6] D Cui G Li D Zhao X Gu C Wang and M ZhaoldquoMicrobial community structures in mixed bacterial consortiafor azo dye treatment under aerobic and anaerobic condi-tionsrdquo Journal of Hazardous Materials vol 221-222pp 185ndash192 2012

[7] X Xie N Liu B Yang et al ldquoComparison of microbialcommunity in hydrolysis acidification reactor depending ondifferent structure dyes by Illumina MiSeq sequencingrdquo In-ternational Biodeterioration amp Biodegradation vol 111pp 14ndash21 2016

[8] N Ertugay and F N Acar ldquoRemoval of COD and color fromdirect blue 71 azo dye wastewater by Fentonrsquos oxidationkinetic studyrdquo Arabian Journal of Chemistry vol 10pp S1158ndashS1163 2017

[9] K Turhan and Z Turgut ldquoDecolorization of direct dye intextile wastewater by ozonization in a semi-batch bubblecolumn reactorrdquo Desalination vol 242 no 1ndash3 pp 256ndash2632009

[10] M M Tauber G M Gubitz and A Rehorek ldquoDegradation ofazo dyes by oxidative processesmdashlaccase and ultrasoundtreatmentrdquo Bioresource Technology vol 99 no 10pp 4213ndash4220 2008

[11] Y Bulut N Gozubenli and H Aydın ldquoEquilibrium andkinetics studies for adsorption of direct blue 71 from aqueoussolution by wheat shellsrdquo Journal of Hazardous Materialsvol 144 no 1-2 pp 300ndash306 2007


[12] UM E Schutte Z Abdo S J Bent et al ldquoAdvances in the useof terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbialcommunitiesrdquo Applied Microbiology and Biotechnologyvol 80 no 3 pp 365ndash380 2008

[13] N Kumaran and G Dharani ldquoDecolorization OF textile dyesBY white rot fungi Phanerocheate chrysosporium and pleu-rotus sajor-cajurdquo Journal of Applied Technology in Environ-mental Sanitation vol 1 no 4 2011

[14] J Garcıa-Montantildeo X Domenech J A Garcıa-HortalF Torrades and J Peral ldquo0e testing of several biological andchemical coupled treatments for cibacron Red FN-R azo dyeremovalrdquo Journal of Hazardous Materials vol 154 no 1ndash3pp 484ndash490 2008

[15] A Dos Santos I Bisschops and F Cervantes ldquoClosingprocess water cycles and product recovery in textile industryperspective for biological treatmentrdquo Advanced BiologicalTreatment Processes for Industrial Wastewaters vol 1pp 298ndash320 2006

[16] M Isık and D T Sponza ldquoAnaerobicaerobic treatment of asimulated textile wastewaterrdquo Separation and PurificationTechnology vol 60 no 1 pp 64ndash72 2008

[17] S Mohana S Shrivastava J Divecha and D MadamwarldquoResponse surface methodology for optimization of mediumfor decolorization of textile dye direct black 22 by a novelbacterial consortiumrdquo Bioresource Technology vol 99 no 3pp 562ndash569 2008

[18] N DeCarlo =e Complete Idiotrsquos Guide to Lean Six SigmaPenguin 2007

[19] Y Y Azila M Mashitah and S Bhatia ldquoProcess optimizationstudies of lead (Pb (II)) biosorption onto immobilized cells ofPycnoporus sanguineus using response surface methodologyrdquoBioresource Technology vol 99 no 18 pp 8549ndash8552 2008

[20] M Amini H Younesi and N Bahramifar ldquoApplication ofresponse surface methodology for optimization of lead bio-sorption in an aqueous solution by Aspergillus nigerrdquo Journalof Hazardous Materials vol 154 no 1ndash3 pp 694ndash702 2008

