Optimization of Na-Alginate Immobilization Method for Sulfide Oxidation Using Immobilized Thiobacillus Species

Optimization of Na-Alginate Immobilization Method for Sulfide Oxidation Using Immobilized Thiobacillus Species
JEWM
Optimization of Na-Alginate Immobilization Method for
Sulfide Oxidation Using Immobilized Thiobacillus Species
Ajit Kumar Surabhi1 and *Mohamed Ibrahim Elzagheid2
1,2Chemical and Process Engineering Technology Department, Jubail Industrial College, Jubail Industrial city, 31961,
Kingdom of Saudi Arabia
The study was aimed to investigate the optimization of Na-alginate immobilization method for
biological sulfide oxidation process using an immobilized Thiobacillus species and the effects of
four factors including Na-alginate concentration, CaCl2 concentration, agitation speed and
amount of inoculum on sulfide conversion. The strength of sodium-alginate immobilization
method for Thiobacillus species was evaluated. For this purpose, experiments were designed by
a central composite design (CCD) and results were optimized by using response surface
methodology (RSM). Design of experiments (DOE) was used to model and optimize the
operational conditions. The central composite design (CCD) was very good for the optimization
of variables; the R2
value for the developed model was 0.91. The results and analysis showed the
optimized values for the sulfide oxidation. 95% Sulfide oxidation was achieved with optimized
values. Beside this a useful by- product was produced from waste effluents.
Keywords: Cell Immobilization, Sodium alginate, Sulfide, Sulfur-oxidizing bacteria, Thiobacillus.
INTRODUCTION
Microbial Anaerobic reduction of sulfate or sulfite to sulfide
is known as a traditional process to remove sulfate for
several years (Tikhonova et al., 2006; Azabou et al., 2007).
As a result, much sulfide is discharged to natural water
bodies and much hydrogen sulfide gas (H2S) is leaked into
the atmosphere. Certain industrial wastewaters also
contain sulfide that has severe toxic effects on ecosystems
even at very low levels (Wiessner et al., 2005).
Wastewater effluents released by the various industries
like crude oil refineries, petrochemicals, pulp and paper,
tannery, and fertilizers also contains considerable
quantities of sulfur-contained emissions. These are
affecting environment severely, causing global warming
and land, air, and water bodies’ pollution. H2S is a toxic
and corrosive gas. It causes several environmental
problems, and economical issues to processing industries
in the form of equipment corrosion (Vaiopoulou et al.,
2005; Li et al., 2009). Most of current commercial methods
of sulfide removal are physicochemical methods, namely
Amoco-cold based adsorption, Lurgi sulfreen, shell class
off-gas treating process. Even though physicochemical
sulfide removal methods were successful, but their high-
energy, chemical, and catalyst consumptions, high
operating costs, and huge capital investments made them
expensive to operate (Ahmad et al., 2017; Liang et al.,
2015; Eow, 2002; Pandey and Malhotra, 1999; Midha et
al., 2012). In order to overcome these difficulties, biological
based sulfide removal methods can be a better choice.
Due to their low temperature and low-pressure operational
conditions, biological sulfide oxidation methods have more
advantages over conventional sulfide removal methods
[Midha et al., 2012; Ramos et al., 2013; Zytoon et al., 2014;
Toth et al., 2015). Several microorganisms have the ability
to remove sulfur and sulfides from wastewater. Among
those are Acidithiobacillus and Thiobacillus species:
Acidithiobacillus thiooxidans (Aroca et al., 2007),
Thiobacillus novellus (Cha et al., 1999), Thiobacillus
thioparus, (Chung et al., 2000; Cox and Deshusses, 2002)
and Thiobacillus denitrificans (Ma et al., 2006) and for
sulfide oxidation Thiobacillus denitrificans, Thiobacillus
thioparus, Thiobacillus thiooxidans, and Thiobacillus
ferrooxidans (Ramirez et al., 2009; Cardoso et al., 2006;
Lee et al., 2006; Visser eta l., 1997).
