Aplicacion de Tecnicas de Modelado Para La Mejora de Bioprocesos Industriales

8/16/2019 Aplicacion de Tecnicas de Modelado Para La Mejora de Bioprocesos Industriales

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Journal of Biotechnology 59 (1997) 63–72

Application of modelling techniques for the improvement of industrial bioprocesses

J.M. Brass a,*, F.W.J.M.M. Hoeks a, M. Rohner b

a LONZA AG , Biotechnology Research and Deelopment Group, CH -3930 Visp, Switzerland b LONZA Biotec s.r.o., Biotechnology Production, CZ 281 61 Kourim, Czech Republic

Received 18 November 1996; received in revised form 26 August 1997; accepted 22 September 1997

Abstract

The impact of modelling on implementation and improvement of four industrial bioprocesses will be reviewed. In

one case, (R)-3-hydroxy-4-(trimethylammonio)-butanoate (L-carnitine) production, modelling facilitated finding the

most cost-effective fermentation mode. In two other cases, 6-hydroxynicotinic acid and 5-methyl-2-pyrazincarbonic

acid production, modelling helped to develop and practise adequate process control systems. In still another process,

nicotinamide production, a combination of modelling with process simulation resulted in a drastic reduction of time

and cost for process development and also helped to choose the most cost-effective process design. © 1997 Elsevier

Science B.V.

Keywords: Modeling; Biotransformation; Industrial bioprocesses; L-Carnitine; 5-Methyl-2-pyrazine carboxylic acid;6-Hydroxynicotinic acid; Nicotinamide

1. Introduction

Because a single bacterial cell contains several

thousand enzymes catalysing different biochemi-

cal reactions, growth and product formation in

biotechnology are a result of complex in-

teractions. Using modelling as a tool, it is possible

to simplify these complex interactions qualita-

tively and quantitatively. These formulated mod-

els can be validated by comparison of the

behaviour predicted by the model with experimen-

tal data. Observed differences between model and

experimental data are used to further improve themodel until good agreement is obtained. Even

variables that are not immediately evident can be

detected, and the performance under different* Corresponding author. Tel.: +41 27 9486082; fax: +41

27 9486581.

0168-1656/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved.

PII S 0 1 6 8 - 1 6 5 6 ( 9 7 ) 0 0 1 6 5 - X


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J .M . Brass et al . / Journal of Biotechnology 59 (1997) 63–72 64

conditions can be predicted. However, it is neces-

sary to know the coherence of variables involved

(Sonnleitner and Fiechter, 1989). In combination

with statistical tools, the modelling technique

complements purely empirical procedures, acceler-

ates and simplifies process development and opens

access to better process understanding.

The off-line and on-line measurement of therelevant parameters and the control conception

for four industrial bioprocesses (biotransforma-

tions) have been described in detail recently

(Rohner and Meyer, 1995; Hoeks et al., 1996).

The models describe the bioprocesses which result

in automated manufacturing at 15 and 50 m3

scale. This review, which is based on an oral

presentation at the First European Symposium on

Biochemical Engineering Science, Dublin (Brass et

al., 1996) will focus on the impact of modelling on

implementation and improvement of these four

industrial bioprocesses.LONZA routinely applies modelling and so-

phisticated process control to overcome inhibition

by chemically produced substrates and also to

avoid formation of inhibiting by-products of the

biotransformation. LONZA’s bioprocesses for the

production of fine chemicals are exclusively fed-

batch processes, where substrates are added dur-

ing fermentation and biotransformation according

to a process-specific regime. This regime is con-

trolled by a computer based algorithm (model),

which was developed individually for each pro-

cess. The time course of the consumption of sub-strates, and the biomass and product formation

are measured and used for controlling the com-

plex production systems.

The economics of the biotechnological produc-

tion of fine chemicals does not only depend on the

bioprocess but also on the product isolation or

downstream processing. Consequently, the inte-

gral development and optimisation approach of a

bioprocess and the subsequent downstream pro-

cessing (if not upstream processing) leads to a

least-cost production process. Modelling as a tool

for process integration will be illustrated for thebiotechnological (R)-3-hydroxy-4-(trimethylam-

monio)-butanoate (L-carnitine) production pro-

cess.