[21] T Rajaee S A Mirbagheri M Zounemat-Kermani andV Nourani ldquoDaily suspended sediment concentration sim-ulation using ANN and neuro-fuzzy modelsrdquo Science of theTotal Environment vol 407 no 17 pp 4916ndash4927 2009

[22] N Prakash S A Manikandan L Govindarajan andV Vijayagopal ldquoPrediction of biosorption efficiency for theremoval of copper(II) using artificial neural networksrdquo Journalof Hazardous Materials vol 152 no 3 pp 1268ndash1275 2008

[23] K Yetilmezsoy and S Demirel ldquoArtificial neural network (ANN)approach for modeling of Pb (II) adsorption from aqueoussolution by Antep pistachio (Pistacia Vera L) shellsrdquo Journal ofHazardous Materials vol 153 no 3 pp 1288ndash1300 2008

[24] L M Alvarez A L Balbo W P Mac Cormack andL A M Ruberto ldquoBioremediation of a petroleum hydro-carbon-contaminated Antarctic soil optimization of a bio-stimulation strategy using response-surface methodology(RSM)rdquo Cold Regions Science and Technology vol 119pp 61ndash67 2015

[25] D R Dudhagara R K Rajpara J K Bhatt H B Gosai andB P Dave ldquoBioengineering for polycyclic aromatic hydro-carbon degradation by Mycobacterium litorale statistical andartificial neural network (ANN) approachrdquo Chemometrics andIntelligent Laboratory Systems vol 159 pp 155ndash163 2016

[26] F Geyikccedili E Kılıccedil S Ccediloruh and S Elevli ldquoModelling of leadadsorption from industrial sludge leachate on red mud byusing RSM and ANNrdquoChemical Engineering Journal vol 183pp 53ndash59 2012

[27] A Tripathi et al Bioremediation of Hazardous Azo DyeMethyl Red by a Newly Isolated Bacillus MegateriumITBHU01 Process Improvement through ANN-GA BasedSynergistic Approach NISCAIR-CSIR New Delhi India2016

[28] W M Abd ElndashRahim H Moawad and M KhalafallahldquoMicroflora involved in textile dye waste removalrdquo Journal ofBasic Microbiology An International Journal on BiochemistryPhysiology Genetics Morphology and Ecology of Microor-ganisms vol 43 no 3 pp 167ndash174 2003

[29] G Astray B Gullon J Labidi and P Gullon ldquoComparisonbetween developed models using response surface method-ology (RSM) and artificial neural networks (ANNs) with thepurpose to optimize oligosaccharide mixtures productionfrom sugar beet pulprdquo Industrial Crops and Products vol 92pp 290ndash299 2016

[30] M S Bhatti et al ldquoRSM and ANN modeling for electro-coagulation of copper from simulated wastewater multiobjective optimization using genetic algorithm approachrdquoDesalination vol 274 no 1-3 pp 74ndash80 2011

[31] B-Y Chen S-Y Chen M-Y Lin and J-S Chang ldquoEx-ploring bioaugmentation strategies for azo-dye decolorizationusing a mixed consortium of Pseudomonas luteola andEscherichia colirdquo Process Biochemistry vol 41 no 7pp 1574ndash1581 2006

[32] P Kundu V Paul V Kumar and I M Mishra ldquoFormulationdevelopment modeling and optimization of emulsificationprocess using evolving RSM coupled hybrid ANN-GAframeworkrdquo Chemical Engineering Research and Designvol 104 pp 773ndash790 2015

[33] F Kuznik J Brau and G Rusaouen ldquoA RSM model for theprediction of heat and mass transfer in a ventilated roomrdquo inProceedings of the Building Simulation pp 919ndash926 BeijingChina September 2007

[34] M M Nourouzi T G Chuah and T S Y Choong ldquoOp-timisation of reactive dye removal by sequential electro-coagulationndashflocculation method comparing ANN and RSMpredictionrdquo Water Science and Technology vol 63 no 5pp 984ndash994 2011

