Dynamic models for the production of glucose syrups from cassava starch

food and bioproducts processing 86 (2008) 25–30

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Short communication

Dynamic models for the production of glucose syrups from cassava starch S. Morales a, H. A´lvarez b,*, C. Sa´nchez c a

Bioprocesses Group, Faculty of Engineering, Department of Chemical Engineering, University of Antioquia, Medellı´n, Colombia Research Group on Automatic (GAUNAL), School of Processes and Energy, Faculty of Mines, National University of Colombia, Medellı´n Campus, Colombia c Biotechnology Group, Faculty of Engineering, Department of Chemical Engineering, University of Antioquia, Medellı´n, Colombia b

article info

abstract

Article history:

This paper presents two dynamic models for the production of glucose syrups from cassava

Received 28 January 2006

starch. The models used are based on those proposed by Paolucci et al. [Paolucci, D.,

Accepted 15 March 2007

Belleville, M.P., Zakhia, N. and Rios G.M., 2000a, Kinetics of cassava starch hydrolysis with Termamyl enzyme, Biotechnol Bioeng, 68(1): 71–77; Paolucci, D., Belleville, M.P., Rios, G.M. and Zakhia, N., 2000b, Kinetics of continuous starch hydrolysis in a membrane reactor,

Keywords:

Biochem Eng J, 6(3): 233–238] for the liquefaction stage, and Zanin and Moraes [Zanin, G.M.

Modelling

and Moraes, F.F., 1996, Modelling cassava starch saccharification with amyloglucosidase,

Liquefaction

Appl Biochem Biotechnol 57–58: 617–625] for the saccharification stage. These models were

Saccharification

modified in order to include aspects that were not considered in previously reported studies.

Cassava starch

Hence, the liquefaction stage can be modeled at different operating temperatures and substrate concentrations; furthermore, this model relates the activity of the enzyme with the temperature. This model of the saccharification stage simulates continuous operation at variable operating temperatures. Additionally, it enables the prediction of reduced glucose production due to the inclusion of a thermal deactivation constant. The improvements to each stage of the models permit a better approximation to real behavior by linking the two models to provide a complete simulation of the process. # 2007 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1.

Introduction

Cassava is one of the most important crops in tropical regions (Sa´nchez and Alonso, 2002). It is principally used in the food, textile, and paper industries, as well as in the chemical industry for the production of alcohol, among other uses (Henry et al., 1998). Alcohol is obtained from cassava through a process of fermentation of glucose syrups, produced in a twostep enzymatic process: liquefaction and saccharification. Liquefaction is carried out with the enzyme a-amylase, which fragments the starch into regularly sized chains, resulting in dextrin, maltose, maltotriose and maltopentose (Uhlig, 1998). The enzyme presents an endo-type action and may only hydrolyze a:1–4 starch chains. The amyloglucosidase enzyme

acts in the saccharification stage, in which glucose syrups are obtained from hydrolyzed starch. The enzyme presents an exo-type action and can hydrolyze the bonds /:1–4 and /:1–6, the latter at lesser velocity (Jennylynd and Byong, 1997). Given the great potential for cassava starch syrup production industries in tropical countries, there is a need for tools such as semi-physical models that permit design, control, optimization and scaling of the process. Until now, empirical models based upon Michaelis–Menten type kinetics have been published for each of the glucose syrup production phases, and some articles even demonstrate the combined action of the two enzymes (Akerberg et al., 2000; Wojciechowski et al., 2001). However, no work has been published that presents the two models in a way that allows the description of each of the

* Corresponding author. ´ lvarez). E-mail address: [email protected] (H. A 0960-3085/$ – see front matter # 2007 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.fbp.2007.10.011

