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Research Article

Received: 18 September 2012 Revised: 7 January 2013 Accepted article published: 31 January 2013 Published online in Wiley Online Library: 13 March 2013

(wileyonlinelibrary.com) DOI 10.1002/jctb.4042

Kinetic studies of ethanol fermentation using

Kluyveromyces sp. IIPE453Sachin Kumar,a,c, Pratibha Dheeran,a, Surendra P. Singh,b IndraM. Mishrac and Dilip K. Adhikaric

Abstract

BACKGROUND: To operate the fermentation process effectively and efficiently, the kinetic modeling of cell growth and ethanolfermentation is necessary to predict the results of industrial fermentations under the optimized conditions.

RESULTS: A kinetic study was conducted for glucose utilization for growth of the yeast strainKluyveromycessp. IIPE453 andethanol formation. Effect of temperature, pH and initial glucose concentration on cell growth and ethanol formation wasstudied. The data obtained experimentally were validated with existing kinetic models for product and/or substrate inhibition.

CONCLUSION: Of all the models, the Aiba model for substrate inhibition was found to be the best fit to the experimental dataforthe growth ofKluyveromyces sp.IIPE453, andthe Luong model for product inhibition wasfound to be the best fit for ethanolformation usingKluyveromyces sp. IIPE453.c 2013 Society of Chemical Industry

Keywords: ethanol fermentation; thermotolerant yeast; Kluyveromycessp.; growth kinetics; fermentation kinetics

NOTATIONKCM Maintenance coefficient (h

1)

Kd Specific death rate (h1)

KI Substrate inhibition constant for cell growth

(g L1)

KIP Substrate inhibition constant for ethanolproduction (g L1)

KP Ethanol inhibition constant for cell growth (g L1)

KP Ethanol inhibition constant for ethanol

production (g L1)

KS Saturation constant for cell growth (g L1)

KSP Saturation constant for ethanol production

(g L1)

m,n,j Empirical numbers

P Product concentration (g L1)

qp Volumetric ethanol productivity (g L1 h1)

qs Specific sugar consumption rate (g g1 h1)

qsp Specific productivity (g g1 h1)

rP Rate of ethanol formation (g L

1

h

1

)rS Rate of sugar consumption (g L1 h1)

rX Rate of cell formation (g L1 h1)

S Rate limiting substrate concentration (g L1)

Sj Variance of error of residues

So Initial substrate concentration (g L1)

wj Weight factor

X Cell concentration (g L1)

YP/S Yield coefficient for ethanol formation per unit

substrate consumed (g g1)

YX/S Yield coefficient for cells formation per unit

substrate consumed (g g1)

YP/S Experimental ethanol yield (g g1)

Greeksymbols

Specific growth rate (h1)

m Maximum specific growth rate (h1)

Specific ethanol production rate (h1)

m Maximum specific ethanol production rate (h1)

, Empirical numbers

j Mean standard deviation Error statistic

ij Difference between model and experimental

value of thejth variable in theith experimental

point

INTRODUCTIONOverexploitation of fossil fuels has been a matter of concern for

energy security andclimate change. Green energy sources such as

bioethanol offer numerous advantages, especially as a transport

fuel to improve the quality of urban air accompanied with the

Correspondence to: Dr. Sachin Kumar, Biotechnology Area, Indian Institute of

Petroleum,Dehradun- 248 005, India. E-mail: [email protected]

Present address: Sardar Swaran Singh National Institute of RenewableEnergy,

Kapurthala-144601, India

Present address: SRM Research Institute, SRM University, Kattankulathur,

Tamilnadu, India

a BiotechnologyArea, Indian Institute of Petroleum,Dehradun- 248 005, India

b Department of Chemical Engineering, Indian Institute of Technology Roorkee,

Roorkee-247 667, India

c Department of Paper Technology, Indian Institute of Technology Roorkee,

Saharanpur Campus- 247 001, India

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Kinetic studies of ethanol fermentation www.soci.org

reduction in the emission of greenhouse gases, nitrogen oxides

and hydrocarbons. Ethanol has been traditionally manufactured

through fermentation route, using sugary raw materials such as

molasses from sugarcane and sugar beet. Lignocellulosic biomass

is a favorable feed-stock for ethanol production based on the

life cycle analysis of the carbon neutral process.1 However,

fermentation of sugars produced from the saccharification

of lignocellulosic biomass such as glucose, xylose, mannose,

galactose, arabinoseand cellobiose, to ethanol has limitations dueto the well-known ethanologens such asSaccharomyces cerevisae

orZymomonasmobilis because of their metabolic inefficiency.2,3 A

thermotolerant yeast strain Kluyveromycessp. IIPE453 (MTCC 5314)

showed growth and fermentation efficiency on a wide range of

substrates such as glucose, xylose, mannose, galactose, arabinose,

sucrose and cellobiose for growth as well as fermentation to

ethanol at a moderately high temperature of 50C.4 The yeast

strain wasalso ableto convertsugarspresentin sugarcane bagasse

hydrolysate, sugarcane juice, molasses, mahua flower extract and

their mixtures, to ethanol efficiently.5

Thermophilic/thermotolerant microorganisms and ther-

mostable enzymes are of great scientific interest, principally

with regard to their potential industrial applications due to theirstability at high temperatures.6,7 Thermophiles have distinct

