Statistical optimization of adsorption variables for biosorption of chromium (vi) using crude tamarind pod shell and activated carbon

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 07 | Jul-2014, Available @ http://www.ijret.org 441
STATISTICAL OPTIMIZATION OF ADSORPTION VARIABLES FOR
BIOSORPTION OF CHROMIUM (VI) USING CRUDE TAMARIND POD
SHELL AND ACTIVATED CARBON
Sudhanva.M.Desai1
, N.C.L.N Charyulu2
, Satyanarayana V Suggala3
1
Associate Professor, Department of Chemical Engineering, Dayananda Sagar College of Engineering, Bangalore,
India
2
Professor, Department of Chemical Engineering, C.B.I.T. Hyderabad, India.
3
Professor Department of Chemical Engineering, J.N.T.U.A.C.E Anantapuramu, India.
Abstract
Hexavalent Chromium is a major pollutant released during several industrial operations. In this study low-cost agro waste
biosorbent tamarind (Tamarindus indica) pod shells with partial and complete Pyrolysed forms were explored for the removal of
hexavalent chromium ions from aqueous solution. All the biosorption experiments were carried out in batch mode to optimize
process parameters using response surface methodology. Based on central composite design, quadratic model was developed to
correlate the variables to the response. Statistical analysis was carried out to identify the most influential factor for all the
adsorbents. Through analysis of variance (ANOVA) it was observed that pH is significant for crude and temperature is significant
for pyrolysed adsorbent. Among all the adsorbents tested, crude tamarind, removed a maximum of 96.09% of chromium with
biomass loading of 4.59g/ 100ml at the optimized conditions of initial concentration 71.7213 mg/l, pH 2.47, and temperature of
41.82
o
C.
Keywords: Biosorption, Tamarindus indica, Hexavalent chromium, Pyrolysis, ANOVA
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
In aqueous system chromium exists in two oxidation states
trivalent chromium [Cr (III)]. and hexavalent chromium [Cr
(VI)]. Both oxidation states of chromium have different
chemical, biological and environmental characteristics [1].
Cr (III) is relatively insoluble and required by
microorganisms in small quantities as an essential trace
metal nutrient [2], while Chromium [Cr (VI)]. is a great
concern because of its toxicity. Chromium (in this text
hereafter chromium refers to Cr (VI)) has been reported to
be a primary contaminant to humans, animals, plants and
microorganisms and it is known to be carcinogenic [3–5].
Chromium is used in a variety of industrial applications
hence, large quantities of chromium is discharged into the
water bodies. Water bodies and ground water are polluted
by chromium by the waste coming out of electroplating
fabrication, paints and pigments, mining, leather tanning,
etc. [6-8]. Due to environmental concern, discharge limits of
chromium have been closely monitored by most industrial
countries. Chromium concentration in industrial waste water
ranges from 0.5 to 270 mg/l [7]. The tolerance limit for
chromium for discharge into inland surface waters is 0.1
mg/l and in potable water is 0.05 mg/l [9, 10]. In order to
comply with this limit, it is essential that industries treat
their effluents to reduce the chromium concentration in
water and wastewater to acceptable levels before its
transport and cycling into the natural environment.
Appropriate technologies are applied to reduce the level of
chromium in final effluents. In wastewater treatment,
various methods utilized to remove chromium include
reduction followed by chemical precipitation [11], ion
exchange [12], electrochemical precipitation [13], reduction
[14], adsorption [15], solvent extraction [16], membrane
separation [17], concentration [18], evaporation and reverse
osmosis [19, 20].
Above all adsorption is by far most versatile and effective
method for removing any contaminants like heavy metal,
especially, if combined with appropriate regeneration steps.
This solves the problem of sludge disposal and renders the
system more economically viable, especially if low cost
adsorbents are used [21]. In the last few years, several
approaches have been reported in this direction utilizing
inexpensive and effective adsorbent for removal of
chromium from aqueous solutions. Many biosorbent were
tried for chromium removal as seen in literature [22-37].
The materials tried for this purpose range from industrial
wastes to agricultural waste products and biomass. Some
examples are hydrous concrete particles[22], paper mill
sludge [16], seaweed biosorbent [23], sugar beet pulp[24],
wheat bran [25], activated groundnut husk carbon[26],
coconut husk and palm pressed fibers [27], coconut shell,
wood and dust coal activated carbons [28], coconut tree
sawdust carbon [29], used tyres carbon [30], cactus, wool,
charcoal, and pine needles [31], rice husk carbon [32],
hazelnut shell carbon [33,34], almond shell carbon [35],
corncob [36]. and cow dung carbon[37].

