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Chemical Engineering Journal 172 (2011) 977– 986

Contents lists available at ScienceDirect

Chemical Engineering Journal

j ourna l ho mepage: www.elsev ier .com/ locate /ce j

xperimental design of mixture for the anaerobic co-digestion of sewage sludge

. Venkateswara Rao, Saroj S. Baral ∗

epartment of Chemical Engineering, Birla Institute of Technology and Science, Pilani - K.K. Birla Goa Campus, Zuari Nagar, Goa 403726, India

r t i c l e i n f o

rticle history:eceived 26 March 2011eceived in revised form 4 July 2011ccepted 5 July 2011

eywords:naerobic co-digestion

a b s t r a c t

Batch experiments were conducted for assessing the biogas generation potential of two sets of mixtures(set A and set B). Mixtures consisting of sewage sludge with cow dung and garden waste were studiedwith set A, whereas mixtures consisting of sewage sludge with cow dung and fruit juice wastewater werestudied with set B. Augmented simplex centroid design was used to design the mixture composition forthe anaerobic co-digestion. The reactor performance was assessed using cumulative methane volume andpercentage volatile solids destruction as the criteria. Synergetic effect was found to be 9.71% of garden

iogasewage sludgeixture design

esponse surface methodology

waste (GW), 20.29% of cow dung (CD) and 70% of sewage sludge (SS) in set A and 75.5% of SS and 24.5%of CD in set B. Antagonism was observed with the mixes containing garden and fruit juice wastewater(FJW) with the SS. The reason may be due to the rapid acidification of FJW which leads to the soaring ofthe reactor. In case of GW, the substrate was limiting and the biogas production was negligible after 10thday. Response surface methods were used to find out optimum mixture combination for maximising thebiogas production.

© 2011 Elsevier B.V. All rights reserved.

. Introduction

Increasing concern of safe disposal of different wastes generatedn the society necessitates the scientific community to collect andreat the wastes effectively. Significant developments have takenlace in treating the wastewaters during the last two decades. Thisas resulted in increasing sludge production, which is consuming0% of the current operating costs of wastewater treatment plants1]. Sludge is a by product obtained from different unit operationsf wastewater treatment plants during different physical, chemicalnd biological processes which includes clarifier, biological reactor,entrifuge, etc. The sludge obtained should be treated before its dis-osal as it contains organic matter and pathogens. As the sewageludge (SS) consists of organic matter, energy can be recoveredhile treating it. There are several options to derive the energy from

he treatment of SS [2,3]. Among the options available, anaerobicigestion (AD), production of bio fuels, microbial fuel cells, incin-ration, gasification and pyrolysis, supercritical wet oxidation arehe popular and frequently used [4]. However AD of SS is preferredver the other options because of its advantages such as less capi-al investment, less operation and maintenance charges, etc. [5]. SS

enerally contains low solids concentration and as a result of this,ower biogas yields are observed in digestion. The solids contentnd the nutritional balance can be improved by co-digesting the SS

∗ Corresponding author. Tel.: +91 9767022314.E-mail address: ss baral2003@yahoo.co.in (S.S. Baral).

385-8947/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.cej.2011.07.010

with other organic substances such as confectionary waste, meatprocessing by-products, organic fraction of municipal solid waste,food waste, fruit and vegetable waste, agricultural waste, greasetrap sludge and energy crops [6–10]. The co digestion of SS not onlyresults in high biogas yield but also improves the process stability asdifferent types of wastes are being mixed [11]. As the SS is a com-monly available waste across the globe, the co-digestion processis also feasible all the times. The co-digestion also helps differentorganisations in providing solutions for managing different wastesgenerated in the society. The mixing process in co-digestion alsoresults in the distribution of concentrated substances, otherwisecausing inhibitory effects on the digestion process.

Several waste combinations are possible for the co-digestion ofSS based on the availability of substrates through different seasons.It is also very important to see that the variation in the characteris-tics of the waste to be minimum in order to avoid the disturbancesin the reactor. In most of the cases, SS was co-digested with organicfraction of municipal solid waste [12–17]. SS was also co-digestedwith food waste [18], fruit and vegetable waste [19], industrialwaste [8], grease trap sludge [20,21], meat processing waste [22],and confectionary waste [23]. From the literature, it was observedthat during the batch experiment, the proportions of the mixturecomponents were selected randomly. In case of some continuousplants, the substrates for digestion were mixed to prepare slur-

ries based on the operating parameters such as hydraulic retentiontime and organic loading rate [14]. From the results of these stud-ies, it is understood that the mixture composition largely influencesthe biogas yield, reactor stability, solids destruction efficiency, etc.

9 gineer

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78 P.V. Rao, S.S. Baral / Chemical En

n some cases, more than one combination of the mixtures wasried for digestion. A critical review of literature reveals that thereas no technical basis for selecting the waste proportions to pre-are a mix for digestion process in order to maximise the biogasield and reduction efficiency of solids. There was no comprehen-ive study available to understand the effect of composition on theiodegradation process in order to optimise the response variables.