advantages over mesophiles for ethanol production in terms

of increased solubility of substrates, improved mass transfer

due to decreased viscosity, increased diffusion rates, high

bioconversion rates, ability to use a variety of inexpensive

biomass feed-stocks, low risk of contamination, and facilitated

product recovery.8 12

To operate the fermentation process effectively and efficiently,

kinetic modeling of cell growth and ethanol fermentation is

necessary to predict the results of industrial fermentations

under optimized conditions.6,13 Kinetic modeling of ethanol

fermentation is important to understand metabolic processes, to

estimateprocessparametersandtheirinfluenceoncellgrowthand

product utilization, in the scale-up of the process and the controlof bioreactors.1417 In biological processes, such parameters

as temperature, pH, osmotic pressure, substrate and product

concentrations play key and important roles.18 Monods equation

is themost commonmodel, but is applicablefor fermentationwith

no inhibition.17This modelis also not valid for the initial adaptation

phrase just after inoculation of the substrate. An appropriate

ethanol fermentation model should account for the four kinetic

factors,namely14 substratelimitation,substrateinhibition, ethanol

inhibition and cell death. Phisalaphonget al.13 considered all four

kinetics parameters for ethanol fermentation by the flocculating

yeast, Saccharomycescerevisiae M30 and investigatedthe effect of

temperature on these parameters.

In the present work, the growth of thermotolerant yeastKluyveromyces sp. IIPE453 (MTCC 5314) and fermentation of

glucose to ethanol at 50C are reported. The experimental data

are used for the determination of the kinetic parameters and the

existing mathematical models were tested with the experimental

data and the best fit models were reported.

MATERIALS AND METHODSGrowth Conditions

The growth ofKluyveromyces sp. IIPE453 was carried out in a

Bioflow-110 bioreactor (NewBrunswick,USA) (5 L workingvolume)

under aerobic conditions at a temperature of 50C and pH 5.0. 1

mol L1 phosphoric acid and 1 mol L1 NaOH were used as acid

and base, respectively, to maintain the pH. The dissolved oxygen

(DO) was controlled at 40% by agitation and an aeration rate of

1 vvm. The growth medium, salt medium (SM), contained (in g

L1) di-sodium hydrogen ortho phosphate, 0.15; potassium di-

hydrogen ortho phosphate, 0.15; ammonium sulphate, 2.0; yeast

extract,1.0. Themediumwassterilizedfor 20 min at121Cusingan

autoclave. To determine the effect of temperature and pH on the

growth ofKluyveromycessp. IIPE453, the temperature was varied

from 40 to 60C at constant pH 5.0 and pH was varied from 4.0 to6.0 at a constant temperature of 50C, respectively. The effect of

glucose concentration on thegrowth ofKluyveromyces sp. IIPE453

was observed by varying its concentration from 5 to 40 g L1 at a

temperature of 50C and pH 5.0.

Fermentation conditions

Ethanol fermentation was carried out in a Bioflow-110 bioreactor

(New Brunswick, USA) (2 L working volume) under anaerobic

conditions at a temperature of 50C and pH 5.0. The fermentation

medium was thegrowth medium, except an ammonium sulphate

concentration of 1.0 g L1, was prepared. The medium was

sterilized for 20 min at 121C using an autoclave. To determine

the effect of temperature and pH on ethanol fermentation, thetemperature was varied from 40 to 60C at constant pH 5.0 and

pH was varied from 4.0 to 6.0 at a constant temperature of 50C.

The effect of glucose concentration on ethanol fermentation was

observed by varying its concentration from 200 to 300 g L1

at a temperature of 50C and pH 5.0. All the experiments were

conducted in duplicate and average values are reported.

Analytical methods

Glucose was analyzed by HPLC using a high performance

carbohydrate column (Waters) at 30C with acetonitrile and water

mixture (75:25) as a mobile carrier at a flow rate of 1.4 mL min1

and detected by a Waters 2414 refractive index detector. Ethanol

was analyzed by gas chromatography using an Ashco Neon II gasanalyzer with a 2 m long and 1/8 diameter porapak-QS column

with a mesh range of 80/100. The sample was injected at an

inlet temperature of 220C, oven temperature of 150C and flame

ionization detector temperature of 250C using nitrogen gas as a

carrier.

KINETICSEthanol fermentation is governed by the specificity of the

microorganisms and the metabolic regulations, which are

dependent on the process parameters. Hence, the growth of

microbial cells and fermentation, the cell behavior towards the

substrate and the product, particularly on their concentration andthe role of cells in the overall productivity of the process, need

careful monitoring. A functional relationship between specific

growth rate () and the rate limiting substrate concentration (S) is

generally expressed by the Monod-type equation:

=mS

KS + S (1)

where mis themaximum specific growth rate, and KS is thevalue

of the rate limiting substrate concentration at which the specific

growth rate is half of its maximum value, generally referred to as

the saturation constant. In most processes, high concentrations

of substrate and/or product often lead to inhibitory effects.

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www.soci.org S Kumaret al.

Equation (1) does not incorporate inhibition due to substrate

or product or both. Therefore, the proposed kinetics can be

modified in terms of both substrate and product inhibition and

combined with the death rate and cell maintenance. The rate of

cellmass formation, ethanolformation andsubstrate consumption

are related to the cell concentration (X), ethanol concentration (P)

and substrate concentration (S) as follows:19

Cells : rX= dXdt = X KdX (2)

Ethanol :rP=dP

dt = X (3)

Substrate : rS = dS

dt =

1

Y

X/S

dX

dt

+

1

Y

P/S

dP

dt

+ KCMX (4)

whereKdis the specific death rate (h1);KCM is the maintenance

coefficient (h1);YX/S and YP/S are the yield coefficients for cell

mass and ethanol formation per unit substrate consumed (g

g1), respectively. The Monod equation is not valid during initial

adaptation phase following inoculation. This is basically becauseof the time needed for the establishment of glycolysis and other

pathways by the enzyme pool.

