2011 International Conference on Alternative Energy in Developing Countries and Emerging Economies
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rpm in a thermostat controlled incubating shaker at
temperature of 35 °C for 72 h. Experiments were carried
out in triplicate for all the runs and the average values
were subjected to model analysis. Besides, as a statistical
measure, 6 experiments were conducted at the center
point to check for any error.
For confirmation of optimization conditions,
fermentations were done in 1 l fermentor (BIOFLO 3000,
New Brunswick Scientific, Edison, NJ, USA) with pH
monitoring. Fermentation parameters and FWH
components were carried out with the optimized
conditions. Fermentation studies were done with
Z.
mobilis
alone,
C. shehatae
alone and a mixed culture of
both. Time course experiment was also done.
Reproducibility of the process was checked in repeat runs
with the above conditions.
E. Experimental Design and Data Analysis
A central composite experimental design was used to
optimize the nitrogen source (X
1
), phosphorus source
(X
2
), yeast extract (X
3
) and inoculum size (X
4
) on ethanol
production from FWH. Ethanol production was used as
dependent output variables. 21 experiments were
performed in triplicate according to Table 2 to optimize
the parameters. A quadratic model
[26]
was used to
evaluate the optimization of environmental factors as
following equation (Eq.1):
Y
=
β
0
+
β
1
X
1
+
β
2
X
2
+
β
3
X
3
+
β
4
X
4
+
β
12
X
1
X
2
+
β
13
X
1
X
3
+
β
14
X
1
X
4
+
β
23
X
2
X
3
+
β
24
X
2
X
4
+
β
34
X
3
X
4
+
β
11
X
2
1
+
β
22
X
2
2
+
β
33
X
2
3
+
β
44
X
2
4
(Eq.1)
Where
Y
= predicted response; X
1
, X
2
,
X
3
and X
4
=
parameters;
E
0
= offset term;
E
1
,
E
2
,
E
3
and
E
4
= linear
coefficients;
E
11
,
E
22,
E
33
and
E
44
= squared coefficients;
and
E
12
,
E
23
,
E
13,
E
14
,
E
24
and
E
34
= interaction coefficients.
The response variable (Y) was fitted using a predictive
polynomial quadratic equation in order to correlate the
response variable to the independent variables
[27]
. The
Y values were
regressed with respect to nitrogen source,
phosphorus source, yeast extract and inoculum size.
Design expert software version 6.0 (Stat-Ease. Inc., MN,
USA) was used for regression and graphical analysis of
the experimental data obtained. The optimum levels of
the selected variables were obtained by solving the
regression equation and by analyzing the response surface
contour and surface plots. The quality of the fit of
quadratic model was expressed by the coefficient of
determination
R
2
, and its statical significance was
checked by the
t
-test in the same program.
F. Analytical Methods
Ten milliliters of fermentation broth was centrifuged at
5000 rpm for 30 min at 4ºC and the supernatant was used
to determine the ethanol, reducing sugars concentration
and soluble end products. Fermentation end products
(volatile fatty acids and ethanol), lactic acid, formic acid,
xylose, fructose, sucrose and glucose were analyzed with
a high performance liquid chromatograph (HPLC;
Agilent 1200 series), equipped with Aminex® HPX-87H
ion exclusion column
[28]
. Suspended solid (SS), volatile
suspended solid (VSS), oil concentration and pH were
determined in accordance with the Standard Methods
[29].
Chemical oxygen demand (COD), total nitrogen,
ammonium-nitrogen, total phosphorus and phosphate
concentration were analyzed using commercial test kits
from Spectroquant (Merck Ltd., Germany). The total
carbohydrate in FWH was analyzed using anthrone
method
[30].
The reducing sugars concentration in FWH
and fermentation broth was assayed by the Somogyi-
Nelson method
[31]
.
III. R
ESULT AND
D
ISCUSSION
Food waste is an important municipal waste and
mainly composed of carbohydrate. Hydrolysis of food
waste by microbial digestion (Look-Pang) generated
hydrolysates with high concentration of reducing sugar
(145 g/L). Food waste hydrolysate (FWH) characterized
abundance in nutrition (
Table I
). Ethanol production from
food waste was analyzed as low-cost feedstock using co-
culture of
Z. mobilis
and
C. shehatae
fermentation. In the
present paper, a central composite design (CCD) of
response surface methodology (RSM) has been used to
optimize conditions for transforming FWH to ethanol
using co-culture of
Z. mobilis
and
Candida shehatae
under non sterilized condition. While a similar study
proved that the most important chemical factors, which
affected the fermented production of ethanol, were the
nitrogen source ((NH
4
)
2
SO
4
), phosphorus source
(KH
2
PO
4
), yeast extract and inoculums size
[25]
. To
evaluate the potential of ethanol yield from FWH, 21
experiments were conducted according to the CCD
method.
Table II
shows the actual parameters and ethanol
production in CCD. The maximum ethanol yield was
0.158 g-ethanol/g-food waste (79.5 g/l) revealed 96.8%
of the theoretical yield (theoretical yield of ethanol was
calculated by the equations used by other researchers
[32]
)
at the center point. The results from this study
helped to frame a second order polynomial equation (Eq.
2) that relates the ethanol concentration, Y (g/l) to the
concentrations of (NH
4
)
2
SO
4
(g/l), KH
2
PO
4
(g/l), yeast
extract (g/l) and inoculums size (%).The regression
coefficients and significance levels are given in
Table III
.
This equation was used to predict the ethanol
concentration at optimum condition. Although the model
showed a satisfactory explanation (R
2
= 0.99), not all the
effects of factors and their interactions on ethanol
concentration were significant (P < 0.01). Thus, the
ethanol concentration was adequately explained by the
model equation (Eq.2).
Y
= -41.2
+
62.5X
1
+ 34.9X
2
+ 18.4X
3
+ 7.8X
4
-6.4X
1
X
2
+
4X
1
X
3
+
0.
7X
1
X
4
+ 4.7X
2
X
3
–
0.8X
2
X
4
-
0.2
X
3
X
4
–
27X
1
2
–
9.1X
2
2
–
12.9X
3
2
–
0.
3X
4
2
(Eq.2)
Each item in the regression model (Eq. 2) has an
identified effect on the ethanol concentration. Students
‘t’
-test can be used to quantify the intensity of parameters
on the ethanol concentration, while ‘P’ values signify the
pattern of interaction among the parameters. The larger
the value of ‘t’ and the smaller the value of ‘P’, the more
significant is the corresponding coefficient term
[33]
. The
regression coefficients and ‘t’ and ‘P’ values for all the