*Corresponding Author: Mohamed Ibrahim Elzagheid,
Chemical and Process Engineering Technology
Department, Jubail Industrial College, Jubail Industrial city,
31961, Kingdom of Saudi Arabia. Email:
elzagheid_m@jic.edu.sa, elzagheid66@yahoo.com, Tel:
00966133425428. Co-Author Email: ajit_KS@jic.edu.sa,
Tel: 00966133425424
Journal of Environment and Waste Management
Vol. 5(2), pp. 275-282, November, 2018. © www.premierpublishers.org. ISSN: 0274-6999
Research Article

Surabhi and Elzagheid 276
Sulfide oxidation reaction pathway in Thiobacillus species
follows the main oxidation pathway of S2---->S0---->SO3
2--
--->SO4
2-. It is often not easy to control and separate these
reactions that carried out by microorganism (Midha et al.,
2012; Suzuki, 1974 and 1999). Due to this, it is essential
to stop formation of sulfates, by using an immobilization
method which may help avoiding formation of sulfates,
safeguards the cells from wash away, and prevents them
from contacting harmful compounds (Li et al., 2009). The
use of immobilized cells is increasing in food production,
medicine, biofuels, chemical industries, industrial
wastewater treatment, and in textiles (Midha et al., 2012;
Toth et al., 2015; Siripattanakul et al. 2008; Mirzaei et al.,
2014). Several natural and synthetic support materials are
available for immobilization such as agar, alginate,
agarose, kappa-carrageenan, and synthetic like polyvinyl
alcohol (Mirzaei et al., 2014). Immobilization techniques
like carrier bonding, adsorption, cross-linking, and
encapsulation are being used widely in industries and in
researches (Li et al., 2009; Toth et al., 2015; Mirzaei et al.,
2014; Krishnakumar et al., 2005).There were several
approaches to immobilized cell biological sulfide oxidation,
but such techniques have failed to explain the effects of
operational variable interactions, and difficult to provide
optimum operation variables (Habeeb et al., 2016). The
aim of this study is utilize central composite design (CCD)
and design of experiments (DoE) under response surface
methodology (RSM). Response surface methodology
(RSM) is a statistical approach and is a mathematical
technique that can explain the relation and interactive
effects between several independent variables and one or
more responses. RSM is a very effective tool for
optimization (Kalantari et al., 2017). The current study
focuses on elevation and optimization of process
conditions of sodium alginate immobilization method for
sulfide removal from waste effluent water stream
biologically using immobilized Thiobacillus species.
MATERIALS AND METHODS
Collection, isolation, and immobilization of the microbial
species were carried out as described by Pavichandra et
al (2007, 2008, and 2009) and Himabindu et al (2006).
Accordingly, sulfide oxidizing bacteria was isolated from
aerobic sludge, which was collected from wastewater
treatment plant. The sludge is then kept for activation by
mixing sulfide-oxidizing bacteria (SOB), Thiobacillus
enrichment media. After adapting the sludge to
Thiobacillus media for a period of one month the sludge
was used as a source for the isolation (SOB) of
Thiobacillus species. The Thiobacillus species cells were
immobilized in Na-Alginate by entrapment method, Figure
1. Bacterial cell pellet was weighed and 20 mg of cells
were added to sodium alginate solution. The 3% sodium
alginate solution was prepared by adding 3 gm of high
viscous sodium alginate in 100 ml of Thiobacillus species
growth media by continuous stirring till the sodium alginate
was completely mixed in solution.
Figure 1: Thiobacillus species immobilized in Na-
alginate beads
The beads were prepared with a peristaltic pump using a
pipe of 2 mm diameter in CaCl2 solution. The 3 % CaCl2
cross-linking solution was prepared by dissolving 6 gm of
CaCl2 in 150 ml of double distilled water. The beads were
allowed in cross-linking solution for 3 hours. Later beads
were transferred into 150 ml of maintenance medium
containing NH4Cl, 4 g/l; MgSO4.7H2O, 1 g/l; KH2PO4, 2 g/l;
and 10 ml of trace element solution for overnight. The whole
process was carried out in aseptic conditions to prevent any
possibility of contamination.
