Mathematical modelling approach for concentration and productivity enhancement of 1,3-propanediol using Clostridium diolis

Biochemical Engineering Journal 68 (2012) 34–41

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Mathematical modelling approach for concentration and productivity enhancement of 1,3-propanediol using Clostridium diolis Guneet Kaur, Ashok K. Srivastava ∗ , Subhash Chand Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India

a r t i c l e

i n f o

Article history: Received 24 October 2011 Received in revised form 3 May 2012 Accepted 8 July 2012 Available online 16 July 2012 Keywords: 1,3-Propanediol Clostridium diolis Modelling Glycerol Fed-batch culture Kinetic parameters

a b s t r a c t 1,3-Propanediol (1,3-PD) is an organic compound of immense importance particularly for polycondensation reactions to synthesise polyesters, polyethers and polyurethanes. Batch cultivation of obligate anaerobe Clostridum diolis DSM15410 for 1,3-PD production using statistically optimised medium was attempted in the present investigation. A mathematical model for the description of batch 1,3-PD production kinetics was then proposed which also incorporated substrate/product inhibition data. The kinetic parameters of the model were identified by non-linear regression technique using batch experimental data. The developed model adequately described the experimental data to the extent of 99% accuracy as tested by statistical validity test. The batch model was then extrapolated to fed-batch cultivation primarily to identify fresh nutrient feeding strategies (off-line on the computer) for enhanced production of 1,3-PD. Experimental implementation of one such fed-batch fermentation strategy involving constant feed rate and subsequent comparison of the observed kinetics with model simulation further established the accuracy of the developed model. A 1,3-PD concentration of 61.2 g/L with a productivity of 1.02 g/L/h of 1,3-PD was obtained in this mathematical-model guided fed-batch fermentation which is the highest 1,3-PD concentration ever reported in literature using this strain. © 2012 Elsevier B.V. All rights reserved.

1. Introduction 1,3-Propanediol (1,3-PD) is a versatile degradable organic compound with various applications. It is particularly important as a monomer for the production of a new polyester called polytrimethylene terephthalate (PTT) which has many interesting properties. PTT is principally used in manufacture of carpet and textile fibres [1,2] but it also finds applications in engineering thermoplastics, films and coatings. 1,3-PD has myriad other uses, e.g. solvents, laminates, mouldings, antifreeze, medicines, lubricants, cosmetics, insect repellents and detergents [3]. The biotechnological route to this important metabolite involves microorganisms belonging to the genera Klebsiella, Clostridium, Enterobacter and Citrobacter, which can convert glycerol to 1,3-PD [4–8]. Huge amounts of waste glycerol are being generated by biodiesel plants throughout the globe which necessitate the search for methods for its disposal [9]. Thus green production of 1,3-PD offers several advantages. It entails cultivation at normal temperature and pressure, no use of expensive catalysts and no generation of toxic intermediates as compared to the conventional

∗ Corresponding author. Tel.: +91 11 26591010; fax: +91 11 26582282. E-mail addresses: [email protected] (G. Kaur), [email protected], [email protected] (A.K. Srivastava), [email protected] (S. Chand). 1369-703X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bej.2012.07.004

petrochemical-based 1,3-PD production [10]. Moreover it increases the economic sustainability of the biodiesel plant by valorizing its waste glycerol into a valuable product (1,3-PD). Bio-based production of 1,3-PD is limited by inhibition from both substrate and product which reduces the overall growth and product formation rates [11,12]. Inhibitory effect of glycerol can be mainly described with the respect to its action on the enzymes of 1,3-PD pathway particularly glycerol dehydratase (GDHt). Inactivation (of GDHt) by glycerol is a sort of mechanism-based inactivation and is accompanied by irreversible cleavage of the Co C bond of the enzyme bound coenzyme. Since the modified coenzyme remains tightly bound to apoenzyme, this results in activation of the enzyme. High glycerol contents modify all the kinetic parameters (growth and 1,3-PD production); it has also been suggested that this is probably due to a disturbance in the permeability of the cell membrane which limits the entry of nutrients, thereby decreasing the product yield [13]. 1,3-PD inhibition of fermentation is believed to be a general phenomenon of alcohol inhibition which might result from a modification of membrane organisation by an increase in fluidity of membrane or inhibition of membrane ATPase and other transport mechanisms by 1,3-PD [12]. Thus an in-depth understanding of the inhibition kinetics is imperative in order to design inhibition-free cultivations for achieving high 1,3-PD concentration yield and/or productivity. Biebl [14] investigated the inhibition potential of products of 1,3-PD fermentation on growth