_______________________________________________________________________________________
Agro waste such as tamarind pod shell as biosorbent is
promising because of low cost, abundance in availability in
India and reasonably high efficiency. It will not be used for
any other purpose other than as low grade fuel. In this study
tamarind pod shell, partially pyrolysed tamarind pod shell
and completely pyrolysed tamarind pod shell were used for
chromium removal.
Response Surface Methodology (RSM) is a collection of
statistical technique for design of experiments, model
building, evaluation of the effects of parameters and
optimizing the parameters for maximum removal efficiency.
It is widely used for multivariable studies in many processes
[38-40]. No work has been carried out with partial and
complete pyrolysis of tamarind pod shell. The objective of
present study is to quantify the biosorption of chromium by
various forms of tamarind pod shell and to select the best
out of them. Further, to optimize the process parameters for
maximum uptake of chromium using RSM.
2. MATERIALS AND METHODS
2.1 Preparation of Biosorbent
2.1.1 Collection of Tamarind Pod Shells
Natural agro waste biosorbent Tamarindus indica pod shells
collected from Kolar district, Karnataka were used for
removal of chromium. Natural biosorbent along with
various pyrolysis processes were employed for comparative
metal removal efficiency.
2.1.2 Crude (Untreated) Tamarind Pod Shells (T)
Tamarind pod shells were sun dried, powdered, sieved using
60/80 mesh BSS Standard sieve to get uniform sized
particles. The fraction that was retained on 80 mesh were
collected, washed thoroughly with distilled water and dried
in the hot air oven for 2 hours at 80 ºC.
2.1.3 Preparation of Activated Carbon by Pyrolysis
The activated carbons used in this study were prepared by
Complete Pyrolysis and Partial Pyrolysis using Crude
Tamarind pod shell in a muffle furnace. The complete
pyrolysis tamarind (TCP) adsorbent is obtained by just
keeping the crucible in the muffle furnace whereas partial
pyrolysed tamarind (TPP) is obtained by keeping the lid on
the crucible.
2.2 Preparation of Chromium Stock Solution
Synthetic chromium solution was prepared by dissolving
potassium dichromate (K2Cr2O7) in double distilled water.
1000 ppm of stock chromium solution was prepared by
dissolving 2.83 mg of potassium dichromate in one litre of
double distilled water. Other required concentrations were
prepared by diluting the stock solution. The pH of the
solution was adjusted to the required value.
2.2.1 Preparation of Diphenylcarbazide (DPC)
Solution
Diphenylcarbazide (DPC) solution was prepared by
dissolving 250mg of DPC in 50ml of acetone in a 100ml
volumetric flask.
2.3 Analysis of Chromium
0.25ml of phosphoric acid was added to 1ml of standard
sample containing known concentration of chromium, pH
was adjusted to 1.0±0.3 using 0.2N sulphuric acid. The
solution was mixed well and then diluted to 100ml in a
volumetric flask using double distilled water. Further 2ml of
DPC solution was added and mixed well. After full colour
development for 10min, 4ml of this solution was used in an
absorption cell and the concentrations were measured
spectrometrically at 540nm in UV-double beam
spectrophotometer [Shimadzu- UV Visible 1700]. The
calibration curve is prepared by measuring the absorbance
of different known concentrations of chromium solutions
and plotting a graph between concentrations versus
absorbance. A straight line is obtained with R2
of 0.994.
2.4 Initial Experiments
The initial experiments were conducted to fix limits of the
parameters to be varied. Maximum chromium removal is
observed with the range of initial metal ion concentrations
of 10 – 200 ppm, pH 1-7, temperature 30-50o
C and
biosorbent dosage of 1 - 10 g/100 ml. These ranges of
variables were employed for further study.
2.5 Design of Experiments using Central Composite
Design (CCD) [42]
The parameters initial metal ion concentration, pH,
temperature and biosorbent dosage were chosen as
independent variables and the removal efficiency of
chromium is output response. A 24
full factorial
experimental design, with seven replicates at the centre
point and thus a total of 31 experiments were employed in
this study using the statistical software, MINITAB 16 (PA,
USA). The centre point replicates were chosen to verify any
change in the estimation procedure as a measure of precision
property. Each independent variable had 5 levels which
were -2, –1, 0, +1 and +2. Therefore, a total of 31 different
combinations were chosen in random order according to a
CCD configuration for four factors.Table.1 shows the levels
of chosen variables used in the experiment for the removal
of chromium.