26

food and bioproducts processing 86 (2008) 25–30

v Dv Vmx

Nomenclature Liquefaction model P

Ci

P

Ci;lim

E Ea hn kn kon m

n

pn qn R S S0 t T Vf Xn

indicates the oligosaccharide considerations which, through hydrolysis, produce the oligosaccharide with Dp equal to n (g/L) indicates the sum of the oligosaccharides which, through hydrolysis produce oligosaccharide with Dp equal to n, but which remain intact upon hydrolysis completion (g/L) enzyme concentration (ml/L) activation constant (cal/mol) constant (adimensional) reaction constant (h1) Arrhenius constant (h1) value of the oligosaccharide with the least degree of polymerization which produces, upon hydrolysis, the oligosaccharide being modeled, thus for n = 7, m = 8; for n = 6, 4 and 3, m = 7, and for n = 5, 2 and 1, m = 6 oligosaccharide polymerization level to model (1 corresponds to glucose, 2 to maltose, 3 to maltotriose, 4 to maltotetraose, 5 to maltopentose, 6 to maltohexose, 7 to maltoheptose) constant (g/ml h) constant (L/ml h) universal gas constant (cal/mol K) substrate concentration (g/L) initial substrate concentration (g/L) time (h) temperature (K) oligosaccharide formation velocity (g/L h) constant (adimensional)

Saccharification model E Ea E0 F0 Fi Gin Gn Gon Kd Keqn KI Km,n Ks rn

R t T

enzyme concentration (ml/L) activation constant (cal/mol) initial enzyme concentration (ml/L) flow upon exiting the reactor (L/h) flow upon entering the reactor (L/h) oligosaccharide concentration upon entering (mol/L) oligosaccharide concentration (mol/L) oligosaccharide concentration upon exiting (mol/L) enzymatic deactivation constant (h1) condensation reaction equilibrium constant (mol/L) constant of inhibition due to product concentration (mol/L) Michaelis–Menten constant (mol/L) constant of inhibition due to substrate concentration (mol/L) oligosaccharide reaction velocity, the subscript n is the oligosaccharide polymerization level to model, where 1 is glucose, 2 maltose, 3 maltotriose, 4 and 6 are susceptible and resistant maltopentose fractions, respectively (mol/L h) universal gas constant (cal/mol K) time (h) temperature (K)

reaction volume (L) change in volume (L) maximum velocity constant for each oligosaccharide (mol/h ml enzyme) initial maximum velocity constant (mol/h ml enzyme)

V0,mx

phases and thus enables the simulation of the complete process under conditions and operational methods distinct from those previously reported in the literature.

2.

Methods and materials

2.1.

Liquefaction model

For this stage, several models have been postulated. In Henderson and Teague (1988) and Komolprasert and Ofoli (1991), two simple empirical models are presented. These models cannot be applied in this case due to their strong dependence on experimental data. The work of Kilic¸ and Ozbek (2004) includes enzymatic deactivation, while Marc et al. (1983) present a model that takes into account hydrolysis of the malt starch from a- and b-amylase. In spite of their representational capabilities, again these models are strongly data dependent, since they are also empirical models. A model assuming that the starch is comprised of amylose and amylopeptin polysaccharides was postulated in Park and Rollings (1995). The usefulness of this model is its characterization of starch as two polysaccharides. However, representational capabilities are limited to reported experimental conditions. Wojciechowski et al. (2001) used Montecarlo methods in a model that assumes that hydrolysis of the starch is a random process. This assumption is very useful when modelling the hydrolysis stage, since starch partition may be postulated as a random process or as a deterministic process with the enzyme preferring certain sites on the starch chain. Akerberg et al. (2000) suggest a kinetic model due to the combined action of the enzymes a-amylase and amyloglucosidase. However, this approach is not useful for the starch processing that we are modelling here since it uses an integrated model. Other works present models with first order kinetics for different oligosaccharides in a batch process (Paolucci et al., 2000a) and a continuous process (Paolucci et al., 2000b). A third model by the same research group takes into account the loss of enzymatic activity in a continuous membrane reactor (Paolucci et al., 2001). These last three models provide the most realistic process conditions. Therefore, among the various published works about the liquefaction phase, the model postulated in Paolucci et al. (2000a) was selected as a starting point for modelling this phase. This model deals with the production of oligosaccharides at different levels of polymerization, as presented in Eq. (1). This model employs constants determined using cassava starch as a substrate and takes into account the fact that oligosaccharides with a polymerization level of 1–3 cannot be hydrolyzed, given that they are final products of the process.

