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Methanogenic activity optimization using theresponse surface methodology, during theanaerobic co-digestion of agriculture and industrialwastes. Microbial community diversity

J. Jim�enez a,*, Y. Guardia-Puebla b, O. Romero-Romero a,M.E. Cisneros-Ortiz c, G. Guerra d, J.M. Morgan-Sagastume c, A. Noyola c

a Universidad de Sancti Spıritus, Ave. de los M�artires, No 360, CP 60100 Sancti Spıritus, Cubab Universidad de Granma, Carretera de Manzanillo, Km 17 ½, CP 85100 Bayamo, Granma, Cubac Instituto de Ingenierıa, Universidad Nacional Aut�onoma de M�exico, Circuito Escolar S/N Ciudad Universitaria,

Delegaci�on Coyoac�an, CP 04510 D.F. M�exico, Mexicod Facultad de Biologıa, Universidad de La Habana, Calle 25 e/ I y J, Vedado, CP 10400 La Habana, Cuba

a r t i c l e i n f o

Article history:

Received 12 August 2012

Received in revised form

4 April 2014

Accepted 24 October 2014

Available online 14 November 2014

Keywords:

Clay

Pig manure

Optimization

Rice straw

Specific methanogenic activity

Microbial community

* Corresponding author. Tel.: þ53 41664137.E-mail addresses: janet@uniss.edu.cu (J.

Cisneros-Ortiz), ggr@fbio.uh.cu (G. Guerra),(A. Noyola).http://dx.doi.org/10.1016/j.biombioe.2014.10.00961-9534/© 2014 Elsevier Ltd. All rights rese

a b s t r a c t

The anaerobic co-digestion of manure, agriculture and industrial wastes for methane

production depends on the nutritional condition to develop the microbial community. The

effect of each substrate concentrations, as well as their interactive effects on specific

methanogenic activity and microbial community diversity were investigated in this work.

A central composite design and the response surface methodology were applied for

designing the anaerobic co-digestion batch test at 35 and 55 �C. It was analyzed the

anaerobic sludge by specific methanogenic activity (SMA) and using molecular techniques

(terminal restriction fragment length polymorphism, TRFLP). The results showed a sig-

nificant interaction among the substrates and an enhancement of the methane production

and SMA response caused by the three components. Rice straw had lower influence on

SMA than clay residues, due to the mineral content and the beneficial ammonia nitrogen

adsorbent properties of the latter. The optimum condition for mesophilic and thermophilic

anaerobic co-digestion of pig manure, rice straw and clay mixture allowed SMA values of

1.31 and 1.38 gCH4-COD/gVSSd�1, respectively. The TRFLP analysis showed the effect of rice

straw and clay addition on microbial community diversity at both temperatures. The

acetotrophic methanogens belonging to the order Methanosarcinales (genera Methanosarcina

and Methanosaeta) dominated in mesophilic condition, whereas at thermophilic conditions

dominated Methanomicrobiales and Methanobacteriales order. The optimization allowed

identifying the substrate interaction effects in a concentration range with a reduced

number of experiments. Besides, the model validation proved to be useful for defining

optimal combination of wastes in anaerobic system.

© 2014 Elsevier Ltd. All rights reserved.

Jim�enez), yguardiap@udg.co.cu (Y. Guardia-Puebla), mec@pumas.iingen.unam.mx (M.E.jmm@pumas.iingen.unam.mx (J.M. Morgan-Sagastume), noyola@pumas.iingen.unam.mx

23rved.

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 85

1. Introduction

The co-digestion of different wastes may improve nutrient

balance and cause synergy effects, overcoming substrate

deficits [1]. Moreover, this type of waste management may

improve methane yield and increase the efficient use of

equipment by processing different waste streams in a single

facility. The co-digestion of manure and industrial organic

wastes has been widespread in Europe [2] and reports on in-

dustrial applications of this concept have been published

[3e9].

Manures are an abundant source of organic material that

can be used as feedstock in anaerobic digesters [10,11]. How-

ever, manures often contain concentrations of ammonia

greater than necessary formicrobial growth, whatmay inhibit

the anaerobic digestion [12,13]. On such cases, the anaerobic

digestion of pig manure could be enhanced using agriculture

wastes as co-substrates, due to their high content of carbon

and subsequent improvement of carbon nitrogen (C/N) ratio

[1]. This way, rice straw could be a promising feedstock

biomass as co-substrate for pig manure anaerobic digestion,

mainly due to the low costs of this waste biomass [14].

In addition, some clays and zeolite have been described as

a means to reduce ammonia inhibition in the anaerobic

digestion of manure [12,15e19]. Angelidaki and Ahring [16]

used bentonite clay for the anaerobic thermophilic digestion

of cattle manure. Milan et al. [17], used a natural zeolite, a

modified zeolite [15] and a mixture of clinoptilolite, morden-

ite, montmorillonite and others [18], for the anaerobic diges-

tion of pig waste. Tada et al. [19], also found that natural

mordenite had a synergistic effect on the Ca2þ supply as well

as on NH4þ removal during the anaerobic digestion of a sludge

with high ammonia content (NH4Cl 4500mg L�1). On the other

hand, the metal contained on clays could be used by the

anaerobic microorganisms as part of their enzyme structure

and has a significant effect on the anaerobic degradation of

VFA, being this an ongoing research subject [20e24].

Despite the advantages of the co-digestion process, the

addition of co-substrate from a different typology can provoke

cell toxicity, that is why the optimization of substrate con-

centration, temperature and others factors that affect the co-

digestion process is necessary [1,25e27]. Response surface

methodology (RSM) is feasible to solve this kind of problem,

since it is a statistical technique for designing experiments,

building models, evaluating the effects of several factors and

searching optimum conditions for desirable responses,

maintaining a reduced number of experiments.With RSM, the

interaction of possible influencing parameters on methane

production can be effectively evaluated [28]. Furthermore,

central composite design is a fractional factorial design

effective for sequential experimentation to obtain a reason-

able amount of information for testing lack of fit while a large

number of design points are not involved [28].