The rate of cell growth, substrate consumption and ethanol

formation for fermentation without any growth can then be

related as follows:

Cells : rX=dX

dt = KdX (5)

Substrate : rS = dS

dt =

1

Y

P/S

dP

dt

+ KCMX (6)

Ethanol :rP= dPdt = X (7)

=mS

KS + S (8)

where

=1

X

dP

dt = specific product formation rate

g g1 h1

Ethanol fermentation with sugary substrates generally

experiences inhibition of cell growth by the substrate (feed)

and ethanol (product). Several inhibition models have been

proposed by various researchers for either substrate or productor both. These are summarized in Table 1 Equations (T1) to

(T28). Most of the models have been developed for substrate

inhibition. The models are categorized into three types: (1)

substrate inhibition; (2) product inhibition; (3) both substrate and

product inhibition.

Various researchers have validated their models for substrate

and/or product inhibition with experimental data. The initial

parameters for growth and YX/S can be calculated from the

experimental data. Further, the parameters m,Kd,KCM and YX/Scan be calculated from Equations (1) to (4) using Newtons

method (Solver, Microsoft Excel 2003, Microsoft Corporation,

USA) by minimizing the sum of the squared deviations between

experimental and calculated data. The parameters m,KS,KIand

KPcan be calculated by using the best fit model from those given

in Table 1 odd numbered Equations (T1) to (T28) using Newtons

method.

The initial parameters for fermentation and YP/S can be

calculated from the experimental data. Further, the parameters

m,Kd,KCM and YP/S can be calculated from Equations (5) to (8)

using Newtons method. The parameters m,KSP,KIPand KPcan

be calculated by using the best fit model from the models given

in Table 1 even numbered Equations (T1) to (T28) using Newtonsmethod.

The ethanol yield (YP/S) , (gg1), specific sugar consumption rate

(qs), (g g1 h1), volumetric productivity (qp), (g L

1 h1), and

specific productivity (qsp), (g g1 h1), can be calculated as:

YP/S =P

(So S)(9)

qs =So S

Total fermentation time Average dry cell weight (10)

qp =P

Total fermentation time

(11)

qsp =qp

Average dry cell weight (12)

where P is the ethanol concentration; So is the initial sugar

concentration andSis the residual sugar concentration.

Estimation of model parameters

Error minimization between the model predicted values and the

corresponding experimental data was carried out by usingvarious

error estimates including minimization of the weighted sum of

squares of residuals (SSWR), themean standard deviation (j), the

variance of error of residues (Sj), an error statistic (), and the root

mean square error (RMSE) as given by Khanet al.20 The weightedsum of squares of residuals is defined as:

SSWR =

ni=1

mj=1

2ij

w2j(13)

wheren and m are the number of experimental data points and

the number of process variables, respectively; wj is the weight

factor, which is the maximum value of the variables; and ij is

the difference between model and experimental value of the jth

variable in theith experimental point.

The mean standard deviation of the variable (j) is calculated

as follows:

j=1

n

ni=1

ij j= 1, m (14)

The variance of error of residues (Sj) is estimated as:

Sj=1

n 1

ni=1

(ij j)2 j= 1, m (15)

The error statistic () is defined as:

=(n m) n

(n 1) m

m

j=1

2

j

Sj(16)

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Table 1. Kinetic models for microbial growth and fermentation with inhibition effect

S.No. Model Equation for cell growth Equation for fermentation

A. Substrate inhibition

1 Haldane30 (1965) = mS

K

S+S+ S

2

KI

(T1) = mS

K

SP+S+ S

2

KIP

(T2)

2 Andrews31 (1968) = mS

(S+KS)

1+ SKI

(T3) = m S(S+KSP)

1+ S

KIP

(T4)

3 Aibaet al.24 (1968) = mS

(S+KS)exp

S

KI

(T5) = m

S

(S+KSP)exp

S

KIP

(T6)

4 Yano and Koga32 (1969) = max

1+KS/S+

nJ=1

S/Kj

J (T7) = max1+KSP/S+

nJ=1

S/Kj

J (T8)

5 Orhon and Tunay33 (1979) = mS

KS+S+S.SIKI

(T9) = mS

KSP+S+S.SIPKIP

(T10)

6 Luong34 (1987) = mS

(S+KS)

1 S

KI

(T11) = m

S

(S+KSP)

1 S

KIP

(T12)

7 Han and Levenspiel35 (1988) = m

1 S

KI

nS

S+KS

1 S

KI

m (T13) = m

1 SKIP

nS

S+KSP

1 S

KIP

m (T14)

8 Sivakumaret al.36 (1994) = m

1 SKI

nS

S+KS

1 S

So

m (T15) = m 1 SKIPn SS+KSP1 SSo m (T16)B. Product inhibition

9 Aibaet al.24 (1968) = mS

(S+KS)exp (P.KP) (T17) = m

S

(S+KSP)exp

P.KP

(T18)

10 Levenspiel37 (1980) = m

1 P

KP

(T19) = m

1 P

KP

(T20)

11 Luong27 (1985) = m

1

PKP

(T21) = m

1

P

KP

(T22)

C. Substrate and product inhibition

12 Heuvel and Beeftink 38 (1988) = mS

KS+S+S2KI

. KPKP+P

(T23) = mS

KSP+S+S2KIP

.K

PK

P+P

(T24)

13 Goncalveset al.39 (1991) = m

1 SSn

1 PPm

(T25) = m

1 SSn

1 PPm

(T26)

14 Phisalaphonget al.13 (2006) = mS

KS+S+S2

KI

1 P

KP

(T27) = m

S

KSP+S+S2

KIP

1 P

KP

(T28)

S, So andP are substrate, initial substrate and product concentrations, respectively; m and m are the maximum specific growth rate (h1) and

maximum specific ethanol production rate (h1), respectively;KS,KIand KPare the saturation, substrate inhibition and ethanol inhibition constants,for cell growth (g L1), respectively;KSP,KIPand KPare the saturation, substrate inhibition and ethanol inhibition constants, for ethanol production(g L1), respectively; , ,m,nandjare empirical numbers.