Statistical techniques like design of experiments (DOE) are oftensed in process optimization when compared to the conventionalethods because of their significant advantages such as improved

rocess yields, reduced variability and closer conformance to nom-nal or target requirements, reduced development time, reducedverall costs [24,25]. Among different DOE methodologies, mix-ure designs are special class of response surface designs where theroportions of the components or factors are considered importantather than their magnitude and are useful in mixture design [25].he interactions between the components of a mixture for max-mising the response are studied using mixture design approach.n a mixture experimental design, the total amount of material iseld constant because the response depends on the relative propor-ions of the components (ingredients) in the mixture and not on themount of the mixture [26]. Statistical methods were applied to dif-erent engineering problems for improving the performance and tond the optimum process variables. Abdullah and Chin [27] usedhe design of mixtures for obtaining the optimum moisture contentnd C/N ratio for compositing the kitchen waste. Rongzhi et al. [28]sed simplex centroid design for optimising the ceramic adsorbentor AS (V) removal from wastewater. To study the performance of aatch reactor, response variables are needed and should be selectedith proper care. Literature reveals usage of several response vari-

bles to evaluate the performance of batch reactor. Among the list,pecific methane yield, cumulative methane yield, volatile solidsestruction, chemical oxygen demand destruction are widely usedy many researchers.

As the biogas generated from organic waste depends on its com-osition, an attempt has been made in the present investigation totudy the effect of substrate composition on biogas quantity andeneration patterns using mixture design. In the present study,he synergic and antagonistic effects of composition of the mixturen the response variables were studied using statistical methods.ommercial software MINITAB (Version 15) was used for analysinghe results of the experimental design.

. Materials and methods

.1. Substrates for co-digestion

Different feedstock’s such as sewage sludge (SS), garden wasteGW), cow dung (CD) and fruit juice wastewater (FJW) were usedor the AD. The SS for the AD was collected from the outlet of theludge tank of the wastewater treatment plant located in BITS Pilani.K. Birla Goa Campus. The wastewater treatment plant runs usinguidized aerobic bioreactor technology. The plant caters to the needf about 2500 people with a daily inflow rate of 600 m3, receivingostly domestic wastewater. The GW used in the study was pre-

ared from lawn grass generated in the BITS Pilani K.K. Birla Goaampus. The grass grows profusely and hence removed periodicallyo increase the life of the garden. The collected grass was dried in anven (BIOTECH, India) at 50 ◦C for two days to remove the moistureontent. The dried lawn grass was cut into small pieces and pow-ered using a mixer cum grinder (Prestige, India). The powder was

reserved in an air tight polyethylene bottle for further use. 100 gf dried powder was mixed with de-mineralized water to make alurry of 2000 ml. Fresh cow dung was collected from a local cat-le farm located in Zuari Nagar, Goa, India. The collected cow dung

ing Journal 172 (2011) 977– 986

was in the form of semi solid. The visible straws present in the cowdung were removed manually. De-mineralized water was addedto 1000 g of cow dung to prepare slurry of 2000 ml and used asinoculum for all the mixture combinations. 2000 ml of syntheticfruit juice wastewater was prepared in the laboratory by mixing413 g of grinded fruit with de-mineralized water. All the preparedsubstrates were stored in an incubator (BIOTECH, India) at 4 ◦Cuntil characterization. All the feedstock’s were analysed for totalsolids (TS) content to find the amount of moisture required to bringthe TS to around the design value (2–8%). Appropriate amountsof water were then added to bring the feedstock to the designTS. In all the cases a single batch of feedstock was used for eachtrial.

2.2. Analytical methods

All the feed stocks selected for the digestion were analysed fortheir physical and chemical properties. TS, volatile solids (VS), pHand ammonical nitrogen (AN), chemical oxygen demand (COD)were analysed using standard methods [29]. To find out the TS inthe substrates, samples were kept in an oven at 105 ◦C (BIOTECH,India) for 24 h and weights were taken before and after the period.The samples withdrawn from the oven were kept in a dessicator(Qualigens, India) till arriving at room temperature. To find out theVS in the samples, the oven dried crucibles were kept in a mufflefurnace (BIOTECH, India) at 550 ◦C for 30 min. The crucibles wereremoved from furnace and cooled in air until most of the heat haddissipated. It was subsequently transferred to dessicator to coolto room temperature. The sample was then weighed and heatedonce again. Once the sample attains a constant weight, it was noteddown and used for calculation of VS. Hach DR 5000 spectropho-tometer was used for measurement of COD and AN [30]. The totalvolatile fatty acid (TVFA) and total alkalinity (TA) of the solutionswere measured by titration technique [31]. The biogas generatedwas analysed for its methane content using the method adoptedby Sakar et al. [32] and Goksel et al. [33]. A known volume of theheadspace gas (V1) produced in a serum bottle used in biochemi-cal methane production (BMP) experiments was syringed out andinjected into another serum bottle which contained 20 g/L KOHsolution. This serum bottle was shaken manually for 3–4 min sothat all the CO2 and H2S were absorbed in the concentrated KOHsolution. The volume of the remaining gas (V2), which was 99.9%CH4, in the serum bottle was determined by means of a syringe.The ratio V2/V1 provides the CH4 content in the headspace gas. Thefeedstock mixtures taken in batch reactors were analysed for allthe parameters before and after the digestion. All the characteriza-tions were performed in duplicate and the averages were taken forfurther interpretation. All the chemicals used for the analysis are ofanalytical grade.