[35] R Ramakrishnan and R Arumugam ldquoOptimization of op-erating parameters and performance evaluation of forceddraft cooling tower using response surface methodology(RSM) and artificial neural network (ANN)rdquo Journal ofMechanical Science and Technology vol 26 no 5 pp 1643ndash1650 2012

[36] R Ravikumar ldquoResponse surface methodology and artificialneural network for modeling and optimization of distilleryspent wash treatment using phormidium valderianum BDU140441rdquo Polish Journal of Environmental Studies vol 22no 4 2013

[37] S Sen S Nandi and S Dutta ldquoApplication of RSM and ANNfor optimization and modeling of biosorption of chromium(VI) using cyanobacterial biomassrdquo Applied Water Sciencevol 8 no 5 p 148 2018

[38] K-C Chen J-Y Wu D-J Liou and S-C J Hwang ldquoDe-colorization of the textile dyes by newly isolated bacterialstrainsrdquo Journal of Biotechnology vol 101 no 1 pp 57ndash682003

[39] G Neelakanta and H Sultana ldquo0e use of metagenomicapproaches to analyze changes in microbial communitiesrdquoMicrobiology Insights vol 6 2013

[40] T 0omas J Gilbert and F Meyer ldquoMetagenomics-a guidefrom sampling to data analysisrdquo Microbial Informatics andExperimentation vol 2 no 1 p 3 2012


[41] N Aslan ldquoApplication of response surface methodology andcentral composite rotatable design for modeling and opti-mization of a multi-gravity separator for chromite concen-trationrdquo Powder Technology vol 185 no 1 pp 80ndash86 2008

[42] J-S Kwak ldquoApplication of Taguchi and response surfacemethodologies for geometric error in surface grinding pro-cessrdquo International Journal of Machine Tools and Manufac-ture vol 45 no 3 pp 327ndash334 2005

[43] S Chamoli ldquoANN and RSM approach for modeling andoptimization of designing parameters for a V down perforatedbaffle roughened rectangular channelrdquo Alexandria Engi-neering Journal vol 54 no 3 pp 429ndash446 2015

[44] Y Zheng and A Wang ldquoRemoval of heavy metals usingpolyvinyl alcohol semi-IPN poly (acrylic acid)tourmalinecomposite optimized with response surface methodologyrdquoChemical Engineering Journal vol 162 no 1 pp 186ndash1932010

[45] M H Muhamad S R Sheikh Abdullah A B MohamadR Abdul Rahman and A A Hasan Kadhum ldquoApplication ofresponse surface methodology (RSM) for optimisation ofCOD NH3-N and 24-DCP removal from recycled paperwastewater in a pilot-scale granular activated carbon se-quencing batch biofilm reactor (GAC-SBBR)rdquo Journal ofEnvironmental Management vol 121 pp 179ndash190 2013

[46] Y Sun J Liu and J F Kennedy ldquoApplication of responsesurface methodology for optimization of polysaccharidesproduction parameters from the roots of Codonopsis pilosulaby a central composite designrdquo Carbohydrate Polymersvol 80 no 3 pp 949ndash953 2010

[47] Y Zou X Chen W Yang and S Liu ldquoResponse surfacemethodology for optimization of the ultrasonic extraction ofpolysaccharides from Codonopsis pilosulaNannf varmodestaLT ShenrdquoCarbohydrate Polymers vol 84 no 1 pp 503ndash5082011

[48] M A Bezerra R E Santelli E P Oliveira L S Villar andL A Escaleira ldquoResponse surface methodology (RSM) as atool for optimization in analytical chemistryrdquo Talanta vol 76no 5 pp 965ndash977 2008

[49] H A Hasan ldquoResponse surface methodology for optimiza-tion of simultaneous COD NH4+ndashN and Mn2+ removalfrom drinking water by biological aerated filterrdquoDesalinationvol 275 no 1ndash3 pp 50ndash61 2011