The analysis focused on how the removal efficiency is
influenced by independent variables, metal concentration
(X1), pH (X2), temperature (X3) and biosorbent dosage (X4).
The dependent output variable is maximum removal
efficiency (Y).

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2.6 Response Surface Methodology (RSM)
Response surface methodology is an empirical statistical
technique employed for multiple regression analysis by
using quantitative data obtained from properly designed
experiments to solve multivariate equations simultaneously.
The experiments with different metal ion concentration, pH,
temperature and adsorbent dosage were employed
simultaneously covering the spectrum of variables for the
removal of chromium in CCD.
Table 1: Central composite design for biosorption of
chromium
Independent
variable
Range and Level
-2 -1 0 +1 +2
(X1) 50 87.5 125 162.5 200
(X2) 1 2.5 4.0 5.5 7
(X3) 30 35 40 45 50
(X4) 1 3.25 5.5 7.75 10
Where
(X1) is Initial Chromium ion concentration (ppm)
(X2) is pH
(X3) is Temperature ( o
C)
(X4) is Biomass loading (g/100 ml)
The regression analysis was performed to estimate the
response function as a second order polynomial as shown in
equation 1
Y=β0+β1X1+β2X2+β3X3+β4X4+β11X1
2+β22X2
2+β33X
3
2+β44X4
2+β12X1X2+β13 X1X3+β14X1X4+β23X2
X3+β24X2X4+β34X3X4. ………(1)
X1, X2, X3, X4 are linear effects, X1
2
, X2
2
, X3
2
, X4
2
are
squared effects, X1X2, X1X3, X1X4, X2X3, X2X4, X3X4
are interaction effects and Y is the predicted response. β0 is
constant coefficient, β1,β2,β3,β4 are linear coefficients,
β11,β22,β33,β44 are squared coefficients, and
β12,β13,β14,β23,β24,β34 are interactive coefficients,
respectively.
A statistical software package Minitab 16, was used for
regression analysis of the data obtained and to estimate the
coefficient of the regression equation. The equations were
validated by the statistical test called ANOVA. The
significance of each term in the equation is to estimate the
goodness of fit in each case. Response surface were drawn
to determine the individual, square and interactive effects of
test variable on percentage removal of chromium.
2.7 Batch Experiments
Batch adsorption studies were performed by Shaking 100 ml
of different solutions in 250 ml conical flasks with cork lid
in constant temperature shaker at the conditioned mentioned
by central composite design to obtain the equilibrium data.
All experiments were performed in triplicate and the results
were averaged. After specified time interval, the samples
were analysed by the spectrophotometric method.
2.8 Optimization of Parameters
The second degree polynomial equation is solved and the
optimum values for the variables are obtained using
response optimizer in Minitab 16.
3. RESULTS AND DISCUSSIONS
3.1 Central Composite Design (CCD) Analysis
The results of experiments performed according to the CCD
design are given in Table 2. Table 2 gives the experimental
results along with the predicted values (section 3.2) for all
three adsorbents T, TCP and TPP.
It may be observed from Table 2 that the percentage
removal is higher at lower pH. In acidic pH, the biosorbent
surface may be protonated and hence the positively charged
biosorbent removes higher amounts of Chromium in the
anionic form HCrO4
-
. With the increase in the pH of the
system, the degree of protonation on the surface reduces
gradually and hence at higher pH, above 3.0, other
mechanism like physical adsorption on the surface of
sorbent could have taken an important role in sorbing
Chromium and exchange mechanism might have reduced.