2.3. Experimental setup

Experiments were carried out in an air tight glass reactors ofvolume 120 ml with butyl rubbers stoppers. All the experimentswere performed in duplicates at room temperature and the averagewas taken for interpretation. Calculated amount of inoculum andthe substrates were added into the bottles separately for each feedstock composition. Total volume of the substrate in each reactorwas maintained at 70 ml. In each bottle approximately 50 ml spaceis left for biogas collection. In order to maintain anaerobic condi-tions, headspaces of the bottles were flushed with nitrogen gas andthe bottles were closed with air tight butyl rubber stoppers. The

bottles were static throughout, except manual mixing during gasmeasurements. The volume of biogas generated in the batch reactorwas measured in regular intervals using downward displacementtechnique.

gineering Journal 172 (2011) 977– 986 979

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Table 1Experimental design of three component batch co-digestion sets.

ReactorNo.

Mix composition in reactor (% of substrate volume)

SS GW CD FJW

Set A A1 100 0 0 0A2 0 100 0 0A3 0 0 100 0A4 50 50 0 0A5 50 0 50 0A6 0 50 50 0A7 33.33 33.33 33.33 0A8 66.67 16.67 16.67 0A9 16.67 66.67 16.67 0A10 16.67 16.67 66.67 0

Set B B1 100 0 0 0B2 0 0 0 100B3 0 0 100 0B4 50 0 0 50B5 50 0 50 0B6 0 0 50 50B7 33.33 0 33.33 33.33B8 66.67 0 16.67 16.67

P.V. Rao, S.S. Baral / Chemical En

.4. Mixture design

Simplex designs were used to study the effects of mixture com-onents on the response variable. If ‘q’ represents the number of

ngredients in the system under study and ‘xi’ represents the pro-ortion of ith constituent in the mixture, then

q

i=1

xi = x1 + x2 + · · · + xq = 1.0; xi > 0; i = 1, 2, 3, . . . , q

n mixture problems, the purpose of the experiments is to modelhe blending surface with some forms of mathematical equationso that predictions of the response for any mixture or combinationf the ingredients can be made empirically or some measure of thenfluence on the response of each component singly and in combi-ation with other components. When the mixture is composed ofhree components, the mixture space is a triangle with vertices cor-esponding to formulations that are pure blends (mixtures that are00% of a single component) [25]. The mixture blend representinghe three components can be conveniently represented on tri-linearoordinate paper. Each of the three sides of the triangle represents

mixture that has none of one of the three components (the com-onent labelled on the opposite vertex). The standard forms of theixture models that are in widespread use are [26]:

inear : Y =q∑

i=1

ˇixi

uadratic : Y =q∑

i=1

ˇixi +∑ q∑

i<j

ˇijxixj

ull cubic : Y =q∑

i=1

ˇixi +∑ q∑

i<j

ˇijxixj +∑ q∑

i<j

ıijxixj(xi − xj) +

pecial cubic : Y =q∑

i=1

ˇixi +∑ q∑

i<j

ˇijxixj +∑ q∑

i<j<k

∑ˇijkxix

pecial quartic : Y =q∑

i=1

ˇixi +∑ q∑

i<j

ˇijxixj +∑ q∑

i<j<k

∑ˇiijk

here Y represents the yield or output variable of the process.i represents the expected response to the pure blend xi = 1 andj = 0 when j /= i. The potion

∑qi=1ˇixi is called the linear blending

ortion. When there is curvature arising from nonlinear blendingetween component pairs, the parameters ˇij represent either syn-rgistic or antagonistic blending. Higher-order terms are frequentlyecessary in mixture models because the phenomena studied maye complex and the experimental region is frequently the entireperability region and is therefore large, requiring an elaborateodel. The simplex lattice and simplex centroid designs are bound-

ry point designs. To make predictions about the properties ofomplete mixtures, it would be highly desirable to have more runsn the interior of the simplex. This can be done by augmenting thesual simplex designs with axial runs and the overall centroid, ifhe centroid is not already a design point.