Experimental set-up: Experiments were conducted in
500 ml laboratory shake-flask reactor. Prior to the
experiment, the conical flasks were stocked with media,
and sterilized with other accessories at 121oC for 15
minutes. After cooling the media, the shake-flask reactor
was inoculated with the Thiobacillus organism culture. The
operation was carried out in batch wise, the temperature
was maintained in a shaking oven at 25oC, halogenation
was achieved by slow shaking and the aeration was
provided with an air pump.
Analytical methods: For sample analysis standard
methods, APHA Standard methods for the examination of
water and waste water 20th edition was used for the
analysis of pH, temperature, sulfide, sulfate in the liquid
media.
Experimental design and optimization: The optimum
levels of parameters of Na-alginate immobilization method
for the maximum oxidation of sulfide by Thiobacillus
species determined by means of RSM. The RSM consists
of a group of empirical techniques devoted to the
evaluation of relationships existing between a cluster of
controlled experimental factors and measured responses
according to one or more selected criteria. According to
this design, the total number of treatment combinations
was 2^k + 2*k + n0 where k is the number of independent
variables and n0 is the number of repetitions of the
experiments at the center point. Based on the best results
of one at a time approach, four critical parameters of c-
alginate immobilization method medium were selected and
evaluated for their interactive behaviors by using a

J. Environ. Waste Manag. 277
statistical approach. The levels of four variables viz. Na-
alginate concentration, 3% (x1); NaCl2 3% (x2); inoculum
size, 6% (x3); and agitation 150 rpm (x4) were selected
and each of the variables was coded at five levels –2, –1,
0, 1, and 2 by using Equation (1).
xi = Xi – X0/ΔX-----(1)
For statistical calculations, the variables Xi were coded as
xi according to the following transformation (Himabindu et
al., 2006). The range and levels of the variables in coded
units for RSM studies are given in Table 1.
Table 1: Range and levels of the variables used in RSM
and optimization studies
Where xi is the dimensionless coded value of the variable
Xi, X0 the value of the Xi at the center point, and ΔX the
step change. The following quadratic model 2 explained
the behavior of the system.
---- (2)
Where Y, is the predicted response, β0 the intercept term,
βi the linear effect, βii the squared effect, and βij is the
interaction effect. The full quadratic equation for four
factors is given by model 3.
Y = β0 + β1 x1 + β2 x2 + β4 x3 + β4 x4 + β11 x1*x1 +β12
x1*x2 + β13 x1*x3 + β14 x1*x 4 + β22 x2*x2 + β23 x2*x3 + β24
x2*x 4 + β33 x3*x3+ β34 x3*x4 + β44 x4*x4 ----- (3)
Several experimental designs have been considered for
studying such models and central composite design (CCD)
was selected. For this study, a full factorial central
composite design with eight star points and six replicates
at the central points were employed to fit the second order
polynomial model, and 32 experiments were required for
this procedure. STATISTICA by Stat Soft Inc. software
was used for regression and graphical analysis of the data
obtained. In order to search for the optimum combination
of major critical parameters of the sodium alginate
immobilization method for the sulfide oxidation,
experiments were performed according to the CCD
experimental plan shown in Table 2.