In this sense, different molecular methods have allowed

[29,30] a better approach to the physiology of the microor-

ganisms involved in this process. The terminal restriction

fragments length polymorphism analysis (TRFLP) is an effec-

tive tool to determine the molecular composition of the mi-

crobial community and the relative abundance of certain

species [31]. This tool has been successfully applied in

different ecosystems such as methanogenic reactors [32,33].

The interactive effects of substrate concentration onmethane

production from co-digestion of pig manure, rice straw and

clay residues as inorganic additives have not been reported

yet. Consequently, the main objective of this work was to

investigate the effect of pig manure, rice straw and clay res-

idue concentration, as well as their interactive effect, on the

specific methanogenic activity and microbial community di-

versity at mesophilic and thermophilic conditions.

2. Methods

2.1. Inoculum and wastes sources

Two anaerobic inoculums were used depending on the tem-

perature tested: adapted to mesophilic (35 ± 2 �C) and to

thermophilic (55 ± 2 �C) conditions, both fed with pig manure

collected at the Veterinary School at Autonomy National

University of Mexico (UNAM). Rice straw was collected from

Rice Cuban Enterprise “Sur del Jibaro” and the clay residuewas

taken from the oil clarification process at Refinery and Petro-

chemical Industry “Sergio Soto”, both located in the province

of Sancti Spıritus. The pigmanurewas kept at 4 �C and the rice

straw and clay residual were kept at environmental temper-

ature until use. The characteristics of the substrates are

shown in Table 1.

Experiments were carried out in batch tests, containing

mineralmedium (1%), vitamins solutions (1%), micronutrients

(1%), resazurin (0.1%) and cystein (1 g L�1). Rice straw was

pretreated by size reduction. The oily residue was removed

from clay using absorbent paper. All bottles were inoculated

in an anaerobic chamber and incubated at mesophilic condi-

tion (35 ± 2 �C) or thermophilic condition (55 ± 2 �C), during30 days. Bottles with inoculum without substrate, and inoc-

ulum with pig manure were used as blanks.

2.2. Analytical techniques

The anaerobic process was monitored by means of total sus-

pended solids (TSS), volatile suspended solids (VSS), pH (pH-

conductivity meter, OAKTON, EUTECH Instrument,

Singapore), and alkalinity, determined according to the Stan-

dard Methods for the Examination of Water and Wastewater

[34]. The alkalinity ratio (a) was calculated as the quotient of

partial alkalinity (at pH 5.75) and total alkalinity (at pH 4.3).

2.3. Methane production and specific methanogenicactivity

The methane production was determined every day by gas

chromatography (Fisher Gas Partitioner model 1200 with a

thermal conductivity detector and a Porapak Q column).

Subsequently, the total methane in the bottle gas space was

determined. SMA was calculated with the slope of the accu-

mulatedmethane production curve (mL d�1) in the first 5 days,

divided by the amount of VSS introduced in the bottle

Table 1 e Characteristics of the inoculum and wastes used.

Parameters Inoculum Pig manure Rice straw Clay residual

Mesophilic Thermophilic

TS (%) 1.22 1.04 6.3 94.6 97.7

VS (%) 0.79 0.64 5.3 70.4 41.3

Total-N (g kg�1) e e 4.12 8.96 0.0

NeNH4þ1 (g kg�1) 0.68 0.53 2.24 0.28 0.0

C:N ratio e e 19 48 0.0

Alkalinity (gCaCO3 L�1) 2.1 2.2 2.7 e e

Alkalinity ratio 0.8 0.7 0.45 e e

pH 8.0 7.9 7.9 e e

Naþ (mg kg�1) e e 245.8 1972 393.5

Kþ (mg kg�1) e e 2100 12.186 527.5

Mg2þ (mg kg�1) e e 1824 18.76 42.48

Ca2þ (mg kg�1) e e 1761 1309 3708

PO43� (mg kg�1) e e 7019 2.95 <0.5

Al (mg kg�1) e e 271.9 5936 13.127

Fe (mg kg�1) e e 442.4 4754 1510

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 786

(inoculum) using the proper conversion factor to report it as:

gCH4-COD gVSS�1 d�1.

2.4. Central composite design experiments

In order to optimize the concentration of each waste and to

analyze the effect of their interaction, a 23 factorial central

composite design was used based on the STATISTICA software

(version 8.0, StatSoft Inc., USA). The factorial design was

amplified by six axial points, and two replications of center

points. The center runs provided a means for estimating the

experimental errors and a measure of lack of fit. The axial

points were added to the factorial design for estimating the

model curvature.

The experiments were tested in three replicates at both

temperature conditions (35 �C and 55 �C) for the selected

variables: manure (A), straw (B) and clay (C), in concentration

ranges of 9.1e36.6 gVSS L�1, 7.0e17.6 gVSS L�1 and

0.8e8.3 gVSS L�1, respectively. The effect of each factor was

evaluated under both low and high range of the remaining

factors. The concentration of each waste was chosen as three

independent variables in this experiment design; SMAwas the

dependent variable and was calculated as the average of the

replicates.

The dependent variable was fitted using a predictive

polynomial quadratic equation in order to correlate the

response variable to the independent variables. The general

shape of the predictive polynomial quadratic equation is:

Y ¼ N0 þXk

i¼1

Nixi þXk

i¼1

Niix2i þ

Xk

i¼1

Xk

j¼1

Nijxixj (1)

where Y is the response (dependent variable SMA); xi is the

input variable (concentration of manure, straw and clay),

which influence the response variable Y; N0 the i linear coef-

ficient; Nii the quadratic coefficient and Nij is ij interaction

coefficient.