G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

Nomenclature D dCi /dt F KS Sm Pm K1 K2 m P qp qs S S0 Sj SSWR V Wj X Ymax

dilution rate (h−1 ) rate of reaction for the component Ci (Ci = X, S, P) feed flow rate (L/h) saturation constant for substrate (g/L) critical substrate concentration (g/L) critical 1,3-PD concentration (g/L) growth associated contribution for 1,3-PD production (g/g) non-growth associated contribution for 1,3-PD production (g/g/h) maintenance energy constant (g/g/h) 1,3-PD concentration (g/L) specific 1,3-PD production rate (g/g/h) specific substrate (glycerol) consumption rate (g/g/h) substrate concentration (g/L) inlet substrate concentration (g/L) variance of error of a residual sum of squares of weighed residues volume (L) weight of a process variable biomass concentration (g/L) maximum yield (both biomass and 1,3-PD) with respect to substrate (g/g)

of Clostridium butyricum DSM 5431 using a pH-auxostat. It was reported that a concentration of 60 g/L 1,3-PD, 27 g/L acetic acid and 19 g/L butyric acid was sufficient to cause total growth inhibition at pH 6.5, with appreciable inhibition by glycerol above a concentration of 80 g/L. Similar results (as above) were also obtained by Zeng et al. [15] who proposed growth mathematical models for both C. butyricum and Klebsiella pneumoniae which described the inhibition effects of products and substrate of 1,3-PD fermentation. A difference in biochemical behaviour was however exhibited by a new isolate C. butyricum F2b which demonstrated significant cell growth and 1,3-PD production even at very high initial glycerol concentrations (90 g/L) along with high tolerance to 1,3-PD (above 80 g/L) [16]. Inhibition of microbial growth by high initial glycerol concentration clearly indicated that it would be desirable to implement slow feeding of the substrate in the reactor (fed-batch cultivation) to achieve high 1,3-PD concentration yield and/or productivity [17]. Fed-batch processing provides an ideal means of regulating the nutrient feed rate by ensuring adequate substrate availability and controlling its inhibition arising out of any over addition of substrate. Different types of feeding strategies like intermittent substrate addition [18,19], coupled feeding of substrate to alkali consumption [20] or microbial respiratory rate [17], pulse feeding under non-sterile conditions [21], etc. have been utilised by different researchers in the past in an attempt to improve production rates and overall production yields of 1,3-PD. Raw glycerol derived as a by-product from biodiesel plant has also been used for 1,3-PD production by fed-batch fermentation. In this case the possible inhibitory effect of impurities present in raw glycerol is an important consideration as the presence of salts (NaCl) and doubly bonded fatty acids such as oleic acid have been reported to inhibit microbial growth [22]. However successful maintenance of non-limiting and non-inhibitory substrate concentration in the fedbatch cultivations is particularly difficult due to the non availability of on-line sterilizable substrate (glycerol) sensors, thereby complicating the maintenance of the right substrate concentration in the bioreactor.

