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Association of the Chemical Engineers of Serbia AChE
Chemical Industry & Chemical Engineering Quarterly
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Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
V. SANGEETHA1
V. SIVAKUMAR2
1
Department of Food Technology,
Kongu Engineering College,
Perundurai, Tamil Nadu, India
2
Department of Chemical
Engineering, Alagappa College of
Technology Campus, Anna
University, Chennai, India
SCIENTIFIC PAPER
UDC 662.756.3:628.3
DOI 10.2298/CICEQ140612016S
CI&CEQ
BIOGAS PRODUCTION FROM SYNTHETIC
SAGO WASTEWATER BY ANAEROBIC
DIGESTION: OPTIMIZATION AND
TREATMENT
Article Highlights
• Optimization for biogas yield was conducted using response surface methodology
• Mixed culture from sago industry sludge can produce effective biogas
• The optimum condition for biogas production and COD removal was at pH 7 and
32 °C
2
• Adequacy of the model shows R value for COD removal and biogas production was
0.9943 and 0.9880, respectively
•
Abstract
Sago processing industries generate a voluminous amount of wastewater with
extremely high concentration of organic pollutants, resulting in water pollution.
Anaerobic digestion was employed for reduction of COD and maximization of
biogas production using synthetic sago wastewater by batch process. Mixed
culture obtained from sago industry sludge was used as a source for microorganisms. Response surface methodology was used to optimise the variables,
such as pH, initial BOD, temperature and retention time. Statistical results
were assessed with various descriptives, such as p-value, lack of fit (F-test),
coefficient of R2 determination, and adequate precision values. Pareto analysis
of variance revealed that the coefficients of determination value (R2) of COD
and BOD removal and biogas production were 0.994, 0.993 and 0.988, respectively. The optimum condition in which maximum COD removal (81.85%),
BOD removal (91.61%) and biogas production of 99.4 ml/day were achieved
was at pH 7 with an initial BOD of 1374 mg/l, and with the retention time of 10
days at 32 °C.
Keywords: anaerobic digestion, synthetic sago wastewater, biogas production, chemical oxygen demand, optimisation.
Sago, the common edible starch processed from
the tubers of cassava (Mannihotesculenta) is one of
the major tuber crops grown in more than 80
countries in the humid tropics. In the southern part of
India, particularly in Tamil Nadu, there are about 800
small-scale units of sago industries discharging about
40,000 to 50,000 L of sago wastewater and 15 to 30 t
of sludge per unit per day [1,2]. Sago processing
industries generates two types of wastewater; one
resulting from the washing and peeling of cassava in
Correspondence: V. Sivakumar, Department of Chemical Engineering, Alagappa College of Technology Campus, Anna University, Chennai, India.
E-mail: [email protected]
Paper received: 12 June, 2014
Paper revised: 13 April, 2015
Paper accepted: 25 May, 2015
a rotary drum with low chemical oxygen demand
(COD) and the other from the extraction process
which owns a high contaminating load of COD and
biochemical oxygen demand (BOD). Hence, large
quantities of processed water up to 15 m3/t of fresh
cassava root are converted into wastewater, which
must be treated before its release into the environment. The amount of water used to produce one ton
of starch ranges from 10-30 m3 and repeated washing
improves the starch quality [3].
Due to stringent environment protection regulations, it is necessary for the processing industry to
treat wastewater [4]. Hence, it has become mandatory for these units to treat the wastewater for safe
discharge. There is ample space for an effective and
complete treatment system which will ensure a safe
33
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
effluent standard limit and hidden energy recovery in
the form of biogas before the disposal [1]. Viewing the
socio-economic profile of small-scale industrial farming operations, it is necessary to develop a suitable
low-cost treatment method for treatment of sago
wastewater [3]. Physical and chemical methods of
treating the sago wastewater have been unpredictable due to the problem of sludge disposal. Biological
methods are classified into two types: aerobic processes [5-7], which have limited applicability due to
aeration cost [8,9], and anaerobic processes at high
treatment rate such as anaerobic filter [10], hybrid
UASB [11], anaerobic rotating biological contactor
[12] and fluidized bed [1] systems.
From previous literature, it is found that most of
the researchers successfully used anaerobic processes for treatment of sago wastewater [11-13]. Various anaerobic treatment techniques, including conventional method, pave way for sustainable environment [14-18]. Anaerobic treatment has an advantage
of degrading concentrated waste and producing significantly less sludge [4]. Because of variations in
process variables, anaerobic treatment processes are
rare at the industrial scale and easily unstable under
certain circumstances. Therefore, the model has been
developed to optimize the treatment process for COD
removal, BOD removal and biogas production, as
functions of the following operating variables: pH, initial BOD, temperature and retention time. Response
surface methodology (RSM), a mathematical (statistical) technique, is commonly used for developing,
analysing, optimizing, and understanding performance of complex variables in an efficient mode. Recently, it has been successfully applied to different
wastewater treatment for achieving optimization using
experimental designs [19-25]. The advantage of using
RSM is the reduction in the number of experiments,
compared to a full experimental design at the same
level [26].