Each independent variable and their interaction were

compared to verify the feasible hypothesis regarding the

treatment effect and their estimation with 0.05 of confidence

level. The data normality was confirmed through the

construction of the residue normal probability graphic. The

standardized effects of the independent variables and their

interactions on the dependent variable were also calculated

by preparing a Pareto chart.

The quality of fit of the model equation was expressed by

the determination coefficient (R2) and the adjusted determi-

nation coefficient (R2adj). The model statistical significance was

determined by a Fisher test (F-test) based on the p-value with

95% of confidence level. Also, the correlation coefficient (R), the

DurbineWatson (DW) statistic, the sum of squares (SS), the

middle sumof squares (MSS), and chi-square (c2) testwere used

to analyze the statistical significance of the quadratic model.

The total percentage of contribution (PC) achieved from the

lineal models, quadratic terms and interactions were calcu-

lated by the equations two, three and four as follows (Eqs

(2)e(4)):

TPCi ¼Pn

i¼1 SSiPni¼1

Pnj¼1 SSi þ SSj þ SSij

*100 (2)

TPCii ¼Pn

i¼1 SSiiPni¼1

Pnj¼1 SSi þ SSj þ SSij

*100 (3)

TPCij ¼Pn

i¼1 SSijPni¼1

Pnj¼1 SSi þ SSj þ SSij

*100 (4)

where: TPCi, TPCii, and TPCij are the total percentage contri-

butions (TPC) of first-order, quadratic and interaction terms,

respectively. Similarly, SSi, SSii and SSij are the computed sum

of squares for first-order, quadratic and interaction terms,

respectively [35].

Finally, three-dimensional (3D) response surfaces and two-

dimensional (2D) contour plots were constructed to give an

insight about the effects of the different testedwastes and their

interaction on the methane production measured as SMA.

2.5. Cultivation independent analysis of the microbialcommunity structure

Sampling and DNA extraction: For microbial community

analysis samples from the CSTR were taken (i) before the co-

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 87

digestion process, i.e. inoculum only fed with manure kept in

the control reactor, (ii) from the co-digestion of manure and

straw, and (iii) during and after clay supplementation.

Genomic DNA was isolated using the PowerSoil® DNA Isola-

tion Kit (Mo Bio Laboratories Inc., USA) according to the

manufacturer's instructions. For each crude sample, DNA

from two subsamples was extracted. The extracted DNA was

then used as template for TRFLP andQ-PCR analyses aswell as

for the construction of 16S ribosomal RNA gene clone libraries

to characterize the diversity and dynamic of the Bacterial and

Archaeal consortia during the experimental approach

described above.

TRFLP analysis: The TRFLP analyseswere in general carried

out following the protocol proposed above [33]. For the

amplification of the 16S rRNA gene the primer pairs 27f (50

AGAGTTTGATCMTGGCTCAG 30, [36,37]) and 926MRr (50

CCGTCAATTCMTTTRAGTTT 30, [38,39]) (for bacteria) and the

primer pairs Ar109f (50 ACKGCTCAGTAACACGT 30, [40]) andAr912r (50CTCCCCCGCCAATTCCTTTA 30; [41]) (for Archaea)

were used, where both forward primers were labeled with

Indodicarbocyanine (Cy5) at the 50-end. The TRFLP-PCR was

carried out in three replicates per DNA sample. After purifi-

cation of the PCR products, a total amount of 200 ng PCR

products were digested with MspI and Hin6I in case of the

Bacterial assay or with AluI for the Archaeal assay. All en-

zymes were provided by Thermo Scientific Fermentas, Ger-

many. As described in detail [33] the digestion fragments were

electrophoretically separated and detected by fluorescence

using a GenomeLab™ GeXP Genetic Analysis System (Beck-

man Coulter, Krefeld, Germany). The obtained data were pre-

analyzed using the GeXP analysis software (version 10.2)

considering the size calculation of the detected terminal re-

striction fragments (TRFs) based on the migration time of the

applied size standard. All fragments with a sequence length

between 60 and 650 bpwere used for further analyses with the

online available software package T-Rex [42]. The identifica-

tion of “true” peaks by distinguishing baseline “noise” from

Table 2 e Specific methane activity (SMA) observed (Obs.) andstraw and clay residue.

Run Pig manure(gVSS L�1)

Rice straw(gVSS L�1)

Clay residu(gVSS L�1)

1 9.10 7.00 0.80

2 9.10 7.00 8.30

3 9.10 17.60 0.80

4 9.10 17.60 8.30

5 36.60 7.00 0.80

6 36.60 7.00 8.30

7 36.60 17.60 0.80

8 36.60 17.60 8.30

9 9.10 12.30 4.55

10 36.60 12.30 4.55

11 22.85 7.00 4.55

12 22.85 17.60 4.55

13 22.85 12.30 0.80

14 22.85 12.30 8.30

15 22.85 12.30 4.55

16 22.85 12.30 4.55

a Specific methanogenic activity (g CH4-COD gVSS�1 d�1).

signals of fluorescently labeled fragments [43] as well as the

alignment (binning) of TRFs with a threshold of 0.5 [44] were

based on the evaluation of the peak height. In a last evaluation

step, TRFs were visualized by their relative distribution

considering that TRFs with a relative abundance lower than

2% were removed from the analyses.

3. Results and discussion

The results showed an increase on methane production dur-

ing the anaerobic co-digestion of pig manure, rice straw and

clay residues at all level mixture in respect to the blank used

(data not shown), similarly to results previously obtained

[11,14]. But the reactionmagnitudes were different in all cases

showing different metabolic behavior. With respect to the

anaerobic process, the values obtained at the end of each

experimental condition -pH (7.4e8.1), alkalinity

(2.98e3.80 gCaCO3 L�1), a (0.67e0.84) and the VSS removal

(72e85 %)- were acceptable, indicating the process stability.