Table 2: Design of experiments by full factorial Central
Composite Design (CCD) for RSM Studies
Run
No.
x1 x2 x3 x4
Run
No.
x1 x2 x3 x4
1 -1 -1 -1 -1 16 1 1 1 1
2 1 -1 -1 -1 17 -2 0 0 0
3 -1 1 -1 -1 18 2 0 0 0
4 1 1 -1 -1 19 0 -2 0 0
5 -1 -1 1 -1 20 0 2 0 0
6 1 -1 1 -1 21 0 0 -2 0
7 -1 1 1 -1 22 0 0 2 0
8 1 1 1 -1 23 0 0 0 -2
9 -1 -1 -1 1 24 0 0 0 2
10 1 -1 -1 1 25 0 0 0 0
11 -1 1 -1 1 26 0 0 0 0
12 1 1 -1 1 27 0 0 0 0
13 -1 -1 1 1 28 0 0 0 0
14 -1 1 1 29 0 0 0 0
15 -1 1 1 1 30 0 0 0 0
The results of CCD experiments for studying the effect of
three independent variables are presented along with the
mean predicted and observed responses in Table 3. The
regression equations obtained after the ANOVA gave the
level of Thiobacillus species growth as a function of the
initial values of Na2S2O3.5H2O, 6 gm, CaCO3, 3 gm, NH4Cl,
0.4 gm, CaCl2, 0.5 gm, KH2PO4, 2 gm and (NH4)2SO4 3
gm. The application of RSM yielded the following
regression equation, which is empirical relationship
between Thiobacillus species growth (Y) and the test
variables in coded unit.
Table 3: Results of Design of Experiments by Central
Composite Design (CCD) for RSM Studies
Run
No.
Sulfide
oxidation
%
measured
Sulfide
oxidation
%
Predicted
Run
No.
Sulfide
oxidation
%
measured
Sulfide
oxidation
%
Predicted
1 92 92.54167 16 73 91.3333
2 86 85.875 17 86 72.79167
3 97 80.375 18 72 82.95833
4 86 79.83333 19 80 69.29167
5 85 69.20833 20 72 80.45833
6 80 74.66667 21 86 85.95833
7 82 74.16667 22 88 85.79167
8 77 76.875 23 92 80.79167
9 80 73.375 24 86 94.95833
10 72 80.83333 25 80 77.33333
11 68 82.33333 26 77 77.33333
12 70 87.04167 27 76 77.33333
13 91 76.16667 28 74 77.33333
14 88 86.875 29 79 77.33333
15 70 93.375 30 78 77.33333
ijijiiiii
Y x*x*x*
2
0   
Variables -2 -1 0 +1 +2 ∆x
Na-alginate
concentration, %, x1, w/v
1 2 3 4 5 1
CaCl2,%,x2, v/v 1 2 3 4 5 1
Inoculum size,%, x3, v/v 2 4 6 8 10 2
Agitation, rpm, x4 50 100 150 200 250 50

RESULTS AND DISCUSSIONS
Optimization of Na-alginate immobilization method for
sulfide oxidation:
The results of composite design experiments (CCD) matrix
for percentage (%) of observed and measured biological
sulfide conversion by sulfide oxidation bacteria (SOB)
thiobacillus species, and the evaluated model was
represented by the below given Equation 4. By applying
multiple regression analysis on the experimental data, the
experimental results of the CCD design were fitted with a
second order full polynomial equation. The empirical
relationship between sulfide oxidation (Y) and the four test
variables in coded units obtained by the application of
RSM is given by Equation 4.
Y = 77.33 -2.54* x1 -2.79* x2 -0.04* x3 -3.54* x4 +0.13*
x1x1 + 0.68* x1x2 + 0.81* x1x3 + 1.31* x1x4 -0.61* x2x2 -
2.06* x2x3 -3.06* x2x4 + 2.13* x3x3 +
4.31* x3x4 + 2.63* x4x4----- (4)
Where Y is sulfide oxidation in %, is response and x1, x2,
x3, and x4 are the coded values of the test variables, Na-
alginate concentration, 3% (x1); CaCl2, 3% (x2); inoculum
size, 6% (x3); and agitation, 150 rpm (x4). The coefficient
of determination value (R2) was calculated as 0.910028 for
sulfide oxidation (model summary, Table 4 and 5),
indicating that the statistical model can explain 91 % of
variability in the response. The R2 value is always between
0 and 1. The closer the R2 is to 1.0, the stronger is the
model and the better it predicts the response, and higher
degree of co-relation between observed and predicted
values. In this case, the value of the determination
coefficient (R2 = 0.910028) indicates that the model does
not explain only 9 % of the total variations. The adjusted
R2 value corrects the R2 value for the sample size and for
the number of terms in the model. The value of the
adjusted determination coefficient (Adj R2 = 0.826053) is
also high to advocate for a high significance of the model.