[50] D Montgomery Design and Analysis of Experiments JohnWiley amp Sons New York NY USA 5th edition 2001

[51] T P Shah and P J Shah ldquoConnectionist Expert system formedical diagnosis using ANNndashA case study of skin diseasescabiesrdquo International Journal vol 3 no 8 2013

[52] K M Desai S A Survase P S Saudagar S S Lele andR S Singhal ldquoComparison of artificial neural network (ANN)and response surface methodology (RSM) in fermentationmedia optimization case study of fermentative production ofscleroglucanrdquo Biochemical Engineering Journal vol 41 no 3pp 266ndash273 2008

[53] D Bas and I H Boyaci ldquoModeling and optimization IIcomparison of estimation capabilities of response surfacemethodology with artificial neural networks in a biochemicalreactionrdquo Journal of Food Engineering vol 78 no 3pp 846ndash854 2007

[54] A Ebrahimpour R Rahman D Ean Chrsquong M Basri andA Salleh ldquoA modeling study by response surface method-ology and artificial neural network on culture parametersoptimization for thermostable lipase production from a newlyisolated thermophilic Geobacillus sp strain ARMrdquo BMCBiotechnology vol 8 no 1 p 96 2008

[55] I A W Al-Baldawi S R Sheikh Abdullah H Abu HasanF Suja N Anuar and I Mushrifah ldquoOptimized conditionsfor phytoremediation of diesel by Scirpus grossus in horizontalsubsurface flow constructed wetlands (HSFCWs) using re-sponse surface methodologyrdquo Journal of EnvironmentalManagement vol 140 pp 152ndash159 2014

[56] I F Purwanti ldquoArtificial aeration for the enhancement of totalpetroleum hydrocarbon (TPH) degradation in phytor-emediation of diesel-contaminated sandrdquo in From Sources toSolution pp 301ndash306 Springer Berlin Germany 2014

[57] R Saratale ldquoDecolorization and biodegradation of textile dyeNavy blue HER by Trichosporon beigeliiNCIM-3326rdquo Journalof Hazardous Materials vol 166 no 2-3 pp 1421ndash1428 2009

[58] W Handayani V I Meitiniarti and K H Timotius ldquoDe-colorization of acid red 27 and reactive red 2 by Enterococcusfaecalis under a batch systemrdquoWorld Journal of Microbiologyand Biotechnology vol 23 no 9 pp 1239ndash1244 2007

[59] S M Hossain and N Anantharaman ldquoActivity enhancementof ligninolytic enzymes of Trametes versicolor with bagassepowderrdquo African Journal of Biotechnology vol 5 no 2pp 189ndash194 2006

[60] A Pandey P Singh and L Iyengar ldquoBacterial decolorizationand degradation of azo dyesrdquo International Biodeteriorationamp Biodegradation vol 59 no 2 pp 73ndash84 2007

[61] K-T Chung and S E Stevens ldquoDegradation azo dyes by en-vironmental microorganisms and helminthsrdquo EnvironmentalToxicology and Chemistry vol 12 no 11 pp 2121ndash2132 1993

[62] S Mathew and D Madamwar ldquoDecolorization of ranocid fastblue dye by bacterial consortium SV5rdquo Applied Biochemistryand Biotechnology vol 118 no 1ndash3 pp 371ndash381 2004

[63] G Ghodake U Jadhav D Tamboli A Kagalkar andS Govindwar ldquoDecolorization of textile dyes and degradationof mono-azo dye amaranth by Acinetobacter calcoaceticusNCIM 2890rdquo Indian Journal of Microbiology vol 51 no 4pp 501ndash508 2011

[64] D Abdel-El-Haleem ldquoAcinetobacter environmental andbiotechnological applicationsrdquo African Journal of Biotech-nology vol 2 no 4 pp 71ndash74 2003