Fig. 1 shows the {3, 2} simplex lattice design augmented withhe axial points. This design has 10 points, with four of these points

n the interior of the simplex. The augmented simplex centroidesign (ASCD) allows to fit the special cubic model or to add spe-ial quartic terms such as ˇ1233x1x2x2

3 to the quadratic model. TheSCD is superior for studying the response of complete mixtures

q∑

i<j<k

ˇijkxixjxk

k +∑ q∑

i<j<k

∑ˇiijkxix

2j xk +

∑ q∑

i<j<k

∑ˇijkkxixjx

2k

B9 16.67 0 16.67 66.67B10 16.67 0 66.67 16.67

in the sense that it can detect and model curvature in the interiorof the triangle that cannot be accounted for by the terms in the fullcubic model. The ASCD has more power for detecting lack of fit thandoes the {3, 3} simplex centroid design. This is particularly usefulwhen the investigator is unsure about the proper model to use andalso plans to sequentially build a model by starting with a simplepolynomial, testing the model for lack of fit, then augmenting themodel with higher-order terms, testing the new model for lack offit, and so forth.

Two three component mixtures were prepared using differentwaste combinations. For both the sets A and B, ten mixes of thethree wastes were prepared by using ASCD. The waste compo-sitions tested were shown in Table 1. All proportions of all thesubstrates in each mixture were sum to 100%, which means avolume of 60 ml. All the batch reactors were maintained at sameenvironmental conditions during digestion. In the general mixtureproblem, the measured response is assumed to depend only on theproportions of the ingredients present in the mixture. When all theenvironmental factors were held constant, the responses measuredas cumulative methane volume (CMV), percentage VS destruction(PVSD) are the functions of only the propositions of feed stocksused.

2.5. Response analysis

The performance efficiency of the reactor and conversion effi-ciency of feedstock was estimated from the response variables CMVand PVSD. The CMV was obtained by summing up the values ofmethane generated at regular intervals. At the end of the experi-

980 P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986

Table 2Results of characterisation of mixtures before digestion for sets A and B.

DOE order Reactor No. SS:GW:CD TS (%) VS (% of TS) VS (%) pH T alkalinity (mg/L)

1 A2 100:0:0 2.03 84.2 1.71 7.67 7252 A1 0:100:0 4.19 92.4 3.87 6.50 5253 A3 0:0:100 8.26 87.9 7.26 6.20 11754 A9 50:50:0 3.83 91.9 3.52 6.80 8035 A7 50:0:50 5.10 87.4 4.46 6.20 8036 A5 0:50:50 6.66 90.5 6.02 6.50 7467 A6 33.33:33.33:33.33 5.18 89.8 4.65 7.50 8828 A4 66.67:16.67:16.67 3.51 88.0 3.08 7.00 7679 A10 16.67:66.67:16.67 4.92 91.4 4.50 7.10 790

10 A8 16.67:16.67:66.67 6.35 88.5 5.62 7.10 781

DOE order Reactor No. SS:JW:CD TS (%) VS (% of TS) VS (%) pH T alkalinity (mg/L)

11 B2 100:0:0 2.03 84.20 1.71 7.67 72512 B1 0:100:0 2.81 96.74 2.72 6.10 15013 B3 0:0:100 8.26 87.90 7.26 6.20 117514 B9 50:50:0 2.30 91.65 2.11 7.20 43815 B7 50:0:50 5.10 87.38 4.46 6.20 80316 B5 0:50:50 5.16 89.85 4.63 6.00 66317 B6 33.33:33.33:33.33 4.24 89.25 3.78 7.10 68318 B4 66.67:16.67:16.67 3.10 88.16 2.73 7.20 704

mftc

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ental period, the value was calculated for all the mixtures takenor the study. PVSD is a very important parameter which indicateshe conversion efficiency of volatile matter into biogas. PVSD wasalculated using the following relation:

VS destruction = VSinitial − VSfinal

VSinitial

. Results and discussion

.1. Characterisation of substrates

All the substrates used for the co-digestion study were analysedor TS, VS, pH, TA, TVFA, AN and COD. The individual substrates and

Fig. 1. Augmented simplex centroid de

92.36 3.23 7.30 41788.28 5.36 6.40 929

all the combinations used were analysed for all the parameters. Theresults of the characterisation before digestion are shown in Table 2.All the mixtures were analysed for their properties after the diges-tion also. These results are given in Table 3. The TS content for themixtures was found to be varying between 2 and 8% with lowest as2.03% for SS and highest as 8.26% for cow dung. All other mixturesare having varying TS content depending on their composition. TheVS (%of TS) content of all the mixtures indicates presence of goodvolatile degradable matter and found to be varying from 84.2% to92.4%. All the mixtures were found to be having sufficient alkalin-

ity to maintain the pH at the starting of the reactor. The pH of allthe reactors was found to be varying in the range 6.5–7.67 beforedigestion. The SS was found to be in slightly alkaline range where asthe other wastes were found to be either neutral or slightly acidic.

sign plan used for experiments.

P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986 981

Table 3Results of characterisation of mixtures after digestion for sets A and B.