2. Impact of modelling on the implementation and

improvement of four bioprocesses

2.1. Chemical ersus biological L-carnitine

synthesis

L-Carnitine belongs to a group of food factors

known as vitamin nutrients (Scholte and DeJouge, 1987; Ferrari et al., 1992). LONZA has

developed and piloted both, a chemical route

(Voeffray et al., 1987) and a biotransformation

for the production of L-carnitine (Kulla and

Lehky, 1985; Kulla et al., 1986). Comparison of

piloted chemical and biotechnological processes

and subsequent industrial realisation of the most

advantageous process has turned out to be a

strength of LONZA. Two important advantages

of the biological L-carnitine process have been

found:

(i) The biotransformation process yields L-car-

nitine with almost 100% enantiomeric excess. All

other known chemical production processes yield

L-carnitine with a lower optical purity (data not

shown).

(ii) The biotransformation process (fed-batch

process) is ecologically superior in comparison to

the piloted chemical production process.

As shown in Fig. 1, waste treatment costs are

much lower in the biotechnological process com-

pared with the piloted chemical L-carnitine pro-

duction process. The bioprocess exhibitsdrastically reduced salt loads per ton of L-car-

nitine produced, much lower wastewater volu-

mina and total organic carbon loads, and much

lower waste for incineration compared with the

chemical production process. The lower waste

problems of the bioprocess compared with chemi-

cal production clearly translate into lower produc-

tion costs of the bioprocess.

2.2. The original L-carnitine biotransformation


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Fig. 1. The biotechnological production of L-carnitine (fed-batch process) is ecologically superior compared with the piloted

chemical production route. Efforts for waste treatment can be kept much lower in the biotechnological process compared with those

necessary for the chemical production process.

The biotransformation system is based on an

interrupted catabolic pathway. Carnitine dehyro-

genase was removed from the organism by muta-

genesis. The bacterial strain HK 1349 (taxono-

mically between Agrobacterium and Rhizobium

but close to Rhizobium meliloti ) transforms chem-

ically synthesised -butyrobetaine into L-carnitine,

which is excreted into the medium in stoichiomet-ric amounts (Kulla and Lehky, 1985; Kulla et al.,

1986). The first described process for the biotrans-

formation of chemically synthesised -butyrobe-

taine into L-carnitine was a chemostat with cell

recycling (Kulla and Lehky, 1985; Kulla et al.,

1986). This process was successfully scaled up to

450 l (Hoeks et al., 1992).

2.3. Modelling of the L-carnitine bioprocess for

identification of the most cost-effecti e

fermentation mode

For complex biochemical multienzyme and

multicomponent reaction systems with micro-


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Fig. 2. Blackman kinetics: specific product formation rate of

whole cells as a function of the substrate concentration.

Fig. 3. Schematic indication of the different time intervals in

the fed-batch biotransformation of -butyrobetaine into L-car-

nitine with substrate and product concentration as a function

of time. Relatively long turn around time (t-tat) and lag time

(t-lag for growth of biomass) intervals have to be acceptedbefore the start of the operational time (t-op, biotransforma-

tion) and the rest conversion time (t-rc, complete conversion of

substrate without further educt feed). The advantages are that

the process can be run at near-maximal rate during t-op and

results in 99.5% conversion of -butyrobetaine.

organisms, the unstructured model of the reaction

rate as a function of the concentration of a reac-

tion component, Blackman kinetics (Dabes et al.,

1973) is the simplest and often superior. With this

kinetic relationship it is assumed that the reaction

component is rate-limiting below a critical con-

centration. Above the critical concentration, othercomponents and/or reaction mechanisms are rate-

limiting. Below the critical concentration, the re-

action kinetics are like a first-order reaction (Fig.

2). Above the critical concentration the behaviour

is zero order.

For the L-carnitine biotransformation, which is

quantitative, it has been reported that the reaction

rate depends on the substrate, i.e. -butyrobe-

taine, concentration. Furthermore, a high reaction

rate can only be obtained at relatively high -bu-

tyrobetaine concentrations of about 5 g l−1

(Kulla and Lehky, 1985; Kulla et al., 1986; Hoeks

1990; Hoeks et al., 1992). The original continuous

L-carnitine production process with cell recycling

had a high volumetric productivity (0.034 mol l−1

h−1). However, a drawback of the continuous

biotransformation was that a mixture of L-

carninitine and -butyrobetaine was formed

(Kulla and Lehky, 1985; Kulla et al., 1986).

Costly recrystallisations were required to separate

the substrate from the product.

Apart from continuous fermentation (mode A),

three main alternative fermentation modes (modes

B– D) were evaluated empirically and by mod-

elling to achieve complete conversion of -butyro-

betaine and to eliminate the above

recrystallisations (Hoeks et al., 1996).