[65] N Jain S Shrivastava and A Shrivastava ldquoTreatment of pulpmill wastewater by bacterial strain Acinetobacter calcoaceti-cusrdquo Indian Journal of Experimental Biology vol 35 no 2pp 139ndash143 1997

[66] U U Jadhav ldquoEffect of metals on decolorization of reactiveblue HERD by Comamonas sp UVSrdquo Water Air amp SoilPollution vol 216 no 1ndash4 pp 621ndash631 2011

[67] X Wu S Monchy S Taghavi W Zhu J Ramos andD van der Lelie ldquoComparative genomics and functionalanalysis of niche-specific adaptation in Pseudomonas putidardquoFEMS Microbiology Reviews vol 35 no 2 pp 299ndash323 2011

[68] J J Plumb J Bell and D C Stuckey ldquoMicrobial populationsassociated with treatment of an industrial dye effluent in ananaerobic baffled reactorrdquo Applied and Environmental Mi-crobiology vol 67 no 7 pp 3226ndash3235 2001

[69] S Ezhumalai and V 0angavelu ldquoKinetic and optimizationstudies on the bioconversion of lignocellulosic material intoethanolrdquo Bioresources vol 5 no 3 pp 1879ndash1894 2010

[70] Q Wang H Ma W Xu L Gong W Zhang and D ZouldquoEthanol production from kitchen garbage using responsesurface methodologyrdquo Biochemical Engineering Journalvol 39 no 3 pp 604ndash610 2008

[71] S-J You and J-Y Teng ldquoAnaerobic decolorization bacteriafor the treatment of azo dye in a sequential anaerobic andaerobic membrane bioreactorrdquo Journal of the Taiwan Instituteof Chemical Engineers vol 40 no 5 pp 500ndash504 2009


[72] E Kilickap ldquoModeling and optimization of burr height indrilling of Al-7075 using Taguchi method and responsesurface methodologyrdquo =e International Journal of AdvancedManufacturing Technology vol 49 no 9ndash12 pp 911ndash9232010

[73] H-L Liu and Y-R Chiou ldquoOptimal decolorization efficiencyof reactive red 239 by UVTiO2 photocatalytic process cou-pled with response surface methodologyrdquo Chemical Engi-neering Journal vol 112 no 1ndash3 pp 173ndash179 2005

[74] A Maleki and B Shahmoradi ldquoSolar degradation of directblue 71 using surface modified iron doped ZnO hybridnanomaterialsrdquoWater Science and Technology vol 65 no 11pp 1923ndash1928 2012

[75] N Puvaneswari J Muthukrishnan and P GunasekaranldquoBiodegradation of benzidine based azodyes direct red anddirect blue by the immobilized cells of pseudomonas fluo-rescens D41rdquo Indian Journal of Experimental Biology vol 40no 10 pp 1131ndash1136 2002

[76] M Khehra H Saini D Sharma B Chadha and S ChimnildquoDecolorization of various azo dyes by bacterial consortiumrdquoDyes and Pigments vol 67 no 1 pp 55ndash61 2005

[77] S-H Moon and S J Parulekar ldquoA parametric study otprotease production in batch and fed-batch cultures of Ba-cillus firmusrdquo Biotechnology and Bioengineering vol 37 no 5pp 467ndash483 1991

[78] K M Kodam I Soojhawon P D Lokhande and K R GawaildquoMicrobial decolorization of reactive azo dyes under aerobicconditionsrdquo World Journal of Microbiology and Biotechnol-ogy vol 21 no 3 pp 367ndash370 2005

[79] S Asad M A Amoozegar A A PourbabaeeM N Sarbolouki and S M M Dastgheib ldquoDecolorization oftextile azo dyes by newly isolated halophilic and halotolerantbacteriardquo Bioresource Technology vol 98 no 11pp 2082ndash2088 2007