Vf ðnÞ ¼ kn

! 1 1 X X Ci  Ci;lim ; i¼m

i¼m

1n7

(1)

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food and bioproducts processing 86 (2008) 25–30

Eq. (1) predicts the formation velocity of an oligosaccharide with polymerization degree (Dp) equal to n (Dp = n oligosaccharide). This is a first order equation respect to the substrate, like the Michaelis Menten equation, when the reaction velocity has a lineal substrate dependency (Doran, 1995). When the terms Ci and Ci,lim are subtracted, the result represents the net available substrate for producing oligosaccharides during hydrolysis. The first summation term describes the available substrate for Dp = n oligosaccharide formation. In this way all oligosaccharides which produce further oligosaccharides through hydrolysis with Dp = n are grouped together as Ci. The second summation term, Ci,lim describes the oligosaccharides which produce further oligosaccharides through hydrolysis with Dp = n, but which remain unhydrolyzed once hydrolysis has been completed. The reaction constant kn describes the ability of the available substrate to be hydrolyzed. In Eq. (2) the Paolucci et al. (2000a) model proposes the following expression for calculating the second summation term, Ci,lim. In this formulation, S0 is the initial substrate concentration and hn is an empirical constant, different for each Dp = n oligosaccharide. The value for hn constant must be experimentally identified and serves to delimit the initial substrate concentration of oligosaccharide available for the production of each maltodextrine. 1 X Ci;lim ¼ hn S0

(2)

i¼m

The same authors propose another simple expression for calculating kn (Eq. (3)). This expression indicates the dependence of kn on the current enzyme concentration (E) and initial substrate concentration (S0) within an empirical equation including two constants that must be experimentally identified ( pn, qn). Both empirical constants ( pn, qn) are associated with each Dp = n oligosaccharide.  kn ¼

 pn  qn E S0

(3)

The model postulated in Paolucci et al. (2000a) is valid for substrate concentrations from 50 g/L to 270 g/L, at a temperature of 80 8C, using commercial a-amylase 120 L enzyme (Novo Nordisk1) at various concentration values. However, some parameter adjustments and model structure modifications must be made in order to use the model described as a more realistic process model. The modifications we applied to this base model are presented in Section 3.

2.2.

Saccharification model

Various published works present models for the saccharification stage. Empirical models are formulated in Gaouar et al. (1997a), Ulibarri and Hall (1997) and Gaouar et al. (1997b), but the empirical character of these models means that they require the same process conditions that they used to produce their experimental data. In Bryjak et al. (2000) artificial neural networks were used for modelling the process, but this is also an empirical model. One model using a Michaelis type kinetic for the change in substrate is presented by Kusunoki et al. (1982). This work suggests the utility of Michaelis type kinetics for describing the substrate conversion. Nikolov et al. (1989) include condensation reactions in their model, demonstrating the importance of factoring such reactions into any realistic

