Available on line at 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
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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
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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
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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].
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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. REFERENCES
Figure 4. Effect of temperature and retention time on biogas production at optimum pH and initial BOD.
[1]
R. Saravanane, D.V.S. Murthy, K. Krishnaiah, Water Air Soil Pollut. 127 (2001) 15–30
[2]
S. Savitha, S. Sadhasivam, K. Swaminathan, Feng Huei Lin, J. Cleaner Prod. 17 (2009) 1363–1372
[3]
X. Colin, J.L. Farinet, O. Rojas, D. Alazarda, Bioresour. Technol. 98 (2007) 1602–1607
[4]
K. Gurdal, S. Arslan, Environ. Model Assess. 14 (2009) 607–614
[5]
P.M. Ayyasamy, R. Banuregha, G. Vivekanandhan, S. Rajakumar, R. Yasodha, S. Lee, P. Lakshmanaperumalsamy, World J. Microbiol. Biotechnol. 24 (2008) 2677– –2684
[6]
M. Rajasimman, C. Karthikeyan, Degradation of starch industry effluent in an inverse fluidized bed reactor, In: th Proceedings of the 58 Annual meeting of the Indian Institute of Chemical Engineers, Mumbai, India, 2004, pp. 27-30
[7]
M. Rajasimman, C. Karthikeyan, Int. J. Environ. Res. 3 (2009) 569-574
[8]
R.C. Leitao, A.C. Van Haandel, G. Zeeman, G. Lettinga, Bioresour. Technol. 97 (2004) 1105–1118
[9]
H. Spanjers, J. B. van Lier, Water Sci. Technol. 53 (2006) 63–76
[10]
P. Khageshan, V.S. Govindan, Anaerobic filter for treatth ment of sago wastewater, In: Proceedings of 4 National Symposium on Environment, Chennai, India, 1995, pp. 248–252
[11]
B. Rajesh, J.S. Kaliappan, D. Beck, Int. J. Environ. Sci. Tech. 3 (2006) 69–77
[12]
P. Sujana, T.K. Ramanujam, Bioprocess Eng. 16 (1997) 163-168
[13]
R. Parthiban, P.V.R. Iyer, G. Sekaran, J. Environ. Sci. 19 (2007) 1416–1423
[14]
C.A. Sastry, R. Murahari, K. Saroja, Studies on the treatment of sago mill wastewater, In: Seminar on Environmental Pollution, Kerala, India, 1964
[15]
K. Saroja, C.A. Sastry, Report on treatment of Sago wastes, National Environmental Engineering Research Institute, India,1972
[16]
C. Tongkasane, Water Waste Treat. 13 (1970) 392–398
[17]
M.B. Pescod, N.C. Thanh, Water Sci. Technol. 9 (1977) 563–574
[18]
G. Lettinga, Water Sci. Technol. 52 (2005) 1–11
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… [25]
H.C. Yatmaz, A. Akyol, M. Bayramoglu, Ind. Eng. Chem. Res. 43 (2004) 6035–6039
[26]
M. Muthukumar, D. Sargunamania, N. Selvakumara, J. VenkataRao, Dyes Pigments 63 (2004) 127–134
R.H. Myers, D.C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Design Experiments, John Wiley and Sons, New York, 2002
[27]
K. Rajkumar, M. Muthukumar, Environ. Sci. Pollut. Res. 19 (2012) 148–160
V. Sangeetha, V. Sivakumar, A. Sudha, K.S. Priyenka Devi, Int. J. Eng. Technol. 6 (2014) 1053-1058
[28]
APHA, Standard Methods for the Examination of Water and Wastewater, American Public Health Association, New York, 2005
[29]
L. Appels, J. Baeyens, J. Degrève, R. Dewil, Prog. Energy Combust. Sci. 34 (2008) 755-781.
[19]
A. Danion, C. Bordes, J. Disdier, J. C. Gauvrit, C. Guillard, P. Lanteri, J. Photochem. Photobiol. A: Chemosphere 168 (2004) 161–167
[20]
B.K. Korbahti, N. Aktas, A. Tanyolac, J Hazard. Mater. 148 (2007) 83–90
[21] [22]
Chem. Ind. Chem. Eng. Q. 22 (1) 33−39 (2016)
[23]
R. Sridhar, V. Sivakumar, V. P. Immanuel, J.P. Maran, Environ. Prog. Sustainable Energy 31 (2011) 558-565
[24]
B. Subha, M. Muthukumar, Sci. World J. (2012), Article ID 239271, doi: 10.1100/2012/239271
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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