35

Mathematical modelling could therefore serve as a different, logical, yet simple, engineering approach in designing the fresh nutrient feeding strategies in order to obtain high 1,3-PD concentration and/or productivity. A mathematical model helps in better understanding of the system and facilitates in intelligently optimizing the process conditions for a particular fermentation process in minimum time without any trial and error. It is particularly useful for production of 1,3-PD as it can predict (off-line) a unique scenario of cultivation which is independent of substrate and/or product inhibition and yet ensure varying desirable availability of substrate and product concentrations by controlled feeding of fresh nutrients into the bioreactor. Several such feeding strategies can be simulated on the computer and the right one which features maximum 1,3-PD production from the bioreactor can be experimentally implemented. In the present investigation previously statistically optimised medium recipe was used to conduct the batch bioreactor cultivation to obtain growth and product formation kinetics data by obligate anaerobe Clostridium diolis. Using the batch kinetics and independently obtained substrate/product inhibition data, a mathematical model was thereafter proposed. The model parameters were obtained by minimizing the difference between the model simulation and original batch kinetics data obtained from the bioreactor. The developed batch model was extrapolated to fed-batch cultivation by taking the mass balance around the bioreactor and this model was then used to identify the nutrient feeding strategies for over production of 1,3-PD. 2. Materials and methods 2.1. Culture and maintenance C. diolis DSM15410 (previously C. butyricum DSM5431) procured from German Collection of Microorganisms (DSMZ, Germany) was used in the present study. It was maintained on Reinforced Clostridium Medium (Himedia, India) and stored at 4 ◦ C. 2.2. Growth medium and culture conditions The medium used in this study was statistically optimised in our laboratory using Plackett–Burman and Central Composite Design (Design Expert 5.0, software developed by StatEase Inc., MN, USA) [5]. It consisted (per litre) glycerol 54.15 g, K2 HPO4 3.21 g; KH2 PO4 2.75 g; (NH4 )2 SO4 2 g; MgSO4 ·7H2 O 0.2 g; CaCl2 ·2H2 O 0.02 g; FeSO4 ·7H2 O 5 mg; yeast extract 2 g; trace element solution (TES) 2 mL. The trace element solution per liter contained: 70 mg ZnCl2 ; 0.1 g MnCl2 ·4H2 O; 60 mg H3 BO3 ; 0.2 g CoCl2 ·2H2 O; 20 mg CuCl2 ·2H2 O; 25 mg NiCl2 ·6H2 O; 0.9 mL HCl (37%). Primary inoculum was grown in 120 mL serum bottles containing 50 mL nitrogen gassed sterile medium with 20 g/L glycerol. The medium pH was adjusted to 7.0 with 2 N NaOH/HCl. Inoculated bottles were incubated at 33 ◦ C and rotated at 150 rpm for 24 h in an orbital shaker. Specially designed 500 mL Erlenmeyer flasks (mouth size reduced to10 mm) were used for preparing secondary inoculum for the bioreactor [5]. Rest of the environmental conditions were same as described above for serum bottles. For substrate inhibition studies varying concentrations of substrate glycerol (10–100 g/L) were taken in modified 500 mL Erlenmeyer flasks containing 100 mL medium. Growth of C. diolis on different concentrations of glycerol was monitored by estimation of optical density (OD) at 650 nm and maximum specific growth rate (max ) was determined from the slope of the least square regression lines of the logarithm of OD vs time data in the exponential growth phase of the culture. Similarly experiments to study the effect of 1,3-PD on microbial growth were conducted by taking different initial concentrations

G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

C. diolis was cultivated in a 3.7 L bioreactor (Bioengineering AG, Switzerland) (working volume 1.5 L) for 1,3-PD production using statistically optimised medium. Bioreactor was inoculated with 10% (v/v) exponentially growing culture from the shake flasks. Nitrogen gas was sparged at 0.5 vvm in the bioreactor to maintain anaerobic conditions. pH was controlled at 7.0 by automatic addition of 2 N NaOH/HCl. Agitation was done by a flat blade turbine impeller at 150 rpm and temperature was maintained at 33 ◦ C by chilled water unit (Julabo, Germany). Biomass and metabolites (glycerol, 1,3-PD, butyric acid, acetic acid) were analysed at regular intervals of 3 h. 2.4. Development of batch mathematical model and optimization of model parameters The batch kinetics data thus obtained and independently obtained inhibition (both substrate and product) data were used for developing the mathematical model of 1,3-PD process. Model parameters were identified by minimizing the difference between experimental observations and model simulation by using the original algorithm of Rosenbrock [23] and other computer programs and methodology as described by Volesky and Votruba [24]. 2.5. Fed-batch cultivation of C. diolis with constant feed rate and constant substrate concentration Fed-batch cultivation was initiated as a batch with initial glycerol concentration of 54.45 g/L in the bioreactor. At 21 h when the culture was in the exponential growth phase and the unconverted substrate glycerol had fallen to 29.2 g/L, fresh glycerol feeding (along with other nutrients) was initiated with a constant feed rate of 30 mL/h and S0 = 180 g/L. This was continued till 40 h after which the batch cultivation was resumed again for next 20 h in order to allow utilization of residual glycerol. Samples were withdrawn at regular intervals of 3 h for biomass and metabolite estimation. 2.6. Analytical methods Biomass was measured turbidometrically at 650 nm (OD) and correlated with cell dry weight directly by a previously established correlation between OD and Biomass. Glycerol, 1,3-PD, butyric acid and acetic acid concentrations were determined by High Performance Liquid Chromatography (Agilent 1200 Series) using a Refractive-Index Detector (RI) and Bio-Rad Aminex HPX-87H column. A solution of 0.5 mM H2 SO4 was used as mobile phase at a flow rate of 0.5 mL/min. Analysis was carried out at 30 ◦ C temperature [5]. Carbon recovery was performed at the end of both batch and fed-batch fermentation and calculated as the percentage of consumed glycerol which appeared in the fermentation products. 3. Results and discussion 3.1. Batch cultivation of C. diolis in bioreactor