The error statistic () can be calculated using Equations (13) and

(16). The error statistic has the Fm,n-m distribution and is used to

find the statistical adequacy for acceptance of the model. If is

less than Fm,n-m value obtained from the F-table, the method is

acceptable for representing the data for a particular percentageconfidence level.

The root mean square error (RMSE), commonly used to

check the validity of the model for a single variable, can be

determined as:

RMSE=

1n

ni=1

(Observedvalues Predictedvalues)2 (17)

Values of SSWR, Sj and RMSE close to zero provide better

estimates of the model parameters. The variance of error of

residues (Sj), and error statistic () take into account the mean

standard deviation (j) in their estimates.

RESULTS AND DISCUSSIONGrowth ofKluyveromyces sp. IIPE453

The growth of the yeast Kluyveromyces sp. IIPE453 was studied

on glucose. Figure 1 shows the residual glucose concentration

(S) during the growth of the yeast at 50C with different initialconcentrations of glucose (10 g L1 So 40 g L

1). The glucose

could be utilized for both growth of the cells as well as ethanol

formation under aerobic conditions (Fig. 1). The cell mass yield,

YX/S, on glucose was obtained as 0.2 g cells g1 glucose, whereas

the ethanol yield was found to be 0.35 g g1 glucose. The ethanol

yield,YP/S, was obtained as 0.35 g g1. The specific growth rate

ofKluyveromycessp. IIPE453 was found to be 0.23 h1 on glucose

at a temperature of 50C and pH 5.0. Banat et al.21 reported a

specific growth rate ofKluyveromyces marxianusIMB3 as 0.63 h1

on glucose in batch fermentation at 50C.

Figure 2 shows the variation of residual glucose concentration

with its initial value So at different times during the growth



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Figure 1.Time-course of glucose concentration (S) ; dry cell weight(X) ; and ethanol concentration (P) during the growth of Kluyveromycessp. IIPE453 at different initial glucose concentrations (So) atT = 50oC; pH = 5.0 [So(g L

1): 10; 20; 40].

Figure 2. Variation of residual glucose concentration (S) ; dry cellweight (X) and ethanol formation (P) [ ] with initial glucoseconcentrations (So) with time (t) as a parameter during the growth of

Kluyveromycessp. IIPE453 atT= 50oC; pH = 5.0 [t (h): 0; 4; 8; 12;16].

of Kluyveromyces sp. IIPE453. Although an increase in Sohastens glucose consumption at any time. The residual glucose

concentration was higher at higherSo at fermentation time 12 or

16 h. Figure 2 shows the variation of cell mass concentration (X)

withSo and time as the parameters. It is found that Xincreases

fromSo =5 g L1 to So =10 g L

1 and then decreases as So is

further increased. The peak ofXat So = 10 g L1 shows that cell

growth is maximum atSo = 10 g L1 for time up to 16 h (Fig. 2).

The influence ofSoon ethanol concentration is shown in Fig. 2. It

is seen that the increase in ethanol concentration withSo is low

initially. As time progresses, the increase in ethanol concentrationbecomes more and more pronounced. At t =16 h, the ethanol

concentration increases from P= 0.6 g L1 atSo = 5 g L1 toP

= 6.5 g L1 atSo = 40 g L1. This trend is in contrast with that

forXversusSo plots in Fig. 2. It can be hypothesized that with an

increase inSo(So>10 g L1), ethanol formation is at the expense

of cell growth.

The time-course of rate of glucose consumption (rs) and cell

mass formation (rx) is shown in Fig. 3. It is found that the rate of

glucose consumption is faster up to t= 12 h and, thereafter, the

increase in rate is slower. The rate of cell mass formation increases

faster up to 8 h and, thereafter, therateof increaseslows. For So =

10gL1, rxshows a decreasing trend beyond t=24h.For So =40 g

L1, rxbecomes almost constant after t= 16 h. Figure3 shows the

Figure3. Time-course of rate of glucose consumption (-rs) ; cell massformation (rx) and ethanol formation (rp) with initial glucose

concentration (So) as a parameter atT= 50oC; pH = 5.0 [So (g L

1): 10;20; 40].

Table 2. Effect of temperature on kinetic parameters forcell growthatSo = 20 g L

1; pH = 5.0

Temperature (C)

Parameters 40 45 50 55 60

m(h1) 0.25 0.32 0.34 0.23 0.12

KS(g L1) 14.2 15.0 16.8 18.5 21.2

Kd(h1) 0.02 0.028 0.005 0.039 0.045

KCM(h1) 0.011 0.011 0.011 0.013 0.012

YX/S 0.45 0.56 0.52 0.38 0.29

YP/S 0.4 0.5 0.54 0.36 0.28

time-course of rate of ethanol formation (rp) at 50C at different

initial glucose concentrations. It is seen that the rate of ethanol

formation increases at a faster rate with an increase inSo. ForSo =40 g L1, the increase inrpbecomes much slower att> 8 h.