Further, the effect of temperature is significant on pyrolysed
type of adsorbent suggests that the biosorption between
chromium and pyrolysed tamarind adsorbent involve the
combination of chemical interaction and physical
adsorption. With the increase in temperature the pores in the
adsorbent enlarges resulting in getting more surface area
available for diffusion and adsorption. [43]
3.2 Response Surface Methodology (RSM)
Regression analysis was done to fit the response function as
per the equation 1 and the results are reported as eq.2, eq.3
and eq. 4 for T, TCP, TPP, respectively
Y=92.9229-3.3183X1-13.3267X2+5.4933X3+5.6333X4-
1.1537X1
2-19.6637X2
2+0.70639X3
2-3.0737X4
2
+4.0000X1X2-0.8400X1X3-14.2250X2X3+7.5050X2X4
+3.0650X3X4 ……………. (2)
Y=75.68-0.84X1-4.7X2+11.9467X3+2.2983X4+1.0242X1
2
-
15.4858X2
2
-0.2308X3
2
+10.9492X4
2
-10.825X1X2-
10.19X1X3+4.3450X1X4+17.94X2X3-0.715X2X4
+7.4X3X4……(3)
Y=70.38-1.36X1-4.9883X2+11.7750X3+1.8783X4
+1.1200X1
2-14.5850X2
2-0.4850X3
2+10.8500X4
2-
10.3600X1X2-7.5600X1X3+5.5650X1X4
+16.78X2X3+0.2950X2X4 +8.9550X3X4..........(4)

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These above equations explain the effect of individual
variable (linear and squared) and interactive effects on
Chromium adsorption onto different adsorbent. The
chromium removal was predicted using eq.2 – eq.4 at
experimental parameters and the obtained values are also
reported in Table 2.
Multiple regression coefficient R2 is calculated from the
second degree polynomial equation (equation 2,3 and 4), is
R2= 0.8612 for T, 0.7995 for TCP and 0.7561 for TPP
indicates that the predicted values are closer to experimental
data as shown in Table 2. For a good statistical model, R2
value should be closer to 1, and a value of 0.75 indicates
aptness of the model. The R2
value of 0.8612 for T implies
that more than 86.12% of experimental data was compatible
with the model and only less than 13.88% of the variations
are not explained by the model [44].
The experimental results were analysed and student‗t‘ test
was conducted to find the significance of individual
parameters, squared parameters and interactive parameters
combination, smaller the probability value more significant
is the effect. The regression coefficients, ‗t‘ test results an d
probability values are reported in Table 3. The results show
that pH and square effect of pH is significant for crude
tamarind (probability value, p=0.000) whereas temperature
becomes significant for complete and partial pyrolysed
tamarind.
Table 2. Central Composite Design Matrix of design specifications along with observed response for chromium removal by T,
TCP & TPP
Run
Order
Conce
ntrati
on
pH Tem
pera
ture
Bioma
ss
loadin
g
% Chromium removal
T TCP TPP
Theoretic
al
Experime
ntal
Theoretic
al
Experiment
al
Theoretic
al
Experimenta
l
1 125 4 40 5.5 89.32 88.67 75.68 70.18 70.38 68.83
2 87.5 5.5 35 3.25 69.68 66.88 70.62 65.74 67.53 65.87
3 162.5 2.5 45 7.75 92.19 94.34 86.8 81.12 82.09 78.43
4 162.5 2.5 45 3.25 83.59 85.43 89.21 85.48 84.77 86.27
5 50 4 40 5.5 91.85 88.89 71.18 67.89 66.68 67.23
6 87.5 2.5 45 3.25 89.70 92.34 86.78 79.15 81.23 76.78
7 162.5 5.5 45 7.75 90.36 90.90 78.39 72.23 73.4 70.23
8 87.5 2.5 35 3.25 88.98 87.78 83.36 77.36 81.02 75.67
9 162.5 2.5 35 3.25 89.51 88.11 84.59 80.55 79.08 77.87
10 87.5 5.5 35 7.75 85.74 86.01 53.68 52.45 48.71 50.23
11 125 4 40 10 90.60 89.14 90.83 86.89 85.23 81.43
12 87.5 5.5 45 7.75 89.21 87.45 89.91 91.23 89.61 86.56
13 125 4 40 5.5 89.44 90.88 75.68 77.03 70.38 72.34
14 125 4 30 5.5 90.36 91.45 56.58 53.28 50.75 51.11
15 87.5 2.5 35 7.75 87.93 88.56 72.2 75.35 66.32 64.34
16 125 4 40 1 83.34 82.17 63.79 66.56 58.57 56.72
17 125 4 40 5.5 88.34 86.36 75.68 71.72 70.38 67.66