DOE order Reactor No. SS:GW:CD TS (%) VS (% of TS) VS (%) pH T alkalinity (mg/L)

1 A2 100:0:0 1.56 74.51 1.16 7.48 50002 A1 0:100:0 3.21 86.92 2.79 5.18 30003 A3 0:0:100 7.10 86.05 6.11 6.40 43754 A9 50:50:0 3.06 86.89 2.66 5.94 30755 A7 50:0:50 4.15 83.16 3.45 7.17 42506 A5 0:50:50 5.62 88.02 4.95 5.14 24257 A6 33.33:33.33:33.33 4.39 86.69 3.80 5.89 26258 A4 66.67:16.67:16.67 2.67 81.62 2.18 7.28 23259 A10 16.67:66.67:16.67 4.08 87.52 3.57 5.17 2975

10 A8 16.67:16.67:66.67 5.51 86.25 4.75 6.35 5250

DOE order Reactor No. SS:JW:CD TS (%) VS (% of TS) VS (%) pH T alkalinity (mg/L)

11 B2 100:0:0 1.56 74.51 1.16 7.48 500012 B1 0:100:0 2.68 91.38 2.45 3.91 013 B3 0:0:100 7.10 86.05 6.11 6.40 437514 B9 50:50:0 2.36 86.20 2.04 4.99 220015 B7 50:0:50 4.15 83.16 3.45 7.17 425016 B5 0:50:50 4.79 87.02 4.16 5.20 342517 B6 33.33:33.33:33.33 4.04 86.41 3.49 5.21 225018 B4 66.67:16.67:16.67 2.79 83.05 2.32 7.09 450019 B10 16.67:66.67:16.67 3.03 87.24 2.64 4.91 185020 B8 16.67:16.67:66.67 5.40 86.07 4.65 5.93 3200

Table 4Results of response values for sets A and B.

Run Independent variables, xij Dependent variables, Y

SS GW CD CMV (ml) PVSD (%)

Set A 100 0 0 620 32.00 100 0 137 28.00 0 100 397 15.9

50 50 0 323 24.450 0 50 833 22.6

0 50 50 105 17.933.33 33.33 33.33 224 18.366.67 16.67 16.67 740 29.316.67 66.67 16.67 109 20.716.67 16.67 66.67 259 15.4

Run Independent variables, xij Dependent variables, Y

SS FJW CD CMV (ml) PVSD (%)

Set B 100 0 0 620 32.00 100 0 73 9.70 0 100 397 15.9

50 50 0 77 3.450 0 50 833 22.6

0 50 50 182 10.133.33 33.33 33.33 102 7.8

TtTwtCe

3

omcc

66.67 16.67

16.67 66.67

16.67 16.67

he pH of the cow dung was found to be acidic before setting uphe reactor and found to be alkaline at the end of digestion period.his may be due its buffering nature of the cow dung. The wasteshich contain cow dung and SS in major proposition have found

o be either slightly alkaline or very near to neutral pH. SignificantOD reduction was also observed for all the mixtures at the end ofxperiment.

.2. Model fitting and regression analysis

The response data based on the independent variables was

btained from the experiments and recorded in Table 4. The experi-ents were conducted with duplicates and found that in all most all

ases there exists good agreement between the original and dupli-ates. All the independent and response variables were fitted to

16.67 422 15.116.67 104 18.466.67 219 13.3

linear, quadratic, special cubic, full cubic and special quartic mod-els. The residual errors were calculated for each model to check thegoodness of the fit. Model summary statistics is given in Table 5.Standard error of regression, S was used as a measure of model fitin regression and analysis of variance (ANOVA). S was measured inthe units of the response variable and represents the standard devi-ation of the residuals [27]. For a given study, the better the equationpredicts the response, the lower the value of S. Another parameterwhich was considered to evaluate the model was R2 (coefficientof determination) value, as the value reflects its relationship withone or more predictor variables. The best model was selected using

the criteria having low standard error for regression and high coef-ficient of determination. After applying the criteria, full cubic andspecial quartic models were found to be the best suited modelsfor both sets A and B. The polynomial model selected was special

982 P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986

Table 5Model summary statistics for CMV and PVSD of sets A and B.

Source Standard error of theregression, S

Regression, R-Sq (%) Predicted regression,R-Sq (pred)%

Adjusted regression,R-Sq (adj)%

Predicted sum ofsquares, PRESS

Cumulative methane volume for set ALinear 157.6100 68.04 56.57 64.28 573852Quadratic 115.4280 85.88 76.75 80.84 307142Special cubic 104.5640 89.24 79.98 84.28 264518Full cubic 65.4575 96.43 90.96 93.84 119426Special quartic 65.4575 96.43 90.96 93.84 119426

% VS destruction for set ALinear 2.9306 76.26 65.08 73.47 214Quadratic 1.8389 92.30 87.78 89.55 75Special cubic 1.8488 92.78 87.43 89.44 77Full cubic 0.6613 99.22 97.94 98.65 12Special quartic 0.6613 99.22 97.94 98.65 12

Cumulative methane volume for set BLinear 169.7420 60.87 48.94 56.26 639093Quadratic 111.5760 86.18 75.15 81.10 310990Special cubic 80.0950 93.34 88.47 90.26 144300Full cubic 54.3040 97.41 92.24 95.52 97153Special quartic 54.3040 97.41 92.24 95.52 97153

% VS destruction for set BLinear 6.5060 40.21 17.65 33.18 991Quadratic 4.5140 76.30 62.54 67.83 450Special cubic 4.6770 76.38 58Full cubic 2.4030 94.72 86Special quartic 2.4030 94.72 86

Table 6Regression coefficients for the special quartic models for sets A and B.