[80] D Georgiou C Metallinou A Aivasidis E Voudrias andK Gimouhopoulos ldquoDecolorization of azo-reactive dyes andcotton-textile wastewater using anaerobic digestion and ac-etate-consuming bacteriardquo Biochemical Engineering Journalvol 19 no 1 pp 75ndash79 2004

[81] P Dubin and K L Wright ldquoReduction of azo food dyes incultures of Proteus vulgarisrdquo Xenobiotica vol 5 no 9pp 563ndash571 1975

[82] J-S Chang and Y-C Lin ldquoFed-batch bioreactor strategies formicrobial decolorization of azo dye using a Pseudomonasluteola strainrdquo Biotechnology Progress vol 16 no 6pp 979ndash985 2000

[83] I K Kapdan F Kargi G McMullan and R MarchantldquoDecolorization of textile dyestuffs by a mixed bacterialconsortiumrdquo Biotechnology Letters vol 22 no 14pp 1179ndash1181 2000

[84] J Yu X Wang and P L Yue ldquoOptimal decolorization andkinetic modeling of synthetic dyes by Pseudomonas strainsrdquoWater Research vol 35 no 15 pp 3579ndash3586 2001

[85] E Betiku O R Omilakin S O Ajala A A OkeleyeA E Taiwo and B O Solomon ldquoMathematical modeling andprocess parameters optimization studies by artificial neuralnetwork and response surface methodology a case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesisrdquoEnergy vol 72 pp 266ndash273 2014

[86] M Y Noordin V C Venkatesh S Sharif S Elting andA Abdullah ldquoApplication of response surface methodology indescribing the performance of coated carbide tools whenturning AISI 1045 steelrdquo Journal of Materials ProcessingTechnology vol 145 no 1 pp 46ndash58 2004


complex toxic and recalcitrant residual [4] 0e textileeffluent needs high chemical oxygen demand and colorationwith the occurrence of dyes pigments and additionalchemicals which make the effluent require particular han-dling [5] Innovative methods were explored to attenuate theenvironmental harm that may induce the removal of textiledyes within industrial effluents Azo dye made up of one ormore -NN- double bond amounts to 60ndash70 of dyeproduction [6 7] Physical and chemical methods includingflocculation adsorption and photochemical oxidation aregreat options for the decolorization of printing and dyeingwastewater (PDW)

Several physical and chemical methods have been ap-plied to treat Direct Blue 71 dye including Fentonrsquos oxi-dation [8] ozonation [9] ultrasound [10] and adsorption[11] 0e release of secondary pollutants and large operatingcosts have become the main drawbacks of this method 0ebiological practice offers the aspects of cost-effective andsimple operations in contrast to physical and chemicalmethods and is currently widespread in the dye treatmentmethod [12] Microorganisms are being intensively studiedregardless of physical and chemical options available [13]Several works with the applied of fungi and bacteria havealready been designed to develop bioprocesses meant fortreating textile effluents

At the moment the majority of isolated bacteria requireanaerobic growing conditions to degrade azo dyes [14]Nonetheless the functional group associated with the azo dyemakes up a complex composition and influences the capa-bilities of the bacteria 0erefore choosing the best azo dyedecolorizing bacteria is critical Often the deposition of toxicmutagenic and carcinogenic aromatic amines is coupled withdecolorization of the azo dyes which are tolerant to degra-dation in anoxic conditions and disrupt the food chain as itaccumulates [15 16] 0us a key for the complete removal ofazo dye from the environment is the azo dye full degradationbesides only the elimination of color [17]

Presently both response surface methodology (RSM)and artificial neural network (ANN) tools are used for theoptimization and modeling of environmental researchRelationship determination among experimental variablesand responses use response surface methodology (RSM)Besides that this great technique is able to depict the mainand interaction effects Particular experimental designmixtures are used in RSM to build mathematical modelswith linear quadratic and interaction terms by a providednumber of elements and response factors to get optimumoperation [18] Azila et al [19] and Amini et al [20] havereported studies in environmental issues in applying theRSM technique