Evaluation of kinetic parameters for cell growth

Using the experimental growth data under aerobic conditions, the

Monod model was fitted to determine kinetic parameters and the

effect of temperature, pH and initial glucose concentration using

Equations (1)(4) as shown in Tables 2, 3 and 4, respectively. As

shown in Table 2, the maximum specific growth rate (m) for So =

20 g L1 and pH= 5.0 increases with an increase in temperature

up to 50C and, thereafter, shows a sharp decline. Table 3 shows

the maximum growth rate at pH 5.0. Table 4 shows the maximum

growth rate at the glucose concentration of 10 g L1. The values

ofm atSo = 10 g L1 andSo = 20 g L1 are almost the same.However, So > 20 g L

1, m decreased considerably. Similarly,

m is maximum at pH = 5.0 and decreases considerably when

pH is decreased or increased. Wang et al.22 estimated the kinetic

parameters using the Monod model on glucose usingS. cerevisiae

strain CCTCC M201022 asm = 0.094 h1,YX/S = 0.22 g g

1,YP/S= 1.22 g g1 andKCM = 0.114 h

1.

Simulation of the growth ofKluyveromyces sp. IIPE453 cells

The Monod Equation (1) is valid for non-inhibitory growth of

microbial cells but is not valid for the initial adaption period,

as stated earlier. Figure 2 shows that the cell concentration (X)

decreases with an increase in So beyond 10 g L1. However,



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0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 5 10 15 20 25 30 35 40

S (g l-1)

0 5 10 15 20 25 30 35 40

S (g l-1)

(h-1)

m= 0.177 h-1

m= 0.13 h-1m= 0.15 h-1

m= 0.19 h-1m= 0.21 h-1

(a)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

(h-1)

(b)

KS= 30g l-1

KS= 25.2 g l-1

KS= 35 g l-1

KS= 20 g l-1

KS= 15 g l-1

Figure 4.Simulation of growth ofKluyveromycessp. IIPE453 atSo = 40 g L1; pH = 5.0;T= 50C using Monod equation for (a) different values ofmat

KS = 25.2 g L1 ; (b) different values ofKSat m = 0.177 h

1 ( model curve; experimental data).

Table 3. Effect of pH on kinetic parameters for cell growth atSo =20 g L1;T= 50C

pH

Parameters 4.0 4.5 5.0 5.5 6.0

m(h1) 0.18 0.26 0.34 0.31 0.24

KS(g L1) 2.5 2.2 16.8 2.9 6.2

Kd(h1) 0.035 0.038 0.036 0.042 0.46

KCM(h1) 0.012 0.012 0.011 0.015 0.014

YX/S 0.42 0.5 0.52 0.35 0.23

YP/S 0.48 0.5 0.54 0.39 0.31

Table 4. Effect of initial glucose concentration (So) on kineticparameters for cell growth at T= 50C; pH = 5.0

So(g L1

)

Parameters 5 10 20 40

m(h1) 0.27 0.35 0.34 0.18

KS(g L1) 1.7 5.5 16.8 25.2

Kd(h1) 0.036 0.038 0.036 0.036

KCM(h1) 0.011 0.011 0.011 0.017

YX/S 0.52 0.54 0.52 0.48

YP/S 0.44 0.56 0.54 0.47

the experimental cell growth data are found to be correlated

satisfactorily by the Monod equation for 10 g L1 < So< 40 g

L

1

. The Monod model fits with theexperimental data varying mand KS are shown in Fig. 4. Table 5 shows the sensitivity of the

model parameters m andKS and error estimates for the model

fit with the experimental data. RMSE is found to be lowest at

m = 0.177 h1 and Ks = 25.2 g L

1. The Monod model does

not represent the transient data during the initial adaptation

phase, therefore, as So increases, higher values of KS are used

to fit the data. Although, Ren et al.23 used a pathway metabolic

approachto dealwith sucha situation,which makes KSmaintain its

value within a reasonable and meaningful range, this approach is

not used here.

Various models are available for representing the experimental

cell growth data under substrate inhibition conditions (Table 1).

Of all the models, the model of Aiba et al.,24 Equation (T5) was

Table 5. Sensitivity analysis of Monod model parameters and errorestimates for the experimental Kluyveromyces sp.IIPE453 growthdata

Model

equation withparameters

Parametricvalue Sj RMSE Figure

Equation (1) m= 0.177 h1

Ks = 15 g L1 0.00011 0.0020 0.0616 4(a)

Ks = 20 g L1 4.0x10-5 0.0007 0.0287

Ks = 25.2 g L1 9.0x10-6 0.0002 0.0092

Ks = 30 g L1 4.4x10-6 7.6x10-5 0.0214

Ks = 35 g L1 1.4x10-5 0.0002 0.0374

Equation (1)Ks= 25.2 g L1

m = 0.13 h1 6.5x10-5 0.0011 0.0567 4(b)

m = 0.15 h1 1.93x10-5 0.0003 0.0331

m = 0.177 h1 9.0x10-6 0.0002 0.0092

m = 0.19 h1 2.6x10-5 0.0004 0.0188

m = 0.21 h1 7.8x10-5 0.0014 0.0416

found to fit the experimental data satisfactorily and adequately.