60 50

3 40

2.5 2

30

1.5

20

1 10

0.5 0

0 0

5

10

15

20

25

30

35

40

time (h) biomass model (g/L)

biomass experiment (g/L)

glycerol experiment (g/L)

1,3-PD experiment (g/L)

glycerol model (g/L)

1,3-PD model (g/L)

Fig. 1. Batch fermentation of 1,3-PD using C. diolis. Data points represent experimental values. Smooth curve illustrates model predictions.

36 h. Reasonably low concentrations of by-products of 1,3-PD fermentation namely butyric acid (2.3 g/L) and acetic acid (1.38 g/L) were also obtained towards the end of fermentation (Fig. 2). Batch fermentation resulted in a high 1,3-PD yield and productivity of 0.65 mol/mol and 0.72 g/L/h respectively. Carbon recovery of 85% (w/w) was obtained in batch cultivation. Table 1 summarises the results obtained in batch cultivation of different strains of Clostridium sp. in various literature reports [9,25–28].

3.2. Batch mathematical model development For development of batch mathematical model for 1,3-PD production, batch kinetic data (as obtained above) and both substrate and product inhibition data was utilised. Development of the present 1,3-PD batch model was based on the following assumptions:

1. Glycerol is the only limiting nutrient. 2. There is no process limitation by nitrogen, phosphorous and other growth factors (e.g. yeast extract) and these are in excessive quantities in the fermentation medium. 3. There is no change in pH and temperature throughout the course of cultivation.

2.5

2

1.5

1

0.5

0 0

Fig. 1 shows the time course of batch cultivation of C. diolis in the bioreactor. After an initial lag of 3 h the biomass concentration increased to 3.4 g/L at the end of fermentation. The culture featured an accumulation of 1,3-PD concentration of 25.8 g/L at the end of

glycerol (g/L), 1,3-PD (g/L)

2.3. Study of batch kinetics in the bioreactor

4 3.5

biomass (g/L)

of 1,3-PD (0–100 g/L) in modified 500 mL flasks which contained 20 g/L glycerol. Growth was monitored every 1 h and max was plotted against initial 1,3-PD concentration to ascertain the growth inhibitory effects of 1,3-PD.

butyric acid (g/l), acetic acid (g/L)

36

5

10

15

20

25

30

35

40

time (h) butyric acid (g/L)

acetic acid (g/L)

Fig. 2. Production of butyric acid and acetic acid during batch fermentation.

G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

37

Table 1 1,3-PD production by batch cultivation. Organism

Glycerol type

1,3-PD (g/L)

1,3-PD yield (mol/mol)

Q (g/L/h)a

Ref. no.

C. butyricum CNCM1211 C. butyricum CNCM1211 C. butyricum DSM5431 C. butyricum VPI3266

Raw glycerol Raw glycerol Pure glycerol Pure glycerol Raw glycerol Raw glycerol Raw glycerol Pure glycerol

63.4 65.4 29.5 7.35 8.26 7.93 47.1 25.8

0.69 0.66 0.56 0.58 0.51 0.56 0.64 0.65

1.85 1.67 2.3 0.31 0.34 0.33 1.12 0.72

[25] [26] [27] [28] [28] [28] [9] Present study

C. butyricum F2b C. diolis DSM15410 a

1,3-PD productivity.

The model consisted of a system of differential material balance equations which described the batch dynamics of 1,3-PD production. These equations are summarised below:  = max

dX = dt



S (S + KS )





max

 S a  



1−

S (S + KS )

1−

Sm



1−

 P b 

1−

Sm

 = max

1−

 P b  Pm

X

(2)

 S a

(3)

Sm

The inhibition model proposed by Andrews [30] was not considered as it did not correspond well with the experimental data of the present study. Andrews model described complete growth inhibition only at an infinite substrate concentration which was not obtained in this study. In addition the experiments on inhibitory effect of 1,3-PD on growth of C. diolis demonstrated a linear reduction in maximum specific growth rate with increasing initial 1,3-PD concentration (Fig. 4). max was found to become zero beyond an initial 1,3-PD concentration of 60 g/L. Thus inhibition of growth by the product (1,3-PD) itself was represented by a similar expression