Coefficient Cumulative methane volume % VSD

Set A Set B Set A Set B

ˇ1 624 621 32.2 32.4ˇ2 142 74 28.1 10.1ˇ3 402 399 16 16.3ˇ12 −203 −1072 −21.9 −68.5ˇ13 1317 1303 −5.0 −3.8ˇ23 −629 −207 −15.7 −9.2ˇ1123 15403 −3849 393.8 −557.6

qb

+ 15

3 + 3

− 38

− 55

RTumdtvctiTf

3

a(i

ˇ1223 −9118 4892 −224.2 1043.2ˇ1233 −20624 −21559 −285.6 −420.0R2 96.43 97.41 99.22 94.72

uartic for both sets A and B for both the responses and is givenelow:

YCMV,A = 624x1 + 142x2 + 402x3 − 203x1x2 + 1317x1x3 − 629x2x3

YPVSD,A = 32.2x1 + 28.1x2 + 16.0x3 − 21.9x1x2 − 5.0x1x3 − 15.7x2x

YCMV,B = 621x1 + 74x2 + 399x3 − 1072x1x2 + 1303x1x3 − 207x2x3

YPVSD,B = 32.4x1 + 10.1x2 + 16.3x3 − 68.5x1x2 − 3.8x1x3 − 9.2x2x3

egression coefficients for both the responses were shown inable 6. ANOVA was also performed for both the models. The val-es of R2, a measurement of fitness of the regression equations areentioned in Table 6. These results indicate that the experimental

ata is in good agreement with the predicted values. To determinehe significance of the regression coefficients of the parameters Talue is used. P value is defined as the smallest level of signifi-ance leading to rejection of null hypothesis [34]. It is preferableo have larger magnitude of T value and smaller P value, whichndicates more significance for the corresponding coefficient term.he detailed analysis of the regression coefficients is done in theollowing section.

.2.1. Regression analysis for set A

From the fitted model for CMV and PVSD, it was observed that

ll the linear terms to be positive with the coefficient for the SSSS) being the maximum. This indicates all the coefficients are hav-ng synergetic effect on the responses with SS as the maximum.

.75 65.47 496

.12 90.88 167

.12 90.88 167

403x21x2x3 − 9118x1x2

2x3 − 20624x1x2x23

93.8x21x2x3 − 224.2x1x2

2x3 − 285.6x1x2x23

49x21x2x3 + 4892x1x2

2x3 − 21559x1x2x23

7.6x21x2x3 + 1043.2x1x2

2x3 − 420.0x1x2x23

However the quadratic terms of the model reflects synergetic andantagonistic effects from the coefficients. The coefficient ˇ13 is hav-ing significant synergetic effect (high T value = 5.82 and P = 0.000)where as the other quadratic terms were found to be antagonisticfor the response CMV. However the antagonistic effects were notsignificant because of its T (negative values) and P values (0.39 and0.018) respectively. So these terms can be ignored from the model.From the quartic terms, ˇ1123 is found to be highly significant(T = 3.24 and P = 0.008) for CMV. This means that mix combina-tions prepared with the combinations of SS and cow dung werebetter than the mixes involving garden waste for maximising theCMV. The other quartic terms were ignored because of negativeT value. In case of PVSD all the quadratic terms were found to benegative with negative T value and hence ignored. However thequartic term ˇ1123 was found to be highly significant with a positivevalue of 393.8 with T = 8.20 and P = 0.000. For both the responses the

quartic term ˇ1123 found to be highly significant, implying co-digestion with a mix combination of more SS and less GW and CD.After eliminating the insignificant terms from the model, it can bere written as the following:

YCMV,A = 624x1 + 142x2 + 402x3 + 1317x1x3 + 15403x21x2x3

YPVSD,A = 32.2x1 + 28.1x2 + 16.0x3 + 393.8x21x2x3

3.2.2. Regression analysis for set BIn case of set B special quartic model was selected for fitting

the data. The results were similar to set A for both the responsevariables in case of linear terms in the model. The coefficient ˇ13was having significant synergetic effect (high T value = 6.94 and

P = 0.000) where as the other quadratic terms were found to beantagonistic for CMV. The coefficient ˇ13 was having significantantagonistic effect for CMV because of positive coefficient and Tvalue with low P value. None of the quartic terms were found to be

P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986 983

1000-100

99

90

50

10

1

Residual

Per

cent

8006004002000

50

0

-50

-100

-150

Fitted Value

Res

idua

l500-50-100-150

6.0

4.5

3.0

1.5

0.0

Residual

Freq

uenc

y

2018161412108642

50

0

-50

-100

-150

Observation OrderR

esid

ual

Versus FitsNormal Probability Plot

Versus OrderHistogram

Residual Plots for CMV

Fig. 2. Residual plot for CMV of set A.