One of the best tools for modeling nonlinear andcomplex conditions is undoubtedly artificial neural net-works (ANNs) out of various multivariate statistical tech-niques [21] It is a strong modeling tool because of itsflexibility to extend and learn the response of all nonlinearand complex processes ANN can be effectively used in themodeling of many operations as applied by Prakash et al[22] and Yetilmezsoy and Demirel [23] A major cut in thenumber of experiments as well as the information on

singular or combination effects related to the independentvariables is possible by using multivariate statisticalmethods [20] 0is leads to the optimization and growth ofthe operating system noticeably lowering the expense oftrials 0e response surface methodology (RSM) and arti-ficial neural network (ANN) are the most frequently utilizedmethods in research on dye decolorization literature 0esetwo are strong data modeling tools concerning independentvariables and responses of the system to gain and depictcomplex nonlinear interactions

Different kinds of optimization for the handling ofenvironmental pollution have implemented RSM and ANNtechniques due to its utmost precision [24 25] Predictionmodels received from RSM and ANN have effectively op-timized the lead removal from industrial sludge leachate[26] and Singh [27] used the ANN practice for methyl reddecolorization in 24 hours by optimizing the design pa-rameters of a bacterial isolate ITBHU01

Abd ElndashRahim et al [28] worked on optimization ofmetal ion concentration pH amount of adsorbent andtemperature using RSM and ANN which is designed tooptimize the chromium (VI) abatement by working withcyanobacteria Similarly Astray [29] worked on optimizingthe capabilities of cyanobacteria in optimum conditionpredicted by RSM and ANN to degrade and decolorizedistillery spent wash Furthermore RSM and ANN ap-proaches were also tremendously employed in diverse fieldsfor example a simulation model of ventilated room withthermal effects [30] predicting the suitability of sugar beetpulp to make oligosaccharides [31] optimizing the copperremoval from synthetic wastewater by using electro-coagulation system [32] modeling the quality parameters ofspray-dried pomegranate juice [33] predicting cold watertemperature in a forced draft cooling tower [34] and cre-ating a stable oil-in-water for emulsification process as wellas decreasing the number of trials time and cost [35]Comparison between RSM and ANN techniques was alsodone by Ravikumar [36] and Sen [37] for their predictionand optimization abilities

In former studies the mixed bacterial cultures weregrown in rich media supplemented with a carbon source inanaerobic conditions to get the highest decolorization ac-tivity [38] Yet these complex laboratory substrates wouldnot be suited for in situ application It was discovered thatANN has a higher prediction ability with minimum error[36] and higher accuracy compared to the RSM model [37]Even so a method for mixed bacterial decolorization ofDirect Blue 71 dye by using both RSM and ANN techniqueshas not been discovered yet Hence the main motivationbehind this study is to optimize the bioremediation of DB71dye decolorization by using both RSM and ANN techniquesdespite the absence of carbon and nitrogen source for thedecolorization process

2 Materials and Methods

21 Dyes Chemicals and Culture Media Direct Blue 71 dye(CI 34140 dye content 50) used in this study was pur-chased from Sigma-Aldrich Chemical Co USA 10 g of dye








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7372

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tration (p

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661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References

































































































Ab01113890 1113891 times 100 (1)










R2

1 minus1113936


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7 717372

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4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References































































































R2

1 minus1113936


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2

(2)

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p

⎧⎨

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4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References


































































































30

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4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References



































































































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4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References






























































































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60

70

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(d)












Connectiontype




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Dye co

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771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued








050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References




































































































Connectiontype




R2 DC


Input Output

Bias





R2 = 09859

0

20

40

60

80

100

40 50 60 70 80 90 100

Pred

icte

d A

NN

Experimental

ANN



949

5443

663

6669

7275

051

152

253

40

50

60

70

80

90

100

Dec

olor

izat

ion

()