 P b

(4)

Pm

The exponent ‘a’ and ‘b’ in Eq. (2) indicate the nature of relationship between max and S or P. Sm and Pm represent the critical concentrations of substrate and product at which the cell growth is completely inhibited. The values of model parameters max , Sm , a, b and Pm were determined by fitting the substrate and product inhibition data to Eqs. (1), (3) and (4) using a non-linear regression technique. A very high value of Sm (98.3) was obtained in this study. However the incorporation of this term in the model was necessary, despite it being low as compared to the substrate concentrations taken in the bioreactor, in order to account for substrate inhibition when the model would be extrapolated to nutrient feeding strategies such as fed-batch. The specific rate of substrate consumption was best mathematically described by Eq. (5): qs = −

 1 

dS =− dt

Y max

 1  Y max



+m

(5)

+m X

(6)

Here Ymax is a lumped parameter representing the maximum yield of both biomass and product based on substrate and ‘m’ represents the maintenance energy requirement of the cell. From the batch kinetics data, it was observed that 1,3-PD formation occurred during both growth and stationary phases of cultivation. Therefore 1,3-PD formation rate was represented by both growth and non-growth associated components as described below: qp = K1  + K2

(7)

0.7

0.9 0.8

0.6

0.7

0.5

0.6

μmax (h-1)

μ max (h-1)

1−

(1)

Eq. (2) represents the biomass formation rate as a function of limiting nutrient (glycerol) as given by Monod for substrate limitation. From the preliminary experiments on growth inhibition by increasing glycerol concentrations (Fig. 3) it was observed that the maximum specific growth rate (max ) started to decrease after a particular concentration of glycerol and thereafter became zero at a specific finite glycerol concentration. Since the inhibition phenomenon is not completely understood the empirical model given by Luong [29] for the inhibitory effect of ethanol on yeast growth was extended to growth inhibition of C. diolis by glycerol as given below:



  = max

Pm

 S a  

term [Eq. (4)] as the substrate assuming a common mechanism for both substrate and product [29]:

0.5 0.4 0.3

0.4 0.3 0.2

0.2

0.1

0.1

0

0 0

20

40

60

80

100

Initial glycerol concentration (g/L) Fig. 3. Effect of increasing initial glycerol concentration on max .

120

0

10

20

30

40

50

60

70

Initial 1,3-PD concentration (g/L) Fig. 4. Effect of increasing initial 1,3-PD concentration on max .

80

38

G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

where K1 and K2 are growth and non-growth associated product formation constants. dP = (K1  + K2 )X dt

(8)

Hence, Eqs. ((1), (5) and (7)) represent the model equations for batch glycerol to 1,3-PD bioconversion. 3.2.1. Evaluation of model parameters The optimal values of model parameters were obtained by nonlinear regression technique assisted by computer programs [24,31] which minimised the difference between experimental data and model prediction. An integration program based on Runge–Kutta method of 4th order was used to calculate the model predictions using the system of differential Eqs. ((1), (5) and (7)) which described the batch production kinetics of 1,3-PD. The optimization program for direct search of the minimum of a multivariable function was based on original method of Rosenbrock [23]. Minimum criterion used in the program was: SSWR =

n m 2ij i=1

j=1

1 n ij n i=1

where ‘n’ represents the total number of experimental data points and ij is the difference between the experimental value of a process variable and its corresponding model simulation. The estimation of variance of the error of a residual (Sj ) was then done by the following formula: Sj =

1 n 2 (ij − j ) n−1 i=1

j = 1, m

where ‘m’ is the number of variables. The value of statistics () which has Fm,n−m distribution was calculated as follows: (n − m)n m ij (n − 1)m j=1 Sj

2

=

In order to design fresh nutrient feeding strategies for improved concentration and/or productivity of 1,3-PD, the existing batch mathematical model was extrapolated to fed-batch by taking the mass balance around the bioreactor and appropriately adding the dilution terms. The fed-batch model equations are summarised below: dV =F dt D=

Its value was found to be 1.141 which was less than the ‘F3,10 ’ value (obtained from F-tables) for 99% confidence, for the whole set of experimental data. Thus on the basis of F-test the hypothesis of zero mean deviation between the experimental and model values could be accepted which further reinforced the validity of the proposed model structure.