3.1. Determination of the regression model and thewaste effect on specific methanogenic activity

The design matrix in actual terms and the experimental re-

sults of SMA are presented in Table 2. The experimental data

were fitted to equation one, resulting in a mathematical

regression second-ordermodel to explain the behavior of SMA

in mesophilic and thermophilic conditions respectively in

terms of uncoded as follows:

SMA ¼� 1:12217þ 0:156522$Aþ 0:0986102$B� 0:0533851$C

� 0:00239521$A2 � 0:00251286$A$B� 0:00267879$A$C

� 0:00366126$B2 � 0:0124214$B$C� 0:00233563$C2

(5)

predicted (Pred.) at various waste levels: pig manure, rice

e SMAa (35 �C) SMA (55 �C)

Obs. Pred. Obs. Pred.

0.27 0.36 1.11 1.15

0.42 0.36 0.61 0.54

0.42 0.40 1.02 0.89

1.26 1.32 0.29 0.29

1.26 1.20 0.25 0.27

0.56 0.58 0.69 0.84

0.38 0.45 0.15 0.25

0.84 0.75 0.85 0.83

0.84 0.76 0.74 0.90

0.84 0.89 0.96 0.72

1.12 1.12 1.45 1.32

1.26 1.23 1.14 1.19

1.26 1.17 1.31 1.28

1.26 1.32 1.32 1.27

1.26 1.29 1.27 1.35

1.26 1.29 1.27 1.35

Table 3 e Analysis of the variation of SMA for the

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 788

SMA ¼ 0:358717þ 0:0899109$Aþ 0:01027$B� 0:0793558$C2

experiments carried out in mesophilic (35 �C) andthermophilic (55 �C) conditions.

Source SSa DFb MSc F e ratio P e value

Mesophilic (35 �C)Model 2.1533 9 0.2393 25.61 0.00041d

A: Manure 0.0449 1 0.0449 4.81 0.07086

B: Straw 0.0281 1 0.0281 3.01 0.13359

C: Clay 0.0563 1 0.0563 6.02 0.04952d

A2 0.5549 1 0.5549 59.41 0.00025d

AB 0.3160 1 0.3160 33.83 0.00114d

AC 0.1891 1 0.1891 20.25 0.00411d

B2 0.0312 1 0.0312 3.34 0.11735

BC 0.4278 1 0.4278 45.80 0.00051d

C2 0.0040 1 0.0040 0.42 0.53873

Error 0.0560 6 0.0093

Total 2.2093 15

Thermophilic (55 �C)Model 2.3820 9 0.2647 9.24 0.00682d

A: Manure 0.0757 1 0.0757 2.64 0.15519

B: Straw 0.0436 1 0.0436 1.52 0.26365

C: Clay 0.0006 1 0.0006 0.02 0.88608

A2 0.7791 1 0.7791 27.20 0.00199d

AB 0.0276 1 0.0276 0.96 0.36412

AC 0.7021 1 0.7021 24.51 0.00258d

B2 0.0256 1 0.0256 0.90 0.38063

BC 0.0001 1 0.0001 0.004 0.95207

C2 0.0163 1 0.0163 0.57 0.47926

Error 0.1719 6 0.0286

Total 2.5538 15

a Sum of squares.b Degrees of freedom.c Mean of squares.d p-value <0.05 were considered to be significant.

� 0:00290271$A þ 0:00100343$A$Bþ 0:0056667$A$C

� 0:00369502$B2 þ 0:00091195$B$C� 0:0061364$C2

(6)

where SMA is the predicted value from regression equation

and A, B, and C are the substrate concentration (pig manure,

rice straw and clay residual, respectively). The optimum

values from the selected variables were obtained by Eqs. (5)

and (6) and also analyzing the response surface contour plots.

On the other hand, the analysis of variance (ANOVA) was

conducted to study the significance of fit from second-order

polynomial equation for the experimental data. The SMA re-

sults obtained at various wastes combinations using ANOVA

are summarized in Table 3 for mesophilic and thermophilic

conditions.

The p-values lower than 0.05 indicated that model terms

were significant. The ANOVA showed that quadratics models

were the most significant, illustrated by the F-test value

(Fmodel 35 �C¼ 25.61 and Fmodel 55 �C¼ 9.24) because the p-values

were lower than 0.05 (p-value ¼ 0.0041 and p-value ¼ 0.00682,

for mesophilic and thermophilic conditions, respectively).

Besides the F-value calculated (Fcal ¼ MSmodel/MSerror) were

higher than F-value tabulated (Fa;df,(n�dfþ1) F0.05,9,6 ¼ 4.1) at the

5% level of confidence. This indicated that the F-value ach-

ieved was enough to justify the quadratic model adequacy,

also showed that all waste combinations were significant.

These results are similar to reported by some authors

[35,45,46].

The higher F-test values and the p-value lower than 0.05

reject the null hypothesis and showed a significant effect from

both models. In mesophilic conditions the ANOVA (Table 3)

shows the significant effect of the quadratic effect of manure

(A2), manure-straw interaction (AB), manure-clay interaction

(AC) and straweclay interaction (BC), also the lineal effect of

Clay (C) was significant. Nevertheless, the p-value (p-

value¼ 0.04952) showed a little effect on the regressionmodel.

This result accorded with the quadratic effect of clay (C2)

which the least effect on the response showed. Besides, the

addition of straw did not show a significant effect on SMA. In

thermophilic condition, the quadratic effect of manure (A2),

and manure-clay interaction (AC) had a significant effect on

SMA, with p-values of 0.00199 and 0.00258, respectively.

However, the clay and the straweclay interaction showed the

slightest effect on SMA. The significant effect of temperature

condition on anaerobic processes is evident, essentially the

different substrate typology assimilation by the anaerobic

consortia previously reported [25,47].

The model adequacy was better-quality in mesophilic

conditions than in thermophilic conditions, where the deter-

mination coefficient (R2 ¼ 0.97463) showed that only 2.54% of

total variation could not be elucidated from regression model.