Table 1

Process modelling of L-carnitine production: effect of different fermentation modes on productivity and production cost

B, 1-stage continuous, re- C, 2-stage con- D, fed-batchFermentation mode A, 1-stage con-

duced feed rate tinuoustinuous

0.034 0.0019 0.0155 0.0075Volumetric productivity (mol l−1 h−1)

99.591Bioconversion yield (mol.% of substrate) 99.599.560100Cost estimation for the biotransformation 75105

and the isolation of L-carnitine (arbi-

trary units)


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1. Mode B: reduction of the feed rate in the

continuous process to allow complete bio-

transformation of -butyrobetaine.

2. Mode C: development of a 2-stage continuous

process with a second vessel for rest conver-

sion of -butyrobetaine.

3. Mode D: development of a fed-batch process

which runs at high substrate concentration,followed by a rest conversion time for com-

plete conversion of -butyrobetaine.

Table 1 summarises the industrially relevant

parameters of the four fermentation modes. Mod-

elling shows reduction of the feed rate in the

continuous fermentation (mode B) could theoreti-

cally result in complete conversion but at the

expense of the volumetric productivity. It is esti-

mated that this 18-times lower productivity, com-

pared with the original operation of the

continuous process with 8% of the -butyrobe-

taine left, will outweigh the cost savings in thedownstream processing (Table 1).

The two-stage continuous fermentation mode

with cell recycling (mode C) has a much higher

volumetric productivity of 0.0155 mol l−1 h−1.

But it seems unlikely that this higher productivity

is attractive enough for production, because of the

technical difficulties of a complex plant with two

bioreactors in series and with two cell-recycling

systems, which has to be operated in a continuous

aseptic way. Beyond doubt, the two-stage contin-

uous process will require a relatively high invest-

ment per unit working volume. This is reflected by

the estimated production cost (Table 1 (Hoeks et

al., 1992)).

The development of a fed-batch process for the

biotransformation (mode D) showed that almost

complete conversion of -butyrobetaine into L-

carnitine made the costly separation of substrate

and product obsolete (Hoeks, 1990; Hoeks et al.,

1992). Even though the fed-batch fermentation

mode has an overall volumetric productivity

which is only half that of mode C (0.0075 mol l−1

h−1), it is still economically the most cost-effec-

tive solution. Fig. 3 shows the different steps of

the fed-batch fermentation mode. Thus LONZAdecided to produce L-carnitine in a multipurpose

15 m3 fed-batch plant in the Czech Republic,

which was adapted to our specific needs (Meyer,

1993). Nowadays, the process runs in a 50 m3

fed-batch plant.

2.4. Modelling of the biotransformation of nicotinic

acid to 6 -hydroxynicotinic acid

The process (Kulla and Lehky, 1991) and process

control for production of the fine chemical interme-

diate 6-hydroxynicotinic acid was described in

detail recently (Rohner and Meyer, 1995). The

process consists of two steps. In the first manufac-

turing step, the cells are grown and induced on

nicotinic acid as a single carbon source. Nicotinic

acid is completely oxidised via the intermediate6-hydroxynicotinic acid to carbon dioxide, biomass

and water. Complete nicotinic acid degradation is

observed only if nicotinic acid is below inhibiting

concentrations (0.4 g l−1). The formation of the

enzymes involved is growth-related. Subsequently,

in the second manufacturing step, the inhibitory

effect of high nicotinic acid concentrations (0.4

g l−1) is used to block the catabolism downstream

of 6-hydroxynicotinic acid, resulting in the accumu-

lation of 6-hydroxynicotinic acid. Respiratory quo-

tient (RQ) values, calculated from the molar off-gas

fractions provide the key information on the statusof the biotransformation. Three different RQ con-

ditions for biomass formation were defined for

biomass formation: (i) RQ1.1; (ii) RQ=1.1; and

(iii) RQ1.1.

6-Hydroxynicotinic acid, the RQ value equals

1.1. Rapid degradation of accumulated 6-hydrox-

ynicotinc acid results in RQ values much higher

than 1.1. Accumulation of 6-hydroxynicotinic acid

results in RQ values below 1.1. RQ turned out to

be a fast, reliable indirect parameter to monitor any

accumulation of nicotinic acid during biomass

production.The partial pressure of oxygen was the leading

parameter for process control, with RQ and 6-hy-

droxynicotinic acid concentrations as adjacent


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parameters for on-line process control. The par-

tial pressure of oxygen (pO2) has a similar func-

tion as the idle motion control of a motor. If the

dissolved oxygen concentration in the fermenta-

tion broth is high enough (pO2 high), the feed of

nicotinic acid is switched on. If the pO2 is low,

nicotinic acid feed has to be stopped. If the pro-

cess is fed in the right regime (no over- norunder-dosage of nicotinic acid), a strong, fast and

clear response between dosing of nicotinic acid

and use of oxygen (pO2 response) is observed. The

process can be driven with a cascade control of

the nicotinic acid dose valve connected with a pO2controller (see Fig. 4).