If there are many terms in the model and the sample size
is not very large, the adjusted R2 may be noticeably
smaller than the R2. Here in this case the adjusted R2 value
is 0.826053, which is lesser than the R2 value of 0.910028,
these values are in good agreement with R2 =0.875 value
reported by Olmez (2009). At the same time, a relatively
lower value of the coefficient of variation (CV = 13.63%)
indicates a better precision and reliability of the carried out
experiments.
Response surface methodology (RSM) approach was
applied for identifying the significant factor. The ANOVA
has conducted for the second order response surface
model and the results are given Table 4 and 5. The
Analysis of Variance (ANOVA) is statistical method used
to compare the means of two or more groups, The ANOVA
provides complete information on model accuracy and
significance, and identifies the most significant factors that
influence the process (Zhou et al., 2010). The significance
of each coefficient was determined by Student’s t-test and
p-values, which are listed in Table 4 and 5. The larger the
magnitude of the t-value and smaller the p-value, the more
significant is the corresponding coefficient. This implies
that there are significant linear effects of medium variables
on sulfide oxidation x1 (p<0.00134), x2 (p<0.00061) and
x4 (p<0.000064), the interactive effects of x2x3 (p <
0.020061) x2x4 (p < 0.020061) x3x4 (p < 0.020061) and
quadratic effects of x3 (p < 0.003057) and x4 (p <0.00057).
The model F-value of 10.83699, and values of prob > F
(<0.05) indicated that the model terms are significant.
Table 4: Model Summary and Analysis of Variance
(ANOVA) for the Quadratic Model
Source of
Variations
Sum of
Squares
Degree
of
freedom
Mean
Square F-Value
Prob-
ability
Regression1526.45 14 109.032110.83699 1.99E-05
Residual 150.9167 15 10.06111
Total 1677.367
R=0.953954, R2
= 0.910028, Adjusted R2
= 0.826053,
CV=13.63 %
Table 5: Model Coefficients Estimated by Multiple
Linear Regressions (Significance of Regression
Coefficients)
Model
Unstandardized
Coefficients
Standard-
ized
Coeffi-
cients
Compu-
ted
t-values p-values
B Std. ErrorBeta
(Cons-
tant) 77.33333 1.294933 59.71994 2.97E-19
x1 -2.54167 0.647467 -0.30403 -3.92556 0.001349*
x2 -2.79167 0.647467 -0.33393 -4.31168 0.000617*
x3 -0.04167 0.647467 -0.00498 -0.06435 0.949539
x4 -3.54167 0.647467 -0.42364 -5.47004 6.46E-05*
x1x1 0.135417 0.60565 0.017744 0.223589 0.826094
x1x2 0.6875 0.792981 0.067146 0.866981 0.399609
x1x3 0.8125 0.792981 0.079354 1.024614 0.321786
x1x4 1.3125 0.792981 0.128187 1.655146 0.118661
x2x2 -0.61458 0.60565 -0.08053 -1.01475 0.326314
x2x3 -2.0625 0.792981 -0.20144 -2.60094 0.020061
x2x4 -3.0625 0.792981 -0.2991 -3.86201 0.001536
x3x3 2.135417 0.60565 0.279811 3.525829 0.003057
x3x4 4.3125 0.792981 0.421187 5.438337 6.86E-05
x4x4 2.635417 0.60565 0.345328 4.351389 0.00057
The results of multiple linear regressions conducted for the
second order response model are given in Table 5. The
significance of each co-efficient that was determined by t
& p-values, also listed in Table 5. The larger the value of
the t-value & smaller the p-value are more significant. The
RSM method is an effective tool and stepwise procedure
in finding the optimum parameters. The regression model
developed can be represented in the form of contour plots.