CD

0

1

SS1

0

GW1

0

> – – – – < 16

16 2121 2626 3131 36

36

PVSD

Mixture Contour Plot of PVSD(component amounts)

CD

0

1

SS1

0

FJW1

0

> – – – – < 6

6 1212 1818 2424 30

30

PVSD

Mixture Contour Plot of PVSD(component amounts)

CD

0

1

SS1

0

GW1

0

> – – – – < 200

200 400400 600600 800800 1000

1000

CMV

Mixture Contour Plot of CMV(component amounts)

CD

0

1

SS1

0

FJW1

0 > – – – – < 15 0

150 30 0300 45 0450 60 0600 75 0

750

CMV

Mixture Contour Plot of CMV(component amounts)

Fig. 3. Mixture contour plots for CMV and PVSD of sets A and B.

984 P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986

CD1.00

0.000.00

0SS

500

1.00 0.00

CMV

1000

1.00GW

Mixture Surface Plot of CMV(component amounts)

CD1.00

0.000.00

1.000 0.00

250

500

FJW

750

1.00SS

CMV

Mixture Surface Plot of CMV(component amounts)

CD1.00

0.00

SS

20

PVSD30

1.00 0.00

40

0.00

1.00GW

Mixture Surface Plot of PVSD(component amounts)

CD1.00

0.000 0.00SS

10

20PVSD

30

1.00 0.001.00

FJW

Mixture Surface Plot of PVSD(component amounts)

Fig. 4. Mixture 3D surface plots for CMV and PVSD of sets A and B.

CD

0

1

1

0

1

0

500875

CMV

1530

PVSD

Contour Plot of CMV, PVSD(component amounts)

CD

0

1

S1

0

1

0

400800

CMV

1530

PVSD

Contour Plot of CMV, PVSD(component amounts)

for CM

scwIaˇ1SAr

SS GW

Fig. 5. Overlaid contour plot

ignificant and hence can be ignored for CMV. This means that mixombinations prepared with the combinations of SS and cow dungere better than the mixes involving FJW for maximising the CMV.

n case of PVSD all the quadratic terms were found to be negativend insignificant and can be ignored. However the quartic term1223 was found to be highly significant with a positive value of043.28 with T = 5.98 and P = 0.000. This implies that the mixes ofS, CD with higher proportion of FJW gives good response for PVSD.fter eliminating the insignificant terms from the model, it can be

e-written as the following:

YCMV,B = 621x1 + 74x2 + 399x3 + 1303x1x3YPVSD,B = 32.4x1 + 10.1x2 + 16.3x3 + 1043.2x1x2

2x3

S FJW

V and PVSD of sets A and B.

3.3. Interpretation of residual graphs

Normal probability plots of the residuals are used to checkthe normality of the data. This is a graphical technique to assesswhether the data is normally distributed or not. The residual isthe difference between the observed and the predicted value fromthe regression. If the points of the plot are seen closer to thestraight line, then the data is normally distributed [35]. The resid-ual plot for set A is shown in Fig. 2 for CMV. The results can be

shown with the help of histogram also. Histogram of the residualsshows the distribution of the residuals for all observations. Fig. 2plots the residuals versus the fitted values (predicted response).From Fig. 2, it was observed that the experimental points were

P.V. Rao, S.S. Baral / Chemical Engineering Journal 172 (2011) 977– 986 985

CurHigh

Low0.98 007D

New

d = 0.96 927

MaximumCMV

y = 865 .0132

d = 0.99 098

MaximumPVSD

y = 29.8647

0.980 07Desirabil ityComposite

0.0

1.0

0.0

1.0

0.0

1.0[ ]:GW [ ]:CD[ ]:SS

[0.70] [0 .0971 ] [0.20 29] CurHigh

Low0.94 772D

New

d = 1.0000

MaximumCMV

y = 807 .534 2

d = 0.85 121

MaximumPVSD

y = 27 .768 2

0.94 772Desirabil ityComposite

0.0

1.0

0.0

1.0

0.0

1.0[ ]:FJW [ ]:CD[ ]:SS

[0.755 0] [0.0] [0 .245 0]

r CM

rrm

3

gsipsrmstrotowehfrvotttpft

3

tairtaciis

Fig. 6. Optimization plots fo

easonably aligned suggesting the normal distribution and theesiduals were found to be scattered randomly about zero, whicheans that the errors are having a constant variance.