(9)

F V

dX = dt



 max

S (S + KS )



1−

 S a   Sm

1−

 P b  Pm

X − DX (10)

dS =− dt

wj2

where SSWR represents the sum of the square of weighed residues. ‘i’ and ‘j’ represent the number of experimental data points and the number of variables respectively, Wj represents the weight of each variable (usually the maximum value of each variable; glycerol, 54.15; biomass, 3.4; 1,3-PD, 25.8) and ij denotes the difference between the model and experimental value (ymodel − yexpt ). The optimised values of the model parameters are listed in Table 2 which were used to simulate (on computer) the model equations with respect to biomass [Eq. (1)], substrate [Eq. (5)] and product [Eq. (7)]. Fig. 1 shows the comparison of model simulation and experimental data. Experimental values of the process variables are indicated by points whereas smooth curve illustrates the model simulation. As seen from Fig. 1 the developed batch model could successfully simulate the original experimental data. The degree of reliability of the developed model was further evaluated by a method given by Bard [32] which tested the hypothesis of a zero mean deviation between the model and experimental data. The mean residual of each variable ‘j ’ was calculated as follows: j =

3.3. Model application and validation- fed-batch fermentation design

 1  Y max

 + m X + D(S0 − S)

dP = [K1  + K2 ]X − DP dt

(11) (12)

where ‘D’ is the dilution rate, ‘F’ is the glycerol feed rate and ‘V’ is the volume of the bioreactor. A number of model simulations were done off-line (on computer) to identify a simple fed-batch cultivation strategy which would give maximum 1,3-PD production. This strategy was then experimentally implemented. It was observed that model-based fed-batch cultures provided a more logical approach for fresh nutrient feed into the bioreactor for increased production of desired metabolite whilst simultaneously reducing the time invested in (numerous non-optimal) conventional trial and error approach(es). 3.3.1. Fed-batch cultivation at constant feed rate and constant substrate concentration In an attempt to test the accuracy of the developed fed-batch model, fed-batch cultivation at constant feed rate and constant substrate concentration was simulated and then experimentally verified. Fed-batch experiments were simulated at a feed rate of 30 mL/h with an inlet glycerol concentration of 180 g/L. Cultures were first grown batchwise and feeding of glycerol (along with other nutrients) was started at 21 h of cultivation when the culture was in exponential phase and glycerol concentration in the bioreactor had dipped to 29.2 g/L. Feeding was continued till 40 h of cultivation. At 40 h, it was simulated as batch again until the residual glycerol was consumed. The model predicted a 1,3-PD concentration of 64.1 g/L. This fed-batch strategy was then experimentally implemented. A 1,3-PD concentration of 61.2 g/L was produced in the fermentation broth at the end of 60 h in fed-batch cultivation. It also featured accumulation of 14.2 g/L butyric acid and 8.6 g/L acetic acid as by-products of 1,3-PD fermentation along with high biomass production of 5.2 g/L (Fig. 5). Calculation of carbon recovery performed at the end of fed-batch fermentation was estimated at 93% (w/w). Model-based nutrient feeding strategy not only enhanced 1,3-PD accumulation but also improved its productivity (1.02 g/L/h) as compared to 0.72 g/L/h obtained in batch cultivation. Fig. 6 shows the comparison of observed data (points) with the model simulation (smooth curve) for fed-batch cultivation using the above model based nutrient feed strategy. As seen from the graph a good correlation was observed between the experimental data points and model predictions. This demonstrated that the model was adequately robust to describe the experimental

G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

39

Table 2 Model parameters of 1,3-PD fermentation in present study and literature reports. Parameter

Units

Experimental value (This study)a

−1

max KS Y1,3-PD/S YX /S ms K1 K2 Sm Pm a b

h g/L g/g g/g g/g/h g/g g/g/h g/L g/L Dimensionless Dimensionless

Optimised value (This study)a

0.42 – 0.50 0.07 – – – – – – –

References no.