Figure 5 shows the fit of the experimental data of versusS for

various values ofm,Ksand KI. Table 6 shows the error estimates

for fitting the model Equation (T5) to the experimental growth

data with variation in parametric values. It is found that themodel

of Aibaet al.24 has the best fit with the experimental versusS

data for parametric values m = 0.44 h1, Ks = 71.7 g L

1 and

KI= 99.3 g L1. Comparison of Table 5 with Table 6 shows that

the Monod equation gives a better fit to the experimental specific

growth datawith theerrorestimates lower thanthe corresponding

values for the model of Aibaet al.24 Figure 6 shows the simulation

of residual sugar concentration (S), cell concentration (X) andethanol concentration (P) with time forSo = 40 g L

1, pH = 5.0

at 50C temperature. It is seen that all the data fit well with the

model predicted curves. Thus, the growth rate data can be best

represented by either the Monod model equation:

=0.177 S

25.2 + S (18)

or the model of Aibaet al.:24

= 0.44 S

(S + 71.7)exp

S

99.3 (19)J Chem Technol Biotechnol2013; 88: 18741884 c 2013 Society of Chemical Industry wileyonlinelibrary.com/jctb


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m

= 0.44 h-1

m

= 0.36 h-1

m= 0.40 h-1

m

= 0.48 h-1

m= 0.52 h-1

KS= 80 g l-1

KS= 71.7 g l-1

KS= 90 g l-1

KS= 60 g l-1

KS= 50 g l -1

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

(h-1)

(a)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

(h-1)

(b)

0 5 10 15 20 25 30 35 40

S (g l-1)

Kl= 80 g l-1

Kl= 99.3g l-1

Kl= 120 g l-1

Kl=110 g l-1

Kl= 90 g l-1

0

0.02

0.04

0.06

0.08

0.1

0.12

(h-1)

(c)

0 5 10 15 20 25 30 35 40

S (g l-1)

0 5 10 15 20 25 30 35 40

S (g l-1)

Figure 5.Simulation of growth ofKluyveromycessp. IIPE453 at So = 40 g L1 ; pH = 5.0;T= 50C using the model of Aiba et al. for different values of

(a) m at KS = 71.7 g L1; KI= 99.3 g L

1 ; (b) KS at m = 0.44 h1 ; KI= 99.3 g L

1; (c)KIat m = 0.44 h1 ; KS = 71.7 g L

1 ( model curve;experimental data).

0

5

10

15

20

25

30

35

40

45

0 4 8 12 16 20 24 28 32 36

Time (h)

S(

g

l-1)

0

2

4

6

8

10

12

14

16

X,P(g

l-1)

Figure 6. Simulation of residual glucose concentration (S), dry cell mass(X) and ethanol concentration (P) using the model of Aiba etal. atSo = 40g L1; pH = 5.0;T= 50C; m = 0.44 h

1 ;KS = 71.7 g L1 ( model

curves; S-exp; X-exp; P-exp).

Krishnanet al.25 determined the kinetic parameters for growth

of recombinantSaccharomyces1400(pLNH33) asm = 0.662 h1,KS = 0.565 g L

1, KI= 283.7 g L1, KCM = 0.1 h

1 and YX/S= 0.115

on glucose, andm = 0.19 h1,KS = 3.4 g L

1,KI= 18.1 g L1,

KCM = 0.07 h1 andYX/S=0.162 on xylose.

Ethanol fermentation

Batch fermentation studies were conducted for So = 200 g L1,

250 g L1 and 300 g L1. The concentration of glucose to ethanol

at a So = 300 gL1 and temperatureof 50C was found tobe 57%.

The initial glucose concentrations (200 So300 g L1) did not

haveany effect on the maximumethanolconcentration which was

found to be about 86.5 g L1 (Fig. 7). The ethanol yield was 90%

of its theoretical yield at So = 300 gL1. The volumetric ethanol

Table 6. Sensitivity analysis of kinetic parameters for the model ofAibaet al.24 and error estimates for the experimentalKluyveromycessp. IIPE453 growth data

Model

equation with

parameters

Parametric

value Sj RMSE Figure

Equation (T5) m = 0.36 h1 0.00002 0.0002 0.0404 5(a)

KS = 71.7 g L1 m = 0.40 h

1 0.00001 0.0001 0.0222

KI= 99.3 g L1 m = 0.44 h

1 0.00001 0.0001 0.0101

m = 0.48 h1 0.00003 0.0003 0.0214

m = 0.52 h1 0.00008 0.0008 0.0395

Equation (T5) KS = 50 g L1 0.00015 0.0014 0.0643 5(b)

m = 0.44 h1 KS = 60 g L

1 0.00006 0.0005 0.0316

KI= 99.3 g L1 KS = 71.7 g L

1 0.00001 0.0001 0.0101

KS = 80 g L1 0.000004 0.00004 0.0199

KS = 90 g L1 0.00001 0.00013 0.0359

Equation (T5) KI= 80 g L1 0.00001 0.0001 0.0160 5(c)

m = 0.44 h1 KI= 90 g L

1 0.00001 0.0001 0.0115

KS = 71.7 g L1 KI= 99.3 g L

1 0.00001 0.0001 0.0101

KI= 110 g L1 0.00022 0.0001 0.0113

KI= 120 g L1 0.00002 0.0002 0.0137

productivity for So = 200 g L1, 250 g L1 and 300 g L1 were

found to be 2.28 g L1 h1, 2.17 g L1 h1 and 2.16 g L1 h1,

respectively, whereas the specific ethanol productivity for So =

200 g L1, 250 g L1 and 300 g L1 were found to be 0.63 g

g1 h1, 0.57 g g1 h1 and 0.48 g g1 h1, respectively. The

volumetric ethanol productivity for So = 200 g L1 was found

to be marginally higher than that for So = 250 g L1 or 300 g



8/11


Figure 7. Time-course of glucose concentration (S) and ethanolconcentration (P) during batch fermentation with initial glucoseconcentration (So) as a parameter atT= 50

C; pH = 5.0 [So(g L1): 200;

250; 300].