.4. Interpretation of contour plots and 3D surface plots

Using Minitab software, contour plots and 3D surface plots wereenerated to find the optimum proportion of the mixes for bothets A and B. Contour and surface plots are useful for establish-ng desirable response values and mixture blends. A contour plotrovides a two-dimensional view where all points that have theame response are connected to produce contour lines of constantesponses. A surface plot provides a three-dimensional view thatay provide a clearer picture of the response surface. Figs. 3 and 4

how the contour and 3D surface plots of CMV and PVSD for bothhe sets A and B. It was observed that both sets A and B gave similaresults for the contour and surface plots. From the contour plotsf CMV, the zones of maximum response variables were locatedowards the side of triangle having CD and SS as the vertices. In casef PVSD of sets A and B (Fig. 3), maximum response was observedith co-digestion of GW and FJW. This indicates that to certain

xtent, these waste proportions may be added to improve CMV andaving simultaneous effect of solids reduction. The SS content was

ound to be significant proportion in the mixture to maximise theesponse variable. The addition of CD helps to improve the responseariables where as the addition of GW and FJW has negative effectsn the response variable. This can be verified by observing the mix-ure surface plots (Fig. 4). From the over laid contour plots (Fig. 5),he white zones indicate the feasible region that satisfies bothhe criteria of optimum response variables. These overlaid contourlots are important for an engineer to obtain the optimum mixturerom the available alternative components without compromisinghe values of response variables.

.5. Interpretation of mixture proportion optimization

Response optimization of mixture proportions is used to iden-ify the combination of input variable settings that jointly optimize

single response or a set of responses. It is useful in determin-ng the optimum operating conditions that will maximise theesponse. Joint optimization must satisfy the requirements for allhe responses in the set, which is measured by the composite desir-bility. Using MINITAB, optimal solution can be obtained and plot

an be drawn accordingly. The optimal solution serves as the start-ng point for the plot. This optimization plot allows the user tonteractively change the input variable settings to perform sen-itivity analyses and possibly improve the solution. The optimal

V and PVSD of sets A and B.

solutions obtained for both sets A and B were shown in Fig. 6. Fromthe optimization plots, it was observed that that the use of GWand FJW in major proportion was having the antagonistic effectfor both the responses. However, the proportions of GW and FJWcan be increased to certain extent without compromising on theresponse variables. The optimum mixture ratios obtained from theanalysis are 70% of SS, 9.71% of GW and 20.29% of CD for set A and75.5% of SS and 24.5% of CD for set B. The composite desirabilityof the respective mixtures was found to be 0.98007 and 0.94772for sets A and B respectively, which is a significant factor needed tobe taken into account while considering the optimum composition.The composite desirability nearing to one indicates positive effecton maximising the response variables.

3.6. Model validation

From the optimization plots, the best mixture combinations forsets A and B at which the responses were highest were obtained as70% SS, 9.71% GW, 20.29% CD and 75.5% SS, 24.5% CD respectively.The responses for these combinations were obtained as 865 ml and29.8647% of CMV and PVSD respectively for set A and 807 ml and27.7682% of CMV and PVSD respectively for set B. From the exper-imental data, it was observed that the maximum values for CMVand PVSD were to be 833 ml (for mixture no. A7 and B7) and 32%(for mixture no. A1 and B1). In order to validate the model, experi-ments were conducted with the optimum compositions obtained.The results obtained from the experiments were found to be 850 mlof CMV and 30.42% of PVSD for 70% SS, 9.71% GW, 20.29% CD and780 ml of CMV and 26.23% of PVSD for 75.5% SS, 24.5% CD. Theresults obtained from the experiment were found to be compara-ble with the prediction. The small variation in the results can beattributed to change in the composition of substrates used for thevalidation experiments.

4. Conclusions

Augmented simplex centroid design was used to design theexperiment for the co-digestion of SS with GW, CD and FJW. Ina co-digestion experiment, the proportion of each component inmixture plays a significant role. From the co-digestion experi-ments, it was found that the mixtures containing SS and CD inmajor proportion with GW in minor proportion was having syn-ergic effect with CMV being highest. Addition of GW in major

proportion results in lower CMV values as the grass contains ligno-cellulosic matter which is not easily degradable by microorganismsand generally requires pre-treatment for making bio-available. Theaddition of FJW in major proportion to the mixtures was found

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86 P.V. Rao, S.S. Baral / Chemical En

o be antagonistic as it acidifies rapidly and resulting in droppinghe pH significantly. The sudden drop in pH will also inhibit the

ethanogens which are the key organisms to convert the acidsnto methane. RSM was used to interpret the interactions betweenhe mixture components. Contour and 3D surface plots were moreseful to find the zones of optimum mixture for maximising theesponses. This can be used in plants to generate the maximum out-ut from the digestion process. It is important to note that it mayot be always be feasible to feed the plant with optimum compo-ition of the mixtures as it requires storing some of the substratesased on the need. The study of mixture design for finding the opti-um proportion of the mixture ingredients helps the AD process

o get maximum yield.

cknowledgements

The authors are thankful to the Director, Birla Institute ofechnology and Science, Pilani K.K. Birla Goa Campus for giving per-ission to publish the work. The authors are also thankful to theOD, Chemical Engineering and the Faculty In-Charge, RCEDD, Birla

nstitute of Technology and Science, Pilani K.K. Birla Goa Campusor their constant moral support and encouragement throughouthe study.

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