18 125 4 40 5.5 90.34 89.90 75.68 79.58 70.38 72.45
19 200 4 40 5.5 82.93 83.49 63.59 60.14 57.66 57.78
20 162.5 5.5 35 7.75 81.45 80.79 61.89 63.33 56.93 54.21
21 125 1 40 5.5 97.08 97.27 44.56 46.80 40.4 43.45
22 87.5 2.5 45 7.75 94.93 96.90 83.59 80.90 78.75 74.56
23 125 4 40 5.5 89.34 88.15 75.68 73.51 70.38 67.67
24 125 4 40 5.5 88.34 87.89 75.68 74.72 70.38 67.56
25 125 4 50 5.5 90.14 89.18 75.68 73.35 70.38 65.79
26 125 7 40 5.5 45.68 47.79 57.19 58.11 52.53 55.65
27 162.5 2.5 35 7.75 89.26 91.90 89.53 84.93 82.96 78.89
28 162.5 5.5 35 3.25 71.80 73.76 66.78 68.84 60.59 58.75
29 162.5 5.5 45 3.25 88.38 87.68 80.59 77.57 75.2 73.21
30 125 4 40 5.5 87.34 88.68 75.68 73.57 70.38 66.74
31 87.5 5.5 45 3.25 86.35 84.48 86.55 84.46 80.13 79.12

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3.3 Analysis of Variance (ANOVA)
The results of multiple linear regressions conducted for the
second order response surface model by ANOVA are given
in Table 4.The Fischer‘s variance ratio, F value = (Sr2
/Se2
),
is the ratio of mean square owing to regression to the mean
square owing to the error. The higher the F-value and lower
the probability P value (< 0.01) demonstrates significance
for the regression model. From Table 4 it is seen that linear
effect was significant in all cases and square effect is also
significant in the case of Crude tamarind
Table 3. Student ―t‖ test analysis for bio sorption of chromium by T, TCP & TPP
Term
Regression Coefficient t P
T TCP TPP T TCP TPP T TCP TPP
Constant 92.9229 75.6800 70.38 35.12 17.877 16.817 0.000 0.000 0.000
Concentration -3.3183 -0.8400 -1.36 -1.161 -0.184 -0.172 0.263 0.857 0.809
pH -13.3267 -4.7000 -4.988 -4.664 -1.028 -1.003 0.000 0.319 0.287
Temperature 5.4933 11.9467 11.775 1.922 2.613 2.243 0.073 0.019 0.012
Biomass loading 5.6333 2.2983 1.878 1.971 0.503 0.479 0.066 0.622 0.601
Concentration*Co
ncentration
-1.1537 1.0242 1.120 -0.220 0.122 0.106 0.828 0.904 0.839
pH*pH -19.6637 -15.4858 -14.585 -3.756 -1.848 -1.583 0.002 0.083 0.074
Temperature*Tem
perature
0.7063 -0.2308 -0.485 0.135 -0.028 -0.016 0.894 0.078 0.069
Biomass
loading*Biomass
loading
-3.0737 10.9492 10.850 -0.587 1.307 1.156 0.565 0.210 0.182
Concentration*pH 4.0000 -10.8250 10.360 0.571 -0.966 -0.856 0.576 0.348 0.313
Concentration*Te
mperature
-0.8400 -10.1900 -7.560 -0.120 -0.910 -0.789 0.906 0.376 0.324
Concentration*Bio
mass loading
-1.2800 4.3450 5.565 -0.183 0.388 0.318 0.857 0.703 0.678
pH*Temperature 14.2250 17.9400 16.78 2.032 1.602 1.497 0.059 0.129 0.111
pH*Biomass
loading
7.5050 -0.7150 0.295 1.072 -0.064 -0.057 0.300 0.950 0.889
Temperature*Bio
mass loading
3.0650 7.4000 8.955 0.438 0.661 0.564 0.667 0.518 0.473
Table 4. Analysis of Variance (ANOVA) for the selected quadratic model for the removal of Chromium by T, TCP & TPP
Source DF Sum of squares F Prob>F
T TCP TPP T TCP TPP T TCP TPP T TCP TPP
Regression 14 14 14 2498.73 2361.15 2359.62 3.64 1.34 1.32 0.008 0.283 0.276
Linear 4 4 4 1503.14 1024.80 1020.37 7.67 2.04 2.01 0.001 0.13 0.11
Square 4 4 4 709.18 719.33 715.56 3.62 1.43 1.38 0.028 0.268 0.259
Interaction 6 6 6 286.41 617.01 612.96 0.97 0.82 0.80 0.473 0.571 0.566
Residual
Error
16 16 16 783.91 2007.27 2006.18
Total 30 30 30 3282.64 4368.42 4364.44
3.4 Optimization of Response
The optimization of process variables are done to get
maximum % removal using response optimizer in Minitab
16. In this regard second degree polynomial equations (eq.2,
eq.3 and eq.4) were used to get the optimum values for the
variables. Table 5 gives the optimum values for all
adsorbents. Maximum removal of 96.09% chromium was
achieved with crude tamarind at initial concentration
(71.7213 mg/l), with least biomass (4.59g/ 100ml) and at
lower temperature (41.82
o
C) in acidic medium (pH 2.47).