L1 whereas specific productivity for So = 200 g L1 was found

to be much higher than that for So = 250 g L1 or 300 g L1.

After 40 h of fermentation, with 200 g L1 So 300 g L1, the

ethanol concentration (P) was found to be the same, 86.8 g L1

.This shows high tolerance ofKluyveromyces sp. IIPE453 for the

substrate glucose and the product ethanol at 50C temperature

and pH 5.0. After 32 h of fermentation, the ethanol formation

diminished due to product inhibition as the ethanol concentration

in the broth was found to be more than 86 g L1 . Banat et al.26

reported a maximum ethanol concentration of 69.46 g L1 with

an ethanol yield of97.8% ofits theoretical yield and 1.67 g L1 h1

ethanol productivity when glucose wasused at a concentration of

So = 140 g L1 byKluyveromyces marxianusat 45C.

Figure 8 shows the variation of residual glucose concentration

with its initial value So at different times during the ethanol

fermentation. It wasfound thatglucose consumption wasconstant

forSo = 50 toSo = 200 g L1,4 whereas it decreased forSo = 250

and300 g L1 after 8 h.At t= 12 h, the glucose was exhausted forSo = 50 g L

1, whereas, the glucose was exhausted att= 16 h for

So = 100gL1. The glucose remained unutilized for So = 250gL

1

to So=300gL1 duetoincreaseintheethanolconcentrationinthe

broth.Figure8showsthevariationofethanolconcentration,Pwith

Sowith time as theparameter. It is seen that theincrease in ethanol

concentration withSo is initially slow. Pincreases fromSo = 50 g

L1 toSo = 100 g L1 and then decreases slightly asSo increases.

As time progresses,Pbecomes constant and att= 32 to 40 h, the

ethanol concentrationincreases veryslightly.This trend shows the

inhibition due to increase in ethanol concentration aftert= 32 h.

Figure 9 shows the time-course of rate of glucose consumption

(rs) and the rate of ethanol formation (rp), respectively, during

batch fermentation at 50C temperature and pH 5.0. It is seen that

the rate of glucose consumption and ethanol formation increase

with time, form a plateau and then decrease. Initially, the rates

decreasewith increasein So, but attheendof fermentationat 40h,

the rates become the same. As shown in Fig. 9, the rate of ethanol

formation starts decreasing after 32 h, showing the inhibition due

to high ethanol concentration in the broth.

Kinetic parameters for ethanol fermentation

Using the experimental data for ethanol fermentation, the kinetics

of fermentation was studied. The kinetic parameters for ethanol

fermentation were determined by fitting Equations (5)(8) to the

experimental data at different temperatures, pH andSo as shown

in Tables 7, 8 and 9, respectively.

Figure8. Variation of residual glucose concentration (S) and ethanolformation (P) with initial glucose concentrations (So) with time (t) asa parameter duringbatchfermentationof glucoseusing Kluyveromyces sp.IIPE453 at T= 50 oC;pH= 5.0 [t (h): 0; 4; 8; 12; 16; 20; 24;28; 32; 36;+ 40].

Figure 9. Time-course of rate of glucose consumption (-rs) andethanol formation (rp) during batch fermentation of glucose withinitial glucose concentration (So) as a parameter atT= 50

C; pH = 5.0 [So(g L1): 200; 250; 300].

Table 7. Effect of temperature on kinetic parameters for ethanolfermentation atSo = 200 g L

1; pH = 5.0

Temperature (C)

Parameters 40 45 50 55 60

m(h1) 0.8 0.98 1.08 0.69 0.42

KSP(g L1) 77 83 85 56 43

Kd(h1) 0.01 0.01 0.012 0.025 0.038

KCM(g L1) 0.024 0.025 0.027 0.029 0.034

YP/S 0.48 0.5 0.5 0.39 0.34

As shown in Table 7, the maximum specific ethanol formation

rate (m) was observed at a temperature of 50C whenSo = 200

g L1 and pH was kept at 5.0. The maximum specific ethanol

formation rate was also observed at pH 5.0 for So = 200 g L1

and the temperature was maintained at 50C (Table 8). The effect

ofSo on ethanol fermentation can be observed from Table 9. It is

seen thatSohas very little effect on mfor 200 g L1 So 300 g

L1. Apart from m,YP/Swas also found to be highest (YP/S = 0.5)

during fermentation at a temperature of 50C and pH = 5.0.YP/Swas found to be invariant with Soover 200 g L

1 So 300gL1.

Krishnanet al.25 determined the kinetic parameters as m = 2.0

h1,KS = 1.34 g l1,KCM = 0.1 h

1 andYP/S=0.47.



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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1(a)

0 50 100 150 200 250

m

= 0.85 h-1

m

= 0.89 h-1

m

= 0.93 h-1

K'P= 88.3 g l-1

K'P=86 g l-1

K'P= 90 g l-1

(h-1)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1(b)

(h-1)

S (g l-1)

= 7.1 = 8.5

= 6.5

0

0.1

0.2

0.3

0.40.5

0.6

0.7

0.8

0.9

1(c)

(h-1)

0 50 100 150 200 250

S (g l-1)

0 50 100 150 200 250

S (g l-1)

Figure 10. Simulation of specific rate of ethanol formation at So = 250 g L1;T= 50C; pH = 5.0 for different values of (a) matK

p = 88.3 g L

1; = 7.1;

(b)KPat m = 0.89 h1; = 7.1; (c) at m = 0.89 h

1;KP= 88.3 g L1 [ model curve; experimental data].