The 2-D response surface and sulfide oxidation contour
plots demonstrate the effects of factors on sulfide

conversion efficiency are shown in Figures 2-7 at four
combinations. Each contour curve represents an infinite
number of combinations of two test variables with the other
two maintained at their respective 0 level. The counter
plots show the relationship between the sodium alginate
with the variation in sodium chloride concentration,
inoculum size and agitation speed variables settings used
for an optimum sulfide conversion. The darker red regions
of the counter plots indicate that higher sulfide
conversions, while the other color regions indicate that
lower sulfide conversions.
The contour plots described by the regression model were
drawn to illustrate the effects of the independent variables,
and combined effects of each independent variable upon
the response variable. Figures 2, 3 and 4 show the trend
of sulfide oxidation in % with the variation in sodium
alginate with the variation in sodium chloride
concentration, inoculum size and agitation speed in the
range of -2 to +2 in coded units while the remaining other
two respective variables were at their respective zero
levels. Figures 5 and 6 show the effect of calcium chloride
concentration on the sulfide oxidation, as the
concentration of CaCl2 increased the sulfide oxidation was
increased. These plots demonstrate that the sulfide
oxidation is dependent on linear effects of Na- Alginate (Li
et al., 2009; Kalantari et al., 2017). A similar trend was also
observed for CaCl2 and agitation combination which
shown in Figure 7.
Figure 2: Contour plot of sulfide oxidation (%): Figure 3: Contour plot of sulfide oxidation (%):
the effect of Na-alginate and CaCl2 on sulfide oxidation. the effect of Na-alginate and Inoculum size on sulfide
Other variables are held at zero level oxidation. Other variables are held at zero level
Figure 4: Contour plot of sulfide oxidation (%): the Figure 5: Contour plot of sulfide oxidation (%): the
effect of Na-alginate and agitation on sulfide oxidation. effect of CaCl2 and Inoculum size on sulfide oxidation. Other
Other variables are held at zero level variables are held at zero level

Figure 6: Contour plot of sulfide oxidation (%): the Figure 7: Contour plot of sulfide oxidation (%):
effect of CaCl2 and agitation on sulfide oxidation. the effect of Inoculum size and agitation on sulfide
Other variables are held at zero level oxidation. Other variables are held at zero level
CONCLUSIONS
This study shows that immobilized Thiobacillus species as
sulfur oxidizing bacteria (SOB) can oxidize the sulfide into
elemental sulfur. Optimization of sulfide oxidation
conditions was studied by using response surface method
(RSM) of optimization by central composite. Design of
experiments (DOE) was used to model and optimize the
operational conditions. The central composite design
(CCD) was very good for the optimization of variables; the
R2 value for the developed model was 0.91. The results
and analysis showed the optimized values for the sulfide
oxidation. 95% Sulfide oxidation was achieved with
optimized values. Beside this a useful by- product was
produced from waste effluents.
ACKNOWLEDGMENT
Authors of this article thank Jubail Industrial College
management team for encouragement and support.
CONFLICT OF INTEREST STATEMENT
The authors declare no potential conflicts of interest.
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Accepted 13 November 2018
Citation: Surabhi AK, Elzagheid MI (2018). Optimization
of Na-Alginate Immobilization Method for Sulfide Oxidation
Using Immobilized Thiobacillus Species. Journal of
Environment and Waste Management 5(2): 275-282.
Copyright: © 2018 Surabhi and Elzagheid. This is an
open-access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are cited.

Optimization of Na-Alginate Immobilization Method for Sulfide Oxidation Using Immobilized Thiobacillus Species

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