saccharification model. Various works include enzyme inhibition due to high concentrations of substrate (Sanroma´n et al., 1996) and product (Beschkov et al., 1984; Nagy et al., 1992; Cepeda et al., 2001; Polakovic and Bryjak, 2004). In addition to this inhibition due to substrate or product, other works assume the existence of condensation reactions for free enzymes (Zanin and Moraes, 1996) and for immobilized enzymes (Zanin and Moraes, 1997). This last group of models demonstrates that important variables need to be considered when designing an effective model of the saccharification stage. Therefore, one of these models (Zanin and Moraes, 1996) was selected because it includes the widest range of oligosaccharides. This model has a multichain mechanism based on the Michaelis–Menten equation, where many substrates exist that compete with the active site of the enzyme, and takes into account the condensation reactions and the inhibition due to substrate and product. Additionally, the constants utilized in this model are for cassava starch. Since the starch is not completely hydrolyzed in the liquefaction stage and the greatest quantity produced in this stage is maltopentose (n = 5), the model considers the oligosaccharide produced in the liquefaction stage to have a polymerization degree of 5 (Dp = 5). The model also assumes that the maltopentose is formed in two fractions, one more susceptible to hydrolysis (n = 4), with a:1,4 chains and one more resistant (n = 6), with a:1,6 chains. Furthermore, the model takes into account the condensation reactions in which isomaltose is produced. Eq. (4) presents this model:

rn ¼

Km;n

h

Vmx ½Gn  ðG2n1 =Keqn ÞE P i ; 6 1 þ ðG=KI Þ þ ð1=KS Þ þ j¼2 G j =Km; j

6

2n (4)

The subscript n indicates the oligosaccharide polymerization degree (Dp): 1 is glucose, 2 is maltose, 3 is maltotriose, 4 and 6 are susceptible and resistant maltopentose fractions, respectively, r is the hydrolysis velocity, G is the concentration of oligosaccharides, Km is the Michaelis–Menten constant, Ks and Ki are inhibition constants of the substrate and product, respectively, Vmx is the maximum speed of production of product, E is the enzyme concentration, Keq3 is the equilibrium constant and Geq is the molar concentration of balance. Note that the term for the condensation (G2n1 =Keqn ) is valid only for maltose and maltotriose (where the numerator G2n1 changes to G G2). The term for inhibition due to substrate (1/Ks) is only valid in susceptible (n = 4) and resistant (n = 6) fractions of the maltopentose. Eqs. (5)–(8) are used to find the variation in the concentration of each oligosaccharide over a period of time:

for Maltopentose dG4;6 ¼ r4;6 dt

(5)

for maltoriose dG3 ¼ r4 þ r6  r3 dt

(6)

for maltose dG2 ¼ r3  r2 dt

(7)

28

food and bioproducts processing 86 (2008) 25–30

for glucose

Table 1 – Parameters for the liquefaction stage

dG ¼ 2ðr4 þ r6 Þ þ r3 þ 2ðr2  r1 Þ dt

n

Ea (cal/mol)

Xn

p (g/ml h)

q (L/ml h)

1 2 3 5

43227.4 28818.27 7204.57 14409.14

0.2 0.2 0.25 0.089

51 89 72 138.3

0.089 0.145 0.066 0.26

(8)

The model and its constants are valid in batch processes, at a temperature of 45 8C, a substrate concentration of 300 g/L, and a commercial amyloglucosidase enzyme (Novo Nordisk1) concentration of 200 AGU/ml. This model must be modified and its parameters must be adjusted in order to obtain a useful process model under more realistic process conditions (see Section 3).

2.3.

Validation of the model

The kinetics of liquefaction and saccharification reactors were obtained in order to validate the models. The liquefaction process was carried out in a 10 L stainless steel reactor, in a batch process with commercial cassava starch at a concentration of 400 g/L employed as a substrate. The 0.5 ml/L of aamylase 120 L (Novo Nordisk1) enzyme was added to the starch suspension at 60 8C in order to prevent thickening of the starch, given that the process of pre-hydrolysis had not been performed. A pH of 5.5 was maintained, and the final temperature reached during the process was 87.5 8C, with a reaction time of approximately 3 h. The saccharification process was carried out in a 5 L glass reactor. The maltodextrine solution resulting from the liquefaction stage, adjusted to a pH of 4.5, was utilized as substrate. 1.5 ml/L of amyloglucosidase 300 L (Novo Nordisk1) enzyme was employed. The reactor worked in a continuous process, but during the first 4 h the process was carried out in a batch, at a temperature of 59 8C. To guarantee a continuous process, a Millipore type polyethersulphone membrane was used. The system operated at a temperature of 57 8C, with a residence time of 5 h. In order to finish the reaction in the two stages, 0.5 ml of NaOH 4N was added to the samples and they were submerged in an ice bath. The quantities of glucose, maltose, maltotriose and soluble oligosaccharides taken as maltopentose (n = 5), were analyzed with high resolution liquid chromatography equipment, HPLC, through one Varian Carbohydrates Ca column, with water as the mobile phase.