663

6669

7275

115

225

3

0

0

0


(a)

Figure 5 Continued






949

5443

051

152

25

3

5070

90110

130150

40

50

60

70

80

90

100

Dec

olor

izat

ion

()



152

25

3

7090

110130

15

0

0

0



(b)


949

5443

Dec

olor

izat

ion

()

663

6669

7275

5070

90110

130150

40

50

60

70

80

90

100


663

6669

7275

7090

110130

150

0

0

0


(c)



92

Dec

olor

izat

ion

()

90888684828078767472706866646260

05 0667 0833 1 117 133 15 167Yeast extract (gL)

183 2 217 23326725

283 3 50567

63370

767833

90967

Dye co

ncen

tratio

n (pp

m)

103110

117123

130137

143150

(a)

90

Dec

olor

izat

ion

()

898887868584838281807978

661

6263

6465

6667

pH68

69

74

771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued








050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References





























































































R2 = 09859

0

20

40

60

80

100

40 50 60 70 80 90 100

Pred

icte

d A

NN

Experimental

ANN



949

5443

663

6669

7275

051

152

253

40

50

60

70

80

90

100

Dec

olor

izat

ion

()


663

6669

7275

115

225

3

0

0

0


(a)

Figure 5 Continued






949

5443

051

152

25

3

5070

90110

130150

40

50

60

70

80

90

100

Dec

olor

izat

ion

()



152

25

3

7090

110130

15

0

0

0



(b)


949

5443

Dec

olor

izat

ion

()

663

6669

7275

5070

90110

130150

40

50

60

70

80

90

100


663

6669

7275

7090

110130

150

0

0

0


(c)



92

Dec

olor

izat

ion

()

90888684828078767472706866646260

05 0667 0833 1 117 133 15 167Yeast extract (gL)

183 2 217 23326725

283 3 50567

63370

767833

90967

Dye co

ncen

tratio

n (pp

m)

103110

117123

130137

143150

(a)

90

Dec

olor

izat

ion

()

898887868584838281807978

661

6263

6465

6667

pH68

69

74

771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued








050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References































































































949

5443

051

152

25

3

5070

90110

130150

40

50

60

70

80

90

100

Dec

olor

izat

ion

()



152

25

3

7090

110130

15

0

0

0



(b)


949

5443

Dec

olor

izat

ion

()

663

6669

7275

5070

90110

130150

40

50

60

70

80

90

100


663

6669

7275

7090

110130

150

0

0

0


(c)



92

Dec

olor

izat

ion

()

90888684828078767472706866646260

05 0667 0833 1 117 133 15 167Yeast extract (gL)

183 2 217 23326725

283 3 50567

63370

767833

90967

Dye co

ncen

tratio

n (pp

m)

103110

117123

130137

143150

(a)

90

Dec

olor

izat

ion

()

898887868584838281807978

661

6263

6465

6667

pH68

69

74

771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued








050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References



























































































92

Dec

olor

izat

ion

()

90888684828078767472706866646260

05 0667 0833 1 117 133 15 167Yeast extract (gL)

183 2 217 23326725

283 3 50567

63370

767833

90967

Dye co

ncen

tratio

n (pp

m)

103110

117123

130137

143150

(a)

90

Dec

olor

izat

ion

()

898887868584838281807978

661

6263

6465

6667

pH68

69

74

771

7372

7550567

63370

767833

90967

Dye concen

tration (p

pm)103

110117

123130

137143

150

(b)

Figure 6 Continued








050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References

































































































050667

08331 117

13315

167

Yeast extra

ct (gL)183

2 217233

26725

2833

90D

ecol

oriz

atio

n (

)8886848280787674727068666462

661 62 63 64 65 66 67

pH68 69

74

7 717372

75

(c)





4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References





























































































4 Conclusions


Nomenclature



Data Availability




Acknowledgments


References



























































































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