The objective of the present work is to study the
treatment of COD and BOD removal and biogas production in anaerobic digestion of synthetic sago
wastewater, and also optimizing the effect of the process variables such as pH (4-8), initial BOD
concentration (798–1702 mg/L), temperature (26–34
°C) and retention time (4–12 days) using RSM. A full
factorial Central Composite Design (CCD) was employed for the optimisation of process variables.
Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
MATERIALS AND METHODS
Sago wastewater
Preparation of synthetic sago wastewater was
reported elsewhere [27] and the physicochemical
characteristics of the synthetic sago wastewater were
analysed as per standards of American Public Health
Association (APHA) [28]. The characteristics of the
wastewater were pH: 6.8, COD: 2286 mg/L, BOD:
840 mg/L, TDS: 1237 mg/L, TSS: 537 mg/L, VS: 610
mg/L and VSS: 1015 mg/L.
Experimental setup and procedure
The experiment was carried out in a batch reactor of 1 L capacity (Figure 1) for different time intervals (4-12 days). Mixed sludge from sago industry
was used as inoculum (10 vol.%) containing methanogenic bacteria of Methanosarcina, Methanococcoides, Methanoplanus and Methanospirillum. The
pH was adjusted by 1 M HCl or 1 M NaOH using a pH
meter (1283286 Eutech Instruments, Singapore).
Initial BOD was varied from 798 to 1702 mg/L by
adding sago powder and temperature was adjusted
from 26 to 34 °C with the help of a water bath. The
samples were taken for analysis of COD by the open
reflux method and for BOD by the standard dilution
technique according to APHA [28] and also for biogas
production [13].
Figure 1. Experimental setup.
Experimental design
Four factors and five levels of rotatable CCD
were carried out with 30 experimental runs. Twenty
four experiments were augmented with six replicates
at the design centre to evaluate the pure error. Each
variable was varied from 5 levels and the relationship
between the coded and actual values are described
as follows:
xi =
34
(Xi − X o )
ΔX i
(1)
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
where xi and Xi are the dimensionless and actual
values of the independent variable i, Xo is the actual
value of the independent variable at the centre point
and ΔXi is the step change of Xi corresponding to the
unit variation of the dimensionless value. The variables and its levels are designated as –2, –1, 0, +1 and
+2. The second order polynomial equation was used
to describe the effect of independent variables in
terms of linear, quadratic and interactions:
Y = β o + β1X 1 + β 2 X 2 + β3 X 3 + β 4 X 4 + β12 X 1X 2 +
+ β13 X 1X 3 + β14 X 1X 4 + + β 23 X 2 X 3 + β 24 X 2 X 4 +
Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
COD Removal (Y1) = −513.15 + 15.10 X 1 +
0.061X 2 + 23.152 X 3 + 10.289 X 4 + 0 .009 X 1X 2 +
+2.539 X 1X 3 − 1.062 X 1X 4 + 0.003 X 2 X 3 −
(3)
2
1
−0.0007 X 2 X 4 + 0.165 X 3 X 4 − 6.94 X −
−0.00008 X 22 − 0.669 X 32 − 0.322 X 42
BOD Removal (Y 2 ) = −1418.08 + 55.88 X 1 +
0.21X 2 + 67.62 X 3 + 23.68 X 4 + 0.007 X 1X 2
+1.52 X 1X 3 − 0.115 X 1X 4 + 0.0004 X 2 X 3 +
(2)
+ β34 X 3 X 4 + β11X 12 + β 22 X 22 + β33 X 32 + β 44 X 42
where Y is predicted response, βo is constant coefficient, β1, β2, β3 and β4 are linear coefficients, β11, β22,
β33 and β44 are quadratic coefficients, β12, β13, β14, β23,
β24 and β34 are cross-products coefficients, and X1, X2,
X3 and X4 are input variables (pH, initial BOD,
temperature and retention time).The data obtained
from the response surface methodology on COD
removal and BOD removal and biogas production
was subjected to the ANOVA.
The quality of the fit polynomial model was
stated by the coefficient of determination (R2),
adjusted R2, and its statistical significance was determined by F test. The individual effect of each variable
as well as the effect of the interaction were determined, and numerical optimisation was performed to
determine the optimal solution (maximum COD removal, BOD removal and biogas production).