For thermophilic condition (R2 ¼ 0.9327) this value was higher

(6.73%) (Fig. 1). Nevertheless, both models confirm the

quadratic model adequacy to estimate the experimental data

with high-degree of fit. That was also corroborated by means

of the adjusted coefficient of determination (R2adj ¼ 0.93658 and

R2adj ¼ 0.83174 for mesophilic and thermophilic condition,

respectively). These values were close to the determination

coefficient (R2) values achieved for both condition described

above. It indicated the fine adjustment considering this size

sample and model component number. These results were in

agreement with the higher values for both coefficient ach-

ieved by Yetilmezsoy et al., and Liu et al. [35,46], using an

experimental design for response surfaces. On the other hand,

the correlation coefficient (R) was important to analyze the

relationship between the SMA values observed and the SMA

values predicted by the model. In mesophilic conditions the

elevated values of correlation coefficient (R35 �C ¼ 0.987235)

showed a positive and strong link due to the exponential slope

growth (Fig. 1a). In contrast, in thermophilic conditions

(Fig. 1b) the correlation coefficient (R55 �C¼ 0.804312) was lower

than in mesophilic condition, even though this values was

high too. In addition, the dispersion between the data

observed and the values predicted by models showed that the

points were close to the diagonal line, it evidences the high-

quality of fit in both temperature conditions.

The DurbineWatson (DW) statistic was used to determine

if autocorrelation of first-order existed and to test the linear

association between adjacent residuals, that is, whether

autocorrelation, or correlation between errors, is present in

the model [35]. In both cases, the DW statistic ranges were

nearly two, showing the high-quality of fit in both tempera-

ture conditions. In mesophilic conditions, DW was 2.38229 (p-

value35 �C ¼ 0.1623) while in thermophilic condition, DW was

2.47364 (p-value55 �C¼ 0.1241). In both cases, the p-values were

Fig. 1 e Graphics showing the correlation between the observed and predicted values for both temperatures.

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 89

over 0.05, what means that there is not a serial correlation

between the residues.

In addition, the chi-square (c2) test were carried out to

confirm whether there was a significant difference between

the data observed and the values predicted by the models. In

both temperature conditions the chi-square calculated (c2cal

35 �C ¼ 0.085; c2cal 55 �C ¼ 1.458) were lower than the chi-square

tabulated (c2tab (a; df�1) ¼ c2tab (0.05; 15) ¼ 7.261). Therefore there

is no significant difference between the response observed

and the response predicted by the models. Chi-square test

corroborates that the quadraticmodel proposedwas adequate

to optimize SMA as the response variable from this waste

anaerobic co-digestion process.

The SMA values had a wide range (0.27e1.26 gCH4-

COD gVSS�1 d�1 and 0.15e1.45 gCH4-COD gVSS�1 d�1 at mes-

ophilic and thermophilic conditions, respectively), and it

clearly showed that the SMA were strongly affected by the

selected variables (concentration andwastes typology). This is

corroborated by the wide range of term coefficient values.

The technique of arranging data according to priority or

importance in a problem-solving framework is called Pareto

analysis. Pareto charts are used extensively by improve-

ment teams all over the world; indeed the technique has

become fundamental to their operation for identifying the

really important problems and establishing priorities for

action [48]. Thus, the standardized effects of the indepen-

dent variables and their interactions on the dependent

variable, SMA, were investigated with a Pareto chart (Fig. 2).

In mesophilic conditions, the standardized effect of (C2), (B),

(B2) and A were below the reference line, considering 0.05 of

probability, even if the C component was at limit position.

This showed a lower effect on the SMA. However, the

negative coefficients for the model components (bars with a

minus sign): the quadratic component (A2) and the inter-

action coefficient of (AB) and (AC) indicated an antagonistic

effect on the SMA; while the positive coefficients for the

model component (bars with a plus sign), BC showed a

favorable or synergistic effect on the SMA (Fig. 2a). In

contrast, in thermophilic conditions, the standardized effect

of (BC), (C), (C2), (B2), (AB), (B) and (A) did not contribute to

the prediction model of SMA. The negative coefficient for

the model component A2 was the only one that showed an

unfavorable or antagonistic effect on the SMA, while the

positive coefficient for the model components (AC) showed

a favorable or synergistic effect on the SMA (Fig. 2b). Similar

results were obtained by Campos [1] during the co-digestion

of manure with agriculture waste and other kinds of clays

from olive oil clarification. Given its low effect in thermo-

philic conditions, straw apparently does not represent an

available substrate to methanogenic consortia, due to its

low rate of hydrolysis [49,50].

The significance of each coefficient determined by Stu-

dent's t-test and p-values were illustrated in Table 4. The t-

value represents the ratio of the estimated parameter effect at

an estimated parameter standard deviation. In mesophilic

conditions the principal effect (C) the quadratic effect (A2) and

the first-order interaction effect (AB), (AC) and (BC) were the

most significant on SMA during these waste co-digestion

processes. These results agreed with ANOVA (Table 3). How-

ever, in thermophilic conditions, only the interaction co-

efficients (AB) and (AC) were significant; what shows the effect

of temperature on the SMA response, taking into consider-

ation the substrate concentration and its typology. The per-

centages of contribution (PC) for each individual factor from

the model were calculated and tabulated (Table 4). The best

contributions were in accordance with the best t-values and

its lowest probability values, similar to the result achieved by

Yetilmezsoy et al. [35].

3.2. Three-dimensional (3D) response surface andcontour plots

The three-dimensional response surface plots are a function

of two factors, maintaining all other factors at fixed levels,

presented as a solid surface framed in a three-dimensional

space. This graphic presentation helps to understand the

main and the interaction effects of those two factors [35,51].