The nicotinic acid concentration at its upper

level is controlled by the RQ. The RQ indicates

the inhibition of the catabolism by nicotinic acid.

In order to allow catabolism of nicotinic acid for

biomass production, the dosage/feed control valve

of nicotinic acid is closed and nicotinic acid ismetabolised and the concentration is reduced. As

soon as the RQ value is again higher than 1.1, the

dosage valve is opened. The oscillations of pO2and RQ during growth of Pseudomonas acidoo-rans on nicotinic acid are shown in Fig. 5.

When sufficient biomass has been formed for

fast 6-hydroxynicotinc acid production, nicotinic

acid is added in a fast and uncontrolled way to

achieve high concentrations of nicotinic acid in

order to prevent further biomass production. The

RQ falls immediately after adding nicotinic acid,

down to values 1.1 and only the first reaction

step of the whole nicotinic acid degradation path-

way can take place. After nicotinic acid is trans-

formed, the increasing RQ values indicate the end

of the production step of 6-hydroxynicotinic acid

(Rohner and Meyer, 1995). Modelling clearly

helped to develop and practice this process con-

trol system.

2.5. Modelling of the biotransformation of

2 ,5 -dimethylpyrazine to 5 -methyl -2 - pyrazinic acid

Fig. 4. Control concept for the cell growth phase for later

6-hydroxynicotinic acid (6-HNS) fermentation. Aim of this

control concept is optimal growth and proper induction of the

biomass (Pseudomonas acidoorans) at nicotinic acid (NS)

concentration below 0.4 g l−1. Higher concentrations of nico-

tinic acid inhibit further break down of 6-hydroxynicotinicacid. This regulatory effect is later used in the biotransforma-

tion period at NS concentrations much higher than 0.4 g l−1

(not shown). SP, setpoint; MV, measured value; and RQ,

respiration quotient.

The process (Kiener, 1991, 1992) and process

control for production of this pharma intermedi-

ate was described in detail recently (Rohner and

Meyer, 1995). The biotransformation is a one-

step process with biomass growth and product

formation in parallel. The cells are grown on

p-xylene as the sole carbon source. The enzymesystem for the degradation of p-xylene into 5-

toluic acid is the same as the one used for the

biotransformation of 2,5-dimethyl pyrazine to 5-

methyl-2-pyrazinecarboxylic acid via alcohol and

aldehyde formation. For the model, the oxida-

tion of xylene and the oxidation of 5-methyl-2-

pyrazine carboxylic acid, was considered as a

one-enzyme process. The induction of the en-

zymes for the biotransformation is growth re-

lated. For the production of active biomass for

biotransformation purposes, xylene should not

be restricted. For production, however, the en-zyme system should be saturated with 2,5-

dimethyl pyrazine, thus the concentration of

p-xylene should be minimised. p-Xylene is com-


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Fig. 5. Cell growth phase for later 6-hydroxynicotinic acid

production. The oscillations of pO2 and RQ during growth of

Pseudomonas acidoorans in 10 m3 production scale are shown.

The highly-inhibiting carbon source is fed under full automa-

tion based on three control cycles, namely 6-hydroxynicotinic

acid-, pO2- and RQ-control. Due to the tuning a transientprocess occurs.

The adopted system (Yamada, 1988) and devel-

opment of the process and process control for

production of this vitamin was described in detail

recently (Rohner and Meyer, 1995). Nicotinonitrile

(substrate) is transformed into nicotinamide

(product) by a cellular biocatalyst. For a certain

temperature and pH, the reaction rate is given. The

enzyme, however, is not stable and exhibits a lossof activity. The loss of activity is mainly due to the

actual nicotinonitrile concentration and the reac-

tion temperature. The nicotinamide causes less loss

of activity. The pH had no inhibiting influence on

the reaction system within the range tested.