RESULTS AND DISCUSSION
Statistical analysis and fitting of second order
polynomial equation
Several factors influence the removal of COD
and biogas production from the synthetic sago wastewater, but initial BOD, pH, temperature and retention
time play important roles.
The response COD, BOD and biogas were measured for different runs according to the design matrix
carried out based on the design of experiment and the
values for random runs are shown in Table 1. CCD
seeks to minimise the integral of the prediction variable across the design space. Experimental results
were analysed, approximating the function of COD
and BOD removal and biogas production. The regression equations (3)–(5) shown below are obtained after
the ANOVA:
2
1
(4)
2
2
+0.003 X 2 X 4 − 0.46 X 3 X 4 − 8.14 X − 0.001X −
−1.82 X 32 − 0.66 X 42
Biogas production (Y 3 ) = −2497.74 + 91.60 X 1 +
+0.45 X 2 + 117.33 X 3 + 35.29 X 4 + 0.01X 1X 2
+1.02 X 1X 3 + 0.52 X 1X 4 - 0.0007 X 2 X 3 −
2
1
(5)
2
2
0.002 X 2 X 4 − 0.20 X 3 X 4 − 9.91X − 0.0018 X −
−1.96 X 32 − 1.64 X 42
To check the estimated regression equation for
the goodness of fit, Fishers F-test was employed and
the multiple correlation coefficients R2 was calculated
[21]. The ANOVA results showed the significant response models with highest (p < 0.05) R2 value of
0.994, 0.993 and 0.988 for removal of COD, BOD and
biogas production, respectively. The two different
tests, such as sequential model sum of squares and
model summary statistics are used to decide the adequacy of various models. prob > F values for the
quadratic model were less than 0.0001, while the
maximum adjusted R2 value and predicted R2 value
were found to be 0.989 and 0.970 for COD removal.
Even though the cubic model was found to be aliased, prob > F values were greater than 0.05. Therefore, the quadratic model was chosen for further analysis. Adeq Precision measures the signal-to-noise
ratio; typically a ratio greater than 4 is desirable.
Thus, signal-to-noise ratios of 56.422, 47.407 and
31.701 for removal of COD, BOD and biogas production, respectively, indicate an adequate signal,
and this model can be used to navigate the design
space. The result indicates that the process variables
are significant factors that affect the response variables. The interacting terms significant for removal of
COD, BOD and biogas production are shown in Table 2.
Effect of independent variables on % COD and %
BOD removal
The polynomial equation framed for the above
analysis was expressed as three-dimensional surface
plots to visualise individual and interactive outcome of
factors on the response within the design range.
According to the quadratic model X1, X2, X3 and X4
35
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
CI&CEQ 22 (1) 33−39 (2016)
Table 1. The design of experiment and response for random runs of anaerobic digestion
pH
Int. BOD
T
t
COD Removal, %
BOD Removal, %
X1
X2
X3
X4
Yexp
Ypre
Yexp
Ypre
Yexp
Ypre
1
7
1024
28
10
45.38
47.93
64.62
66.62
72.3
72
2
6
1702
30
8
50.83
49.73
60.81
60.81
54.1
56.26
3
6
798
30
8
43.83
44.22
54.55
54.87
42.6
45.75
4
5
1476
28
6
25.27
26.57
35.27
35.38
26.5
25.54
5
7
1024
28
6
43.24
41.49
55.65
54.54
55.9
52.32
6
6
1250
26
8
38.64
37.8
52.24
51.69
46
45.5
7
6
1250
30
8
63.55
63.95
78.79
81.31
84.6
88.9
8
5
1024
28
6
29.61
30.34
38.08
39.41
21.6
22.37
9
6
1250
30
4
46.02
47.41
56.92
58.4
43.6
48.51
10
8
1250
30
8
56.9
57.23
70.9
72.79
83.1
90
11
4
1250
30
8
16.1
15.06
26.26
24.69
10.1
8.51
Run
Biogas production, ml/day
12
6
1250
34