At the same time, 3D response surfaces and their corre-

sponding contour plots can facilitate the straightforward ex-

amination of the effects of the experimental variables on the

responses [52]. In this work, since the regression model had

three independent variables, one variable was held constant

at the center level. Figs. 3 and 4 showed the 3D response

surfaces plot for SMA and the corresponding contour plots (in

Fig. 2 e Pareto chart showing the standardized effect of independent variables and their interaction on the SMA at both

temperature conditions. The length of each bar in the chart indicates the standardized effect of each factor.

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 790

a two-dimensional plane) as the functions of two variables at

mesophilic and thermophilic condition, respectively. The

nonlinear nature of all 3D response surfaces and corre-

sponding contour plots demonstrated that there were

considerable interactions between each of the independent

variables. In other words, there was no direct linear relation-

ship among the selected independent variables, supporting

the adequacy of the second-order model to predict SMA

values.

In both temperature conditions, the manure had a note-

worthy effect on SMA (Figs. 3a, 3b, 4a and 4b). At extreme

values of manure, SMA decreased, but increased at central

levels (17.5 gVSS L�1). Themanure levels close to 36.6 gVSS L�1

decreased drastically the SMA values because of the ammonia

nitrogen content in manure, as it was described above.

According to the results, there was no evidence that straw

affect the SMA values. In mesophilic conditions a synergic

effect of straw and clay was achieved (Figs. 3a and 4a). In

thermophilic conditions the straw had not a significant effect

on the SMA. The synergic effect of manure and straw at low

concentration showed very low impact on SMA, however with

the straw concentration increase, the SMA values enhanced.

However, in thermophilic conditions a decrease was observed

on the SMAwith a little augmentation of straw concentration.

Actually, even at thermophilic conditions, straw apparently

does not represent an available substrate to methanogenic

Table 4 e Coefficient and significance of the components for the quadratic model.

Factor Coefficient Effect SEa t-ratiob p-value SSd PCe

35 �C Intercept 1.2859

A 0.067 0.134 0.0611 2.1922 0.0709 0.0449 2.72

B 0.053 0.106 0.0611 1.7341 0.1336 0.0281 1.70

C 0.075 0.150 0.0611 2.4540 0.0495c 0.0563 3.40

A2 �0.4588 �0.9176 0.1190 �7.7078 0.0002c 0.5549 33.59

AB �0.1988 �0.3975 0.0683 �5.8165 0.0011c 0.3160 19.13

AC �0.1538 �0.3075 0.0683 �4.4995 0.0041c 0.1891 11.45

B2 �0.1088 �0.2176 0.1190 �1.8277 0.1174 0.0312 1.89

BC 0.2313 0.4625 0.0683 6.7676 0.0005c 0.4278 25.89

C2 �0.0388 �0.0776 0.1190 �0.6517 0.5387 0.0040 0.24

55 �C Intercept 1.3524

A �0.087 �0.174 0.107000 �1.6255 0.1552 0.0757 4.53

B �0.066 �0.132 0.107046 �1.2331 0.2636 0.0436 2.61

C �0.008 �0.016 0.107046 �0.1495 0.8861 0.0006 0.04

A2 �0.5436 �1.0872 0.208481 �5.2151 0.0020c 0.7791 46.63

AB 0.0588 0.1175 0.119681 0.9818 0.3641 0.0276 1.65

AC 0.2963 0.5925 0.119681 4.9507 0.0026c 0.7021 42.02

B2 �0.0986 �0.1972 0.208481 �0.9461 0.3806 0.0256 1.53

BC 0.0038 0.0075 0.119681 0.0627 0.9521 0.0001 0.01

C2 �0.0786 �0.1572 0.208481 �0.7542 0.4793 0.0163 0.98

a Standard error.b t-ratio ¼ Effect

SE .c p-value <0.05 were considered to be significant.d Sum of squares.e Percentage contribution n ¼ SSP

SS*100.

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 91

consortia, as described above. However the great availability

of rice straw in the area studied could justify this study, which

showed again the necessity of pretreating rice straw to

enhance its methane potential.

A synergic effect between manure and clay was observed

(Figs. 3b and 4b). At mesophilic conditions, an increase of SMA

values were achieved with an increase of clay concentration,

maintaining low levels of manure, but at high levels of

manure the clay concentration decreased the inhibitory effect

due to the manure nitrogen content. This result could be

explained because, as it has been reported [12,15,19], the

ammonia could be absorbed by clay using similar compounds,

such as bentonite, zeolite, mordenite and others. In contrast,

at thermophilic condition the SMA values decreased with the

increase of the clay concentration. This suggests that tem-

perature affects the assimilation of thismineral (clay) and also

the ammonia nitrogen adsorbent properties of clays.

A similar response was achieved for SMA values when

straweclay interaction was analyzed maintaining fixed

manure level (Figs. 3c and 4c). At mesophilic condition SMA

values over 1.5 gCH4-COD gVSS�1 d�1 were obtained at high

levels of straw and low levels of clay, or low levels of straw and

high levels of clay. At thermophilic condition there was

almost not variation on SMA values, but those were lower

than the SMA values achieved at mesophilic condition.

In all cases, evidences of significant interaction among the

dependent variables and nonlinear relationship in the

response were obtained.

3.3. Optimal waste combination for maximizing SMA

The second-order polynomial model applied in this study was

used for determining the optimum condition to maximize

SMA and it was adjusted by minimal quadratic methods. The

effect on the dependent variable SMA at different mixture of

independents variables levels (manure, straw and clay) were

carried out to identify the waste concentration that produced

the maximum SMA value.