0.65 12.8 0.502 0.067 0.23 7.3 0.15 98.3 65.2 1.12 1.0

[15]a

[38]b

[16]a

[33]a

[34]a

[21]a

0.67 0.005 – – – – – 181.7 66.4 1.0 1.0

0.67 0.025 – – – – – 185.1 64.62 – –

– – – 0.057 0.47 9.4 – – – – –

0.527 – – – – 17.14 – – – – –

– – – 0.029 – – – – – – –

– – 0.52 0.029 1.607 18.05 – – – – –

Values of Sm , Pm , a, b determined by independent inhibition experiments in the present study. Literature reports [Reference no.] report optimised parameter values. a Microorganism used – C. butyricum. b Microorganism used – Klebsiella pneumoniae.

butyric acid (g/L), acetic acid (g/L)

16 14 12 10 8 6 4 2 0 0

10

20

30

40

50

60

70

time (h) butyric acid (g/L)

acetic acid(g/L)

Fig. 5. Butyric acid and acetic acid production during fed-batch fermentation.

observations other than which were used for model parameter estimations and that the it could serve as a ‘guide’ for designing fresh nutrient feeding strategies for concentration and/or productivity enhancement of 1,3-PD. Mathematical models are usually developed to understand the process behaviour and facilitate process optimisation. In general

80 70

5

biomass (g/L)

60 4

50

Feeding on 3

40 30

2

20

Feeding off 1

glycerol (g/L), 1,3-PD (g/L)

6

10

0

0 0

10

20

30

40

50

60

70

time (h) biomass model (g/L)

biomass experiment (g/L)

glycerol experiment (g/L)

1,3-PD experiment (g/L)

glycerol model (g/L)

1,3-PD model (g/L)

Fig. 6. Mathematical model-based fed-batch fermentation of 1,3-PD. 0–20 h – batch, 20–40 h – fed-batch at constant feed rate of 30 mL/h and constant feed glycerol concentration of 180 g/L, 40–60 h – batch. Data points represent experimental values. Smooth curve illustrates model predictions.

these models are empirical in nature yet they adequately describe the process limitations and are sensitive to inhibitions caused by accumulated products. Invariably they serve as an invaluable tool for the process optimization with minimum experiments in bioprocess engineering. There have been few literature reports on modelling of 1,3-PD production. A Contois-type model was proposed by Papanikolaou and Aggelis [33] to mathematically describe the continuous cultivation kinetics of high 1,3-PD tolerant strain C. butyricum F2b. The developed model predicted high 1,3-PD productivity values (close to the ones reported in literature) upon cultivation of this isolate on raw glycerol and therefore demonstrated the feasibility of usage of the latter for 1,3-PD production. In yet another study an analytical model based on release of CO2 through the phosphoroclastic reaction was developed in order to quantify the effect of raw glycerol concentration on metabolite production by the same strain [34]. Table 2 indicates that the optimised model parameter values obtained in the present study are reasonably close to literature reported values. For example the value of Pm obtained in this study is close to its values reported by Biebl [14] and Zeng et al. [15] for the same C. butyricum strain as used in the present study. However 1,3-PD critical concentration values (Pm ) higher than this have been reported using ‘new isolates’ of C. butyricum which have been characterised as high product tolerant strains [9,35]. Experimental implementation of mathematical model-based constant feed rate fed-batch cultivation of C. diolis DSM15410 in the present investigation resulted in a 1,3-PD concentration of 61.2 g/L and a productivity of 1.02 g/L/h. It was also observed that the culture metabolism featured an accumulation of 0.67 mol 1,3-PD per mol of glycerol consumed (Fig. 7). Over all the culture performance was significantly better than the results obtained in the batch fermentation in the present study. To the best of our knowledge the 1,3-PD concentration and yield in the present investigation is the highest as compared to fed-batch cultivation results in the literature using the same strain (Table 3). Günzel et al. [18] and Abbad-Andaloussi et al. [19] applied intermittent substrate addition for fed-batch cultivation and achieved a 1,3-PD concentration of 56 g/L and 47.5 g/L respectively using C. butyricum DSM 5431. Thereafter Reimann and Biebl [20] reported coupled feeding of two inhibitory substrates glycerol and ammonium to alkali consumption for 1,3-PD production by fed-batch fermentation using C. butyricum DSM5431. A similar 1,3-PD concentration of 47.5 g/L as obtained by AbbadAndaloussi et al. [19] was achieved by the authors. However a considerable shortening of cultivation times was observed which could be attributed to better regulation of substrate addition in the latter work which facilitated in maintaining the substrate at a

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G. Kaur et al. / Biochemical Engineering Journal 68 (2012) 34–41

Table 3 Results of fed-batch 1,3-PD fermentation by different C. butyricum strains. C. butyricum strain

1,3-PDa

Yieldb

Q1,3-PD c

Glycerol type

Reference no.