Results and analyses

3.1.

Modified liquefaction model

The following modifications were introduced upon the original, previously referenced model in order to make the selected model viable at a substrate concentration of 400 g/L, capable of simulation at temperatures between 60 8C and 87.5 8C, so that the results produced by this model can be used in the saccharification model. Since the maltopentose is produced in greater quantities in n = 4 to n = 7 group, these oligosaccharides were classified as maltopentose (n = 5). To adapt the model at substrate native conditions by means of the Chemotaxis Algorithm (Bremermann and Anderson, 1990), the p constants were changed to n = 1 (48 for 51) and n = 3 (89 for 72) and the q constants to n = 5 (0.53 for 0.26). This means that the production velocity of glucose and maltotriose is fastest in native starch, but in maltopentose production velocity is slower. The h constant was changed to n = 3 (0.26 for 0.36), to show that there is more substrate available for

0.29 0.33 0.36 0.32

production of maltotriose in the starch employed. This could be an indication that the relation between amylopeptine and amylose is different in the starch employed in each study (see Table 1). It was necessary to obtain the liquefaction reactor temperature profile due to the increase in enzymatic activity with increasing temperature; experimental data were adjusted to linear equation (9). Also, the Arrhenius equation (10) was utilized so that the velocity constant (kn) changed with the temperature, using activation energy data published by Paolucci et al. (2001). Again, using the Bacterial Chemotaxis Algorithm (Bremermann and Anderson, 1990), the activation energy was multiplied by a different factor (Table 1). A possible explanation for this correction is that the enzyme may have a greater preference for the production of oligosaccharides with n = 3 and n = 5 than with n = 2 and n = 1. The summative term over Ci was found by subtracting the summative term Ci,lim from the substrate. It was necessary to multiply this new term by the factor Xn (Table 1), which means that only a determined quantity of substrate is available to produce each one of the oligosaccharides. This is shown in Eq. (11). T ¼ 22:678t þ 60:481

(9)

  Ea kn ¼ kon exp RT

Vf ðnÞ ¼ kn xn S 

(10)

m X Ci i¼1

! 

! 1 X Ci;lim ;

1n5

(11)

i¼m

Table 1 presents the first stage parameters, and Fig. 1 demonstrates the process simulation compared with the results of the liquefaction stage experimental phase. The model utilized in the liquefaction stage, compared with experimental data, presented a global error average of 6.62%.

3.2.

3.

h

Modified saccharification model

The following modifications were introduced upon the original previously referenced model in order to make the selected model viable in a continuous process, show enzymatic activity decline due to operation time and temperature, capable of being simulated at temperatures between 57 8C and 59 8C, and functional for a commercial enzyme preparation of 300 AGU/ml. The maltopentose resistant and susceptible fractions were added together with the purpose of correlating the saccharification and liquefaction models. Due to the fact that this was operated as a continuous process, taking into account that the stable state adaptation phase is a batch process, it was necessary to balance the system mass (Eq. (12)). Given that the constants presented in the Zanin article are valid for a batch process, the Bacterial Chemotaxis Algorithm (Bremermann and Anderson, 1990) was used to adjust some constants for the continuous process, as in the case of Vm and km for maltopentose (n = 5) and maltose (n = 2), and Keq for maltotriose and maltose (Table 2). The values of these parameters for other

food and bioproducts processing 86 (2008) 25–30

29

Table 3 – Saccharification stage constants Constant

Value

Ks (mol/L) Ki (mol/L) Kd (1/h) Ea (cal/mol)

0.126 0.04 2.87E2 50604

tion stage showed results similar to the experimental data, presenting a global error average of 7%.