8
68.52
68.66
72.23
73.1
63.6
69.41
13
7
1024
32
6
62.33
62.62
76.9
74.66
73.6
70.72
14
5
1476
32
10
48.72
49.96
56
56.36
43.6
42.94
15
7
1476
28
6
46.6
45.78
58.33
57.07
65.3
64.67
16
6
1250
30
8
65.21
63.95
80.41
81.31
89.6
88.9
17
5
1476
32
6
34.98
33.65
45.63
44.06
35.1
34.33
18
6
1250
30
8
64.35
63.95
82.56
81.31
90.1
88.9
19
5
1024
28
10
46.36
45.28
54.4
52.41
37.4
37.87
20
7
1024
32
10
73.52
71.71
80.2
79.34
90.4
87.12
21
7
1476
28
10
50.4
50.94
76.8
75.83
82.6
80.72
22
5
1024
32
6
32.2
31.15
47.1
47.32
34.9
32.54
23
6
1250
30
8
64.2
63.95
81.54
81.31
90
88.9
24
6
1250
30
12
72.25
70.16
83.94
82.78
76.4
76.8
25
5
1024
32
10
46.7
48.74
51.25
52.94
45.2
44.77
26
6
1250
30
8
62.5
63.95
82.54
81.31
88.6
88.9
27
7
1476
32
6
72.6
73.17
76.69
77.94
86.4
81.69
28
7
1476
32
10
80.5
80.99
90.21
89.31
96.3
94.47
29
5
1476
28
10
39.3
40.23
52.4
55.07
35.6
37.42
30
6
1250
30
8
63.86
63.95
82.01
81.31
90.5
88.9
Table 2. ANOVA of the second order polynomial equation for COD and BOD removal and biogas production
Source
df
COD Removal, %
BOD Removal, %
Biogas production, ml/day
Coefficient
p-Value
Coefficient
p-Value
Coefficient
p-Value
estimate
Prob > F
estimate
Prob > F
estimate
Prob > F
Model
14
7184.55
< 0.0001
8093.31
< 0.0001
18091.68
< 0.0001
X1
1
2667.67
< 0.0001
3469.21
< 0.0001
9959.30
< 0.0001
X2
1
45.46
0.0010
52.96
0.0018
165.90
0.0043
X3
1
1428.36
< 0.0001
687.05
< 0.0001
858.01
< 0.0001
X4
1
776.46
< 0.0001
891.45
< 0.0001
1199.92
< 0.0001
X1×X2
1
64.92
0.0002
42.87
0.0040
84.18
0.0303
X1×X3
1
412.80
< 0.0001
148.66
< 0.0001
67.65
0.0487
X1×X4
1
72.21
0.0001
0.86
0.6373
17.43
0.2934
X2×X3
1
39.28
0.0018
0.57
0.6999
1.89
0.7249
X2×X4
1
1.63
0.4515
44.72
0.0034
13.14
0.3595
X3×X4
1
7.04
0.1293
54.58
0.0016
10.73
0.4065
X12
1
1324.67
< 0.0001
1818.38
< 0.0001
2694.50
< 0.0001
36
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
Table 2. Continued
COD Removal, %
Source
df
BOD Removal, %
Biogas production, ml/day
Coefficient
p-Value
Coefficient
p-Value
Coefficient
p-Value
estimate
Prob > F
estimate
Prob > F
estimate
Prob > F
< 0.0001
X22
1
493.56
< 0.0001
944.20
< 0.0001
2461.88
X32
1
196.93
< 0.0001
613.25
< 0.0001
1695.16
< 0.0001
X42
1
45.70
0.0010
196.96
< 0.0001
1180.88
< 0.0001
Residual
15
40.96
Lack of fit
10
36.87
Pure error
5
4.08
55.74
0.0549
are the important factors determining Y1 and Y2. The
results shown in Figures 2 and 3 indicate that at pH 7,
COD and BOD removal are 81.85 and 91.16%, respectively. In mixed sludge, methane producing bacteria are sensitive to mesosphilic temperature range;
the graph shows that at 32 °C removal of COD and
BOD were achieved at a maximum. Further increase
in temperature is not significant in COD removal and
also the production of bio gas decreases. Retention
time less than 4 days is insufficient for a stable digestion because initial volatile fatty acid concentration
was high in the wastewater. After 8-10 days there is a
decrease in volatile fatty acid which leads to high
COD removal [29]. Therefore, increase in retention
time increases the COD removal [1,13]. Similarly, increase in retention time increases the BOD removal
due to reduction in organic content of the wastewater
caused by anaerobic digestion.
44.96
10.78
220.55
0.2159
196.27
0.0683
24.28
Figure 3. Effect of temperature and retention time on BOD
removal (%) at optimum pH and initial BOD.
Effect of independent variables on biogas production
Figure 2. Effect of temperature and retention time on COD
removal (%) at optimum pH and initial BOD.