Based on the calculation steps defined for the optimization

algorithm, in mesophilic condition the optimal response was

close to the SMA value predicted. Fig. 5a shows the non-linear

behavior of the predicted response at the manure levels

evaluated, whose maximumwas at 18.73 gVSS L�1 of manure,

but the graphics of straw and clay showed the maximum

response at 17.6 gVSS L�1 and 8.3 gVSS L�1 respectively. Thus,

the optimal combination was determined for SMA of

1.31 gCH4-COD gVSS�1 d�1 using 28.35 gVSS L�1 of manure,

17.6 gVSS L�1 of straw and 8.3 gVSS L�1 of clay, with 0.05 of

confidence level in the range from 1.12 to 1.52 gCH4-

COD gVSS�1 d�1. In thermophilic conditions, (Fig 5b) the

optimal combination was obtained with 20.1 gVSS L�1 of

manure, 10.18 gVSS L�1 of straw and 3.05 gVSS L�1 of clay for a

maximized SMA value of 1.38 gCH4-COD gVSS�1 d�1 in a range

from 1.19 to 1.57 gCH4-COD gVSS�1 d�1 with 0.05 of confidence

level. These SMA results obtained at both temperature con-

ditions for the experimental set up assayedmay be considered

as representative for this kind of solid wastes [53e55].

3.4. Model validation

Finally, in order to validate the adequacy of the models pro-

posed to predict SMA for both temperature conditions during

the co-digestion of the wastes evaluated, new experiments

were carried out using the optimal waste concentrations

achieved before. The SMA values achieved were

1.23 ± 0.41 gCH4-COD gVSS�1 d�1 (middle value ± standard

Fig. 3 e Three-dimensional (3D) response surface and the corresponding contour plots showing the effects of interactions on

SMA under mesophilic conditions (35 �C) for two factors, maintaining the third variable fixed at their respective center level.

(a) Manure (A) and straw (B); (b) manure (A) and clay (C); (c) straw (B) and clay (C).

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 792

deviation) at mesophilic condition and 1.35 ± 0.26 gCH4-

COD gVSS�1 d�1 at thermophilic condition.

The Student t-test was carried out to evaluate the relation-

ship between the SMA observed at this optimal waste concen-

tration condition and the predicted SMA values by optimization

studiesdescribedabove.The tabulatedt-valuewereobtainedfor

some freedomdegree values (df¼ sample size (1)þ sample size

(2) e 2) and using 0.05 of confidence level. At mesophilic condi-

tions the t-value calculated (tcal ¼ 0.3038) was lower than the t-

value tabulated (ttab(a; df) ¼ ttab(0.05; 4) ¼ 2.1318) and the p-

Fig. 4 e Three-dimensional (3D) response surface and the corresponding contour plots showing the effects of interactions on

SMA under thermophilic conditions (55 �C) for two factors, maintaining the third variable fixed at their respective center

level. (a) Manure (A) and straw (B); (b) manure (A) and clay (C); (c) straw (B) and clay (C).

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 93

valuecal¼ 0.7765 (p-value >0.05). Therefore, this could not reject

the null hypothesis hence there was no significant difference

between the SMA observed and the optimal SMA values pre-

dicted by themodel. Similarly, at thermophilic conditions the t-

value calculated (tcal ¼ 0.1655) was lower than the t-value tabu-

lated, and thep-valuecal¼ 0.8766, hence therewasnosignificant

difference between the SMA observed and the optimal SMA

values predicted by themodel.

Fig. 5 e Level combination effect of independent variables (A: manure; B: Straw; C: clay) on the SMA and optimization. (a)

Mesophilic; (b) Thermophilic.

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 794

3.5. Cultivation independent analysis of the microbialcommunity structure

The diversity obtained in the initial and final samples (those

after 21 days of manure, straw and clay co-digestion) shows

that there is a tendency to the dominance of certain species

with co-digestion. According to Jaccard index (J0), at meso-

philic condition, 48.4% species were similar in both initial and

final samples, in contrast, at thermophilic condition, only

18.8% of similarity between the initial and final sample was

achieved. These results demonstrated the temperature effect

on the uptake of these co-substrates. It is noteworthy that

there is a 35.5% similarity between the initial samples of

mesophilic and thermophilic microbiota, which can be ex-

pected, taking into account that both inoculums were taken

from the same ecosystem, and were adapted to each tem-

perature condition. However, the community structure

developed at mesophilic condition, with the addition of co-

substrates (final sample), was significantly different from

those developed in thermophilic condition (J0 ¼ 13.5%).

The greatest variety of species of bacteria was obtained in

the final sample reflecting the anaerobic co-digestion of the

three substrates at mesophilic condition, although the Shan-

non index indicated that diversity was higher in the initial

samples, at both temperatures (Table 5). The community

organization expressed as the Evenness Index was similar, in

both initial and final samples; however, significant differences

were noted between each temperature.

The relative abundance of the terminal restriction frag-

ment of the initial samples in both temperature conditions

showed the predominance of the 66, 98 and 231 bp fragments

(Fig. 6). In the final samples that reflect the co-digestion of the

three substrates, the dominance of certain groups was noted

(more pronounced in mesophilic condition). Regarding this,

greater relative abundance of fragments of 89, 97 and 162 bp

in mesophilic condition was found, while in thermophilic

condition an increased in abundance of the 144, 151 and

231 bp fragments was observed. These fragments of 151 bp

belonged to Clostridium species [33].

Bacteroidetes phylum, held a 40% relative abundance (89

and 97 bp fragments) showing that this phylum favors the co-

digestion. It was also found in abundance in reactors that

degrade cow manure and in co-digestion of grass silage in

mesophilic conditions [56]. However, as far as the author

knows, prevalence of this bacteria phylum has not been re-

ported at swine manure anaerobic digestion.

As for the archaeal community, in initial sample of the

mesophilic temperature, predominant fragments of 108 bp,

with a 59.21% relative abundance, fragments of 343 bp (12.5%

relative abundance) and other fragments of 57 and 203 bp

Table 5 e Diversity and community organization in bacteria growing with the co-digestion of pig manure substrates, ricestraw and residual clays. Initial sample: time zero in the co-digestion at each temperature condition; Final sample:anaerobic co-digestion of co-substrates after 21 days of incubation at each temperature conditions.