DSM5431 DSM5431 DSM5431 VPI3266 IK 124 VPI1718 VPI1718 AKR 102a DSM 15410

56 47.5 47.5 65 87 70.8 67.9 76.2 61.2

0.66 0.61 0.62 0.69 0.65 0.66 0.67 0.62 0.67

2.3-2.9 0.66 2.4 1.21 1.9 0.70 0.78 2.3 1.02

Pure Pure Pure Pure Raw Raw Raw Raw Pure

[18] [19] [20] [17] [36] [37] [21] [35] Present study

a b c

1,3-PD concentration (g/L). 1,3-PD yield (mol/mol). 1,3-PD productivity (g/L/h).

non-limiting, non-inhibitory level. A fed-batch strategy combining a low base-driven glycerol addition with constant on-line glycerol measurement was followed by Hirschmann et al. [36] using a high substrate and product tolerant strain of C. butyricum IK124. It featured a 1,3-PD concentration of 80 g/L with an overall 1,3-PD productivity of 1.8 g/L/h upon growth on raw glycerol which was comparable to the results (87 g/L, 1.9 g/L/h) obtained using refined glycerol. In a recent report production of 1,3-PD from raw glycerol in 1 L and 200 L scale fermenters was attempted using another isolate C. butyricum AKR102a [35]. Cultivation on refined glycerol in 1 L scale fermenter yielded 93.7 g/L 1,3-PD which decreased to 76.2 g/L 1,3-PD when raw glycerol was used. A reasonably high 1,3-PD concentration of 61.5 g/L with a 1,3-PD productivity of 2.11 g/L/h was achieved in 200 L fermenter which appeared promising to further optimise the process for large scale 1,3-PD production. Recently a 1,3-PD production of 67.9 g/L was obtained by fed-batch cultivation of C. butyricum VPI1718 using pulse feeding of concentrated raw glycerol under non-sterile conditions [21]. Chatzifragkou et al. [37] used the same strain as above to investigate the impact of anaerobiosis strategy and reactor geometry on 1,3-PD production. Fed-batch cultivation of C. butyricum VPI1718 under continual sparging conditions produced 70.8 g/L 1,3-PD. This 1,3-PD concentration significantly decreased to 30.5 g/L when self-generated anaerobiosis strategy was used. The latter also exhibited characteristically high lactic acid production which was found to negatively affect both biomass and 1,3-PD production. A 1,3-PD concentration of 61.2 g/L obtained in the present work is the highest 1,3-PD concentration ever reported in the literature using the native strain of C. diolis DSM15410 by a model-based nutrient feed design approach. The present model could be further used to generate a nutrient feed profile with higher 1,3-PD productivity and maximum 1,3PD concentration. Development and testing of such newer feeding strategies shall be the topic of our future investigation.

4. Conclusions Batch kinetics of 1,3-PD by C. diolis using statistically optimised medium was established in a 3.7 L bioreactor. It featured a biomass and 1,3-PD concentration of 3.4 g/L and 25.8 g/L at the end of 36 h. A 1,3-PD productivity of 0.72 g/L/h was obtained in batch cultivation. A simple batch mathematical model was proposed for 1,3-PD production by C. diolis using the average batch kinetics data thus obtained and independently obtained substrate/product inhibition data. The model parameters were identified by minimizing the difference between model simulation and original experimental data. The proposed model could successfully simulate the batch experimental data. Applicability of the developed model for off-line simulation of different fed-batch operating conditions for improved 1,3-PD concentration and/or productivity was demonstrated. A simple fed-batch cultivation with constant feed rate (30 mL/h) and constant glycerol concentration (180 g/L) was identified by model simulations which predicted an enhanced 1,3-PD concentration of 64.1 g/L. The model predictions were compared with experimental fed-batch results to establish the accuracy of the model developed. The original experimental observations were found to be close to the model predictions which demonstrated the potential use of present model for designing different reactor operating strategies for further improving 1,3-PD production. Acknowledgement The Senior Research Fellowship (SRF) award by Indian Council of Medical Research (ICMR), Govt. of India, New Delhi for the execution of the project is gratefully acknowledged by one of the authors (Ms. Guneet Kaur). References

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1,3-PD produced (g/L)

60 50 40 30 20 10 0 0

20

40

60

80

100

120

140

Glycerol consumed (g/L) Fig. 7. Global yield of 1,3-PD production during model-based fed-batch fermentation.

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