0

50

100

150

200

250

300

0 4 8 12 16 20 24 28 32 36 40

Time (h)

S(

g

l-1)

0

10

20

30

40

50

60

70

80

90

100

P(

g

l-1)

Figure 11. Simulation of residual glucose concentration (S) and ethanolconcentration (P) duringethanol fermentation using Luong model at So =

250 g L

1

;T= 50

C; pH = 5.0; m = 0.89 h

1

; K

p = 88.3 g L

1

; = 7.1[ mode l c urves; S-exp; P-exp].

Simulation of ethanol fermentation using Luong model27

The experimental data for the fermentation of glucose to ethanol

using Kluyveromycessp. IIPE453 at theinitial glucoseconcentration

(So) = 250 g L1 were tested for validity of the models listed in

Table 1. Of these models,the Luongmodel for productinhibition,27

Equation (T22) was found to be the best fit by Kluyveromyces

sp. IIPE453. Luong27 correlated his experimental data of ethanol

fermentation using glucose as a substrate by S. cerevisiae ATCC

4126 satisfactorily. The maximumallowable ethanol concentration

was predicted to be 112 g L1. The calculations are shown in

Table8. Effectof pH on kinetic parametersfor ethanol fermentationusing glucose atSo = 200 g L1;T= 50C

pH

Parameters 4.0 4.5 5.0 5.5 6.0

m(h1) 0.48 0.82 1.08 0.92 0.7

KSP(g L1) 60.2 46 85 76 85

Kd(h1) 0.025 0.02 0.012 0.018 0.03

KCM(g L1) 0.029 0.034 0.027 0.023 0.026

YP/S 0.35 0.39 0.5 0.488 0.5

Table 10 for various values of the model parameters along with

the error estimates for fitting the experimental specific ethanol

formation rate (). The simulated results and the experimental

data are presented in Fig. 10. It is seen that the Luong model fits

well with theexperimental data forthe fermentation of glucose to

ethanol usingKluyveromyces sp. IIPE453, which shows theproduct

inhibition. As shown in Table 10, all the error parameters, namely

Sj, and RMSE are lowest at m = 0.89 h1;KP= 88.3 g L

1 and

= 7.1. The values of simulated S and P are calculated using

Equations (6) and (7) and compared with the experimental data

as shown in Fig. 11. The simulated values ofS and Pare in good

agreement with the experimental data. Thus, the fermentation



10/11


Table 9. Effect of initial glucose concentration (So) on kineticparameters for ethanol fermentation using glucose at T = 50C;pH = 5.0

So(g L1)

Parameters 200 250 300

m(h1) 1.08 1.09 1.1

KSP(g L1) 85 88 84.9Kd(h

1) 0.008 0.012 0.02

KCM(g L1) 0.027 0.027 0.0275

YP/S 0.5 0.5 0.5

Table10. Effect of kinetic parameter of Luong27 model andthe errorestimates

Model

equation with

parameters

Parametric

value Sj RMSE Figure

Equation (T22) m = 0.85 h1 0.00495 0.0891 0.0756

10(a)KP= 88.3 g L1 m = 0.89 h1 0.00475 0.0856 0.0671

= 7.1 m = 0.93 h1 0.00492 0.0886 0.0745

Equation (T22) KP= 86 g L1 0.00857 0.1545 0.1015

10(b)m = 0.89 h1 KP= 88.3 g L

1 0.00475 0.0856 0.0671

= 7.1 KP= 90 g L1 0.00617 0.1110 0.0815

Equation (T22) = 6.5 0.00485 0.0872 0.0685

10(c)m = 0.89 h1 = 7.1 0.00475 0.0856 0.0671

KP= 88.3 g L1 = 8.5 0.00518 0.0933 0.0734

data can be best represented by the Luong model:

= 0.891 P88.3

7.1

(20)Arellano-Plazaet al.28 evaluated the Luong model to predict

product inhibition of the tequila fermentation process and the

calculated kinetic parameters for the Luong model as reported by

them wereKP= 130 g L1 and = 9. Krishnanet al.25 evaluated

fermentation kinetics of ethanol production from glucose by

recombinant Saccharomyces 1400(pLNH33) to predict product

inhibition and calculated the kinetic parameters of the Luong

model as m = 2.0 h1 , KP= 103 g L

1 and = 1.42. Ge and

Bai29 evaluatedthe kinetics of continuous ethanol production of a

flocculating fusant yeaststrain SPSC01 usingthe Luong model and

reportedthe valuesas m = 1.99 h1, KP= 125gL

1 and = 1.72.

CONCLUSIONSOptimum temperature, pH and initial glucose concentration were

found to be 50C, 5.0 and 10 g L1, respectively, for the growth

ofKluyveromyces sp. IIPE453. Optimum conditions for ethanol

fermentation using the yeast strain with glucose as the substrate

were found to be 50C temperature, pH 5.0 and initial glucose

concentration of 200 g L1. On validating experimental data with

existingkinetic models for product and/or substrateinhibition, the

Aiba model was found to best fit the experimental growth data of

Kluyveromyces sp. IIPE453. The Luong model for product inhibition

was found to best represent the ethanol fermentation data with

Kluyveromycessp. IIPE453.

ACKNOWLEDGEMENTSWe thank Dr MO Garg, Director IIP, Dehradun for valuable

suggestions and encouragement to carry out this research work.

One of the authors (Sachin Kumar) gratefully acknowledges a

Senior Research Fellowship awarded by the Council of Scientific

and Industrial Research (CSIR), India.

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