Biological decomposition of organic wastes
results in biogas production. The variation in parameters such as pH, initial BOD, temperature and
retention time are significant factors affecting the
growth of microbes during anaerobic digestion. From
Figure 4 it is observed clearly that an increase in
retention time proportionately increases the biogas
production, which further indicates that a maximum of
99.4 ml/day of the biogas was recovered at optimum
condition. Anaerobic digestion can take place at
either mesophilic or thermophilic temperatures. Even
small changes in temperature from 32–34 °C have
been shown to reduce the biogas production rate.
Hence, mixed sludge was suitable for biogas recovery
in the mesophilic temperature in which anaerobes are
active at 32 °C. pH is an important parameter for
anaerobic digestion. The suitable pH range for
methane producing bacteria is 6.8–7.2. The pH range
of 5.5–6.5 is suitable for acetogenic bacteria. The pH
is maintained with a methanogenic range to prevent
the predominance of the acid forming bacteria [4].
37
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
From the results, it was found that the optimum pH is
7 for biogas production.
Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
thetic sago wastewater using anaerobic digestion is
very effective and the operating variables highly influence the response variables. Hence, this study was
a unique attempt to optimise the treatment and production of effective biogas using anaerobic digestion
treatment. The RSM model helped to identify the
most significant operating factors and the optimum
levels with minimum effort and time.
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Figure 4. Effect of temperature and retention time on biogas
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Optimization of experimental conditions
The optimum region was identified by considering the maximum removal of COD, BOD and biogas
production. The optimized process conditions obtained
at pH 7, initial BOD 1374 mg/L, temperature 32 °C
and a retention time of 10 days showed a maximum
COD and BOD removal of 81.85 and 91.16%, respectively, and maximum biogas production of 99.4
ml/day with a desirability of 0.991. The results
obtained at 10 days of retention time show higher BOD
removal, COD removal and biogas production [2].
CONCLUSION
In the present study, anaerobic digestion methodology has been employed for reduction of COD and
biogas production under optimal condition. The RSM
based CCD was shown to be useful for the design of
experiments to investigate the effect of the four
estimated parameters (pH, initial BOD, temperature
and retention time) on the response parameters
(COD and BOD removal and biogas production). The
results showed good agreement between experimental and predicted values. Based on the ANOVA table,
the coefficient of determination (R2) values of 0.994
and 0.988 indicate the adequacy of the model for
COD removal and biogas production, respectively.
Maximum COD reduction and biogas production was
achieved at a pH value of 7, initial BOD of 1374 mg/L,
temperature of 32 °C and with the retention time of 10
days. It was identified from this study that COD reduction and biogas production in the treatment of syn-
38
V. SANGEETHA, V. SIVAKUMAR: BIOGAS PRODUCTION…
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V. SANGEETHA1
2
V. SIVAKUMAR
1
Department of Food Technology,
Kongu Engineering College,
Perundurai, Tamil Nadu, India
2
Department of Chemical
Engineering, Alagappa College of
Technology Campus, Anna
University, Chennai, India
NAUČNI RAD
PROIZVODNJA BIOGASA IZ VEŠTAČKIH
OTPADNIH VODA IZ PROIZVODNJE SKROBNOG
BRAŠNA PALME SAGO ANAEROBNOM
DIGESTIJOM: OPTIMIZACIJA I TRETMAN
Prerađivačka industrija skrobnog brašna palme sago stvara veliku količinu otpadnih voda
sa izuzetno visokom koncentracijom organskih zagađivača, što dovodi do zagađenja vode.
Za redukciju HPK i maksimalnu proizvodnju biogasa korišćena je anaerobna digestija
veštačkih otpadnih voda iz industrije skrobnog brašna šaržnim postupkom. Mešana kultura
dobijena iz mulja industrije skrobnog brašna je korišćeno kao izvor mikroorganizma.
Metodologija odzivne površine je korišćena za optimizaciju faktora procesa, kao što su:
pH, početna vrednost BPK, temperatura i vreme zadržavanja. Statistički rezultati su ocenjeni preko p vrednosti, odstupanja (F-test), koeficijenta determinacije R2 i adekvatne preciznosti. Pareto analiza varijansi je pokazala da koeficijenti determinacije (R2) za smanjenje HKP i BPK i proizvodnju biogasa iznose 0,994, 0,993 i 0,988, redom. Optimalni
uslovi pri kojima je postignuto maksimalno uklanjanje HPK (81,85%) i BOD uklanjanje
(91,61%) i proizvodnju biogasa (99,4 ml po danu) su: pH 7, početni BPK 1374 mg/l, vreme
zadržavanja 10 dana i temperatura 32 °C.
Ključne reči: anaerobna digestija, veštačka optadna voda iz proizvodnje skrobnog brašna, produkcija biogasa, hemijska potrošnja kiseonika, optimizacija.
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