Michaelis– Menten type kinetics were applied to

the enzymatic reaction system. Temperature was

considered as a parameter. For the loss of activity,

a first-order degradation of the enzyme was as-

sumed. The loss of activity was interpreted as a

degradation of the specific activity (qu, max) over the

time.Time courses for the variation of nicotinamide

and nicotinonitrile concentrations in different reac-

tor configurations were simulated with a computer

(Fig. 7). The reaction in a cascade of five reaction

vessels which are continuously operated is simu-

lated. The biocatalyst is fed in a countercurrent

flow (from stage 5 to 1) and the nicotinonitrile is

fed in a current flow. Fig. 7 depicts the specific

educt transformation rate and the actual educt

concentration at each stage. In stage 5, the specifi-

cations for the product are reached and the nicoti-

nonitrile is no longer measurable.Based on this kinetic process model, LONZA

designed the process for an industrial production

process of 3000 tons nicotinamide per year using

extensively simulation methods in combination

with experimental results. The process was devel-

oped on a computer using the simulation language

SIMNON. Since experiments were quite time- and

labour-consuming, simulation not only speeded up

the process design but reduced costs considerably.

3. Conclusion

We have reviewed four typical LONZA Biotec

processes and have worked out the impact of

pletely oxidised only if p-xylene does not form

a liquid–liquid phase in the fermenter. Both p-

xylene and 2,5-dimethylpyrazine kill the cells at

higher concentrations.

The process control (Fig. 6) is more com-

plex, since two feed streams are used for regu-

lating the process. pO2 is the main parameter

for process control. p-Xylene concentrations

(measurement in the exhaust gas of the fer-menter) and 2,5-dimethylpyrazine concentrations

(detection in the fermenter broth by HPLC)

are used as adjacent parameters for on-line

process control (Fig. 6). Modelling was essen-

tial for design and practice of proper process

control

2.6. Modelling the nitrile hydratase process


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Fig. 6. Control concept for the 5-methyl-2-pyrazine carboxylic acid (MPAC) fermentation process. Aim of this control concept is

to achieve optimal growth and induction of the biomass (Pseudomonas putida) on p -xylene at optimally-balanced p -xylene (carbon

source) and 2,5-dimethyl-pyrazine (DMPY, substrate) concentrations. Carbon source and substrate should not be overdosed since

the formation of a two-phase mixture was found to kill the cells. In addition, for optimal biotransformation, the cellular enzyme

system should be saturated, mainly with dimethyl-pyrazine.

modelling on realisation and improvement of

these industrial bioprocesses (Brass et al., 1996).

In the case of the biotransformation of -butyro-

betain into L-carnitine, it has been demonstrated

that the product isolation process can be strongly

affected by the process design of the foregoing

bioconversion steps. The process was largely

defined by the classical development approach. In

the final process design, however, modelling

helped considerably to find the most cost-effective

fermentation mode (see Table 2). Based on the

know-how derived by the classical approach

(comparison of piloting data from a continuous

and a fed-batch process) and based on modelling

data of other fermentation modes (see Table 1),

LONZA decided to produce L-carnitine in multi-

purpose 15 and 50 m3 fed-batch plants in the

Czech Republic (LONZA Biotec s.r.o.).

Table 2

Impact of empirical data and process modelling on the implementation and improvement of four industrial bioprocesses of LONZA

Impact of modellingBioprocess Impact of empirical pro-

cess development

+++ Helped to find the most cost-effective fermentation modeL-Carnitinea

Helped to develop and practice an adequate process control system+6-OH-nicotinic

acidb

Methylpyrazine- Helped to develop and practice an adequate process control system++

carboxylic

acidb

Process simulation considerably speeded up process design (one experiment=1000+Nicotinamideb

h) and helped to find the most cost-effective biotransformation design

a Hoeks et al., 1996.b Rohner and Meyer, 1995.


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Fig. 7. Process simulation of a 5-stage continuous cascade

with the biocatalyst fed in the countercurrent flow. The

nicotinonitrile is fed in the current flow direction (stages 1

to 5). The five upper curves show the specific substrate

transformation rate (qu). Since the biocatalyst and the

nicotinonitrile are fed constantly, steady state process condi-

tions occur. The biocatalyst leaves the reactor system with a

final specific rate of 30% of the initial rate. The lower

curves show the actual concentration of the nicotinonitrile

(u) in each stage.

opment time, since the number of actual experi-

ments could be drastically reduced, thus reducing

cost considerably (Table 2). The presented exam-

ples show that modelling methods are adequate

tools to save costs. It is therefore extremely im-

portant that these tools are implemented as early

as possible in research and development for indus-

trial bioprocess.

Acknowledgements

We thank M. Widmer, LONZA Stab für

Umweltschutz und Sicherheit for his great help in

compiling the ecological data on L-carnitine pro-

duction.

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Aplicacion de Tecnicas de Modelado Para La Mejora de Bioprocesos Industriales