4.

Fig. 1 – Comparison of the oligosaccharide formation kinetics based on experimental data and model simulation of the liquefaction stage. Lines are simulated data, points are experimental data.

values of n were the same as those reported in the original paper (Zanin and Moraes, 1998). To make this model work at other temperatures, the Arrhenius equation was used (Eq. (13)), using the activation energy reported as deactivation energy in Zanin and Moraes (1998), since this is valid for temperatures between 55 8C and 75 8C, the range within which the temperatures utilized fall. Given that this system operated continuously, which implies long periods of operation with the enzyme, the need arose for an enzymatic deactivation constant (Eq. (14)). The constant Kd and the deactivation energy were taken from the same article (Table 3). Given that the commercial enzyme concentration for this work was 300 AGU/ml, a correction was made by multiplying E by a factor of 1.5, which takes into account the extra concentration utilized. The corrected model is presented in Eqs. (12) through (14). dGn Fi Gin F0 Gon Gn Dv ¼  þ rn þ v dt v v

(12)

  Ea Vmx;n ¼ V0;mx;n exp RT

(13)

E ¼ E0 expðkd tÞ

(14)

Discussion and conclusions

The adjustments made to the model to the models taken as base for modelling the two process stages permit their use in more realistic process conditions. The model published by Paolucci et al. (2000a) mean that the model can be used in simulations with wide intervals of substrate concentration (60–400 g/L) and temperature (60–90 8C); this is necessary for the simulation of processes in which high substrate concentration levels are employed and the enzyme begins hydrolysis at low temperatures. The modified Zanin and Moraes (1996) model permits process simulations in substrate concentrations of 400 g/L, at temperatures between 45 8C and 65 8C, in both batch and continuous processes, and for simulations of declined conversion due to enzyme inactivation with time and temperature. The models proposed in this article for the two stages, with their respective constants, are more complete, since they now predict the enzymatic hydrolysis behavior of the cassava species native to Colombia, as well as being valid for a wide range of variables that affect the currently employed process. These modifications could be included at the industrial level for the production of dextrin, for example, the development of a pre-hydrolysis stage by the addition of a-amylase enzyme at 60 8C, raising the temperature to 90 8C, or the production of glucose syrup in continuous process. The models can also be used as a tool in the design of bioreactors, due to the information provided by the simulation of the process. Additionally, the models can be used to control the process and finally, the two models are joined to provide a complete

Table 2 presents the second stage parameters, Table 3 presents stage constants and Fig. 2 demonstrates the process simulation and the results of the saccharification stage experimental phase. The model employed in the saccharifica-

Table 2 – Parameters for the saccharification stage n

Keq (mol/L)

Km (mol/L)

1 2

0.0544 200

0.003

3 5

a

8

0.0264 0.0264

Values valid in continuous process.

V0,mx (mol/h ml enzyme) 4.69E32 7.244E34a 1.881E34 9.24E34 7.244E32a

Fig. 2 – Comparison the oligosaccharide formation kinetics based on experimental data and model simulation of the saccharification stage. Lines are simulated data, points are experimental data.

30

food and bioproducts processing 86 (2008) 25–30

process simulation of the production of glucose syrups: an integrated process model. In the future, the models described in this article, combined with isomerization process modelling, will enable the entire glucose and fructose syrup derivation process to be completely simulated.

Acknowledgements To Colciencias and the University of Antioquia for the project financing, to COLDAENZIMAS (Novo Scientific) for the donation of the enzymes, and to the National University for allowing the fulfillment of the analytical phase.

references

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