_______________________________________________________________________________________
Table 5. Optimized parameters for T, TCP and TPP
Biosorbent Initial metal
ion
Concentration
(ppm)
pH Temperature
(oC)
Biomass
Load (g/l)
Predicted
%
Chromium
Removal
Experimental
%
Chromium
Removal
T 71.7213 2.47 41.82 4.59 96.86 96.09
TCP 79.9180 2.961 48.56 9.0246 91.08 90.36
TPP 95.0350 2.259 48.8364 9.8701 88.19 86.56
4. CONCLUSIONS
In the present work biosorption of chromium was studied in
batch experiments using three types of biosorbent derived
from tamarind fruit shell. Central composite design was
adopted to study of effect of variation in parameters like
initial chromium concentration, pH, temperature and
adsorbent dosage. For all the adsorbents, it is observed from
the student ―t‖ test that for non- pyrolysed type of
adsorbents, pH was significant and the temperature becomes
significant for pyrolysed samples. ANOVA analysis reveals
that for all the adsorbents linear effect was significant and
for crude tamarind square effect is also significant. Analysis
of variance also showed that a reasonably high regression
coefficient of 0.8612, ensuring a satisfactory adjustment of
the second order regression model with the experimental
data.
The optimization of process variables indicates that crude
tamarind removed maximum chromium of 96.09% with
initial metal concentration of 71.7213 mg/l, pH of 2.47,
temperature of 41.82 °C and lowest biomass loading of 4.59
g/100 ml. The study clearly demonstrating the use of crude
tamarind pod shell which is abundant and available at throw
away price may be used to treat a chemical waste containing
chromium.
5. NOMENCLATURE
T-Tamarind crude
TCP-Tamarind crude completely pyrolysed
TPP- Tamarind crude partially pyrolysed
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115
BIOGRAPHIES
Sudhanva M Desai. is a Associate
Professor at Department of Chemical
Engineering, DSCE Bangalore. He
obtained bachelor degree in Chemical
Engineering with III rank to the
Karnataka University Dharwad, India in 1988. Completed
M.Tech in General Chemical Engineering from Bangalore
University in 1998. Major research areas include
Biosorption of Heavy metals He has 18 years of teaching
and 6 years industrial experience. He has Two International
journal publications and 3 International conference
publications. He has guided many UG and PG projects few
were awarded at various competitions.
N.C.L.N.Charyulu, graduated from
Andhra Uniiversity in Chem.
Engg.with 3rd
rank in the year 1963.
M.Tech. from I.I.T. Kharagpur and
Ph.D. from I.I.Sc., Bangalore. Worked
for 33 years in KREC (Karnataka
Regional Engineering College), Surathkal, Mangalore,
Karnataka, INDIA and 10 years in CBIT Hyderabad, India.
Guided 4 Ph.Ds. Specialisation is Bioconversion,
biosynthesis, bioremediation and chemical reaction
engineering. Published 15 national and international
publications coauthored for two book
Dr. Suggala.V.Satyanarayana, is a
Professor at Department of Chemical
Engineering, JNTUA College of
Engineering, Anantapuramu. He
obtained his B.Tech degree from
Osmania University Hyderabad and
M. Tech & PhD from IIT Kanpur. He has 20 years of

_______________________________________________________________________________________
teaching experience at JNTUA College of Engineering
Anantapuramu and 2.5 years of Research Experience at
IICT, Hyderabad. He received the Best Teacher Award for
the year 2013 by the Government of Andhra Pradesh. Major
research areas include pervaporation, gas separation,
adsorption and multi-objective optimization Guided 4 PhDs
and published 53 research papers. Successfully completed 6
Consultancy & Sponsored Projects and co-authored one
book.
Dr. Suggala.V.Satyanarayana is the corrosponding author of
this paper. Email: svsatya7@gmail.com, 09849509167.

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