Samples Temperature (�C) Richness Shannon Index Evenness Variance

Initial 35 22 2.46 0.80 0.10

Final 35 24 2.29 0.72 0.09

Initial 55 20 2.70 0.90 0.11

Final 55 18 2.51 0.87 0.10

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 7 95

were also highlighted (Fig. 6). In the final sample, reflecting

anaerobic co-digestion of the three substrates in mesophilic

condition, the dominance of the 108 bp fragmentwas reduced,

and other species (fragments of 431 and 470 bp) were favored,

where fragments of 470 bp was predominant.

In the initial sample of the thermophilic temperature,

predominantly fragments 343 and 629 bp were detected,

although fragments of 101, 203 and 257 bp were also detected.

In the final sample reflecting anaerobic co-digestion of the

three substrates, the community structure was similar to that

obtained in the initial stage. However, in this case 629 bp

fragments predominated, while the prevalence of 343 bp

fragments decreased. In this case, fragments of 82, 108, 203,

257, 339 bpwere also detected. It is noteworthy that fragments

of 108, 203 and 343 bp were detected, both in the initial sam-

ples and the final samples of both temperature conditions.

About archaea community, Rademacher et al. [33], also

obtained the predominant fragments of 108, 339 and 629 bp

within a two-phase leach bed biogas reactor supplied with rye

silage and straw in thermophilic conditions. Sequencing as-

says revealed that species belonged to the genera

Methanosaetas, Methanotermobacter and Methanosarcinas,

respectively. With the co-digestion at mesophilic temperature

predominated fragments 108 and 470 bp, corresponding to the

families Methanosarcinaceae and Methanosaetaceae respec-

tively, while at thermophilic temperatures proliferate Meth-

anothermobacter species and Methanobacterium, the order

Methanobacteriales (339 bp) and species Methanosarcinas

(629 bp), similar to that reported previously in the degradation

Fig. 6 e Relative abundance of terminal restriction fragments (FT

initial and final samples of the co-digestion of pig manure, rice

thermophilic conditions (55 �C).

of pig manure [57], because they are part of the pig gut flora

and thus could appear in their feces.

Fragments of 343 bp (Methanobacteriales order) were detec-

ted in all cases, indicating its stability against environmental

changes [32]. However, when the co-substrates (final sample

in both temperature conditions) were added, the abundance

of these fragments of 343 bp decreased, in contrast to the re-

sults of other authorswho demonstrated the predominance of

Methanobacteriales orders and Methanomicrobiales in batch re-

actors trying pig residual [30], even in co-digestion with

chickenmanure [58]. The 108 bp fragmentswere predominant

especially at mesophilic condition. These species belong to

the family Methanosaetaceae according Padmasiri et al. [32],

and Rademacher et al. [33],. Fragments of 470 bp also pre-

dominated in mesophilic conditions, species belonging to the

order Methanosarcinales but Methanosarcinaceae family as ob-

tained by Padmasiri et al. [32].

Certain abundance of fragments 431 bp in mesophilic

conditions, with the addition of the three substrates was also

obtained. Kim et al. [30], and Padmasiri et al. [32], obtained

fragments of this size and identified them within the Meth-

anomicrobiales order. Furthermore, the presence of fragments

of 203 bp, the phylogenetic affiliation was detected, what has

not been reported up to date.

Finally, in mesophilic methanogenic conditions aceto-

trophics methanogens of Methanosarcinales order prevailed

and at thermophilic conditions hydrogenotrophic metha-

nogens of Methanomicrobiales and Methanobacteriales orders

were predominant. During the co-digestion of the three

) and their sizes in bp of bacteria (a) and archaea (b) in the

straw and residual clays, at mesophilic (35 �C) and

b i om a s s a n d b i o e n e r g y 7 1 ( 2 0 1 4 ) 8 4e9 796

substrates, the effect of temperature on the diversity and

community structure was demonstrated [59].

4. Conclusions

The results showed that there was a significant interaction

among the substrates evaluated and that the co-digestion of

the three substrates enhanced SMA. At mesophilic conditions

the optimal combinationwas determined for SMAof 1.31 gCH4-

COD gVSS�1 d�1 using 28.35 gVSS L�1 of manure, 17.6 gVSS L�1

of strawand 8.3 gVSS L�1 of clay. In thermophilic conditions the

optimal combination was 20.1 gVSS L�1 of manure,

10.18 gVSS L�1 of straw and 3.05 gVSS L�1 of clay for a maxi-

mizedSMAof 1.38 gCH4-CODgVSS�1 d�1. At central levels of pig

manure (22 gVSS L�1), SMA was enhanced by the co-digestion

with rice straw and clay residues. At higher levels

(36 gVSS L�1), clay residues reduced the inhibitory effect of pig

manure on SMA. In general, the presence of rice straw had a

lower influence on SMA than clay residues, due to the beneficial

adsorption properties of the latter. Thus, it was corroborated

the positive effect of clay as inorganic additive for stimulating

the anaerobic process as a result of its mineral content and its

adsorbent N-rich sludge properties. The T-RFLP analysis results

showed the effect of the anaerobic co-digestion of manure and

straw with clay addition on microbial community diversity at

different temperature conditions. The assays suggest the

prevalence of acetotrophic methanogens in mesophilic condi-

tion, belonging to the order Methanosarcinales (genera Meth-

anosarcina and Methanosaeta) whereas at thermophilic condition

it was showed the prevalence of Methanomicrobiales and Meth-

anobacteriales order. The statistical methods applied in this

study proved to be useful for defining optimal combination of

wastes considering their anaerobic co-digestion. Specific

methanogenic activity was also a good response variable for

that purpose. These results open the door to a new investiga-

tion about the use of these regression models to adjust a

phenomenological model in anaerobic digestion systems.

Acknowledgments

The first author would like to thank the Institute of Engi-

neering, UNAM, and the Leibniz Institute for Agricultural En-

gineering Potsdam-Bornim (ATB), Germany, for research

grants to acomplisch this investigation.

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