institute of biotechnology and biochemical engineering 1 bernd nidetzky vo bioprocess technology 1...

Institute of Biotechnology and Biochemical Engineering

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Bernd Nidetzky VO Bioprocess Technology 1

BIOPROCESS TECHNOLOGY 1IntroductionMaterial and energy balances in bioprocessesBalances for metabolic processes in cellsBioprocess kinetics and reaction kinetics Transport processesBioreactors and bioreactor operationsReaction engineering and process design

Literature: H. Chmiel (Ed.), Bioprozesstechnik, 2006, Elsevier; P. M. Doran, Bioprocess Engineering Principles (2nd ed., 2013), Academic Press, London

H.W. Blanch, D. S. Clark, 1996, Biochemical Engineering, Marcel Dekker, New York; A. Moser, 1988, Bioprocess Technology, Springer Verlag, Berlin; J.E. Bailey, D.F. Ollis, 1986, Biochemical Engineering Fundamentals, McGraw-Hill, New York


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IntroductionBiotechnology is the scientific study of the fundamentals, the

development, the implementation and the operation of bioprocesses in production processes at industrial scale. (The use of quantifiable methods ensures reproducibility.)

Bioprocess technology constitutes the reaction technology of biochemical and biological processes. It deals with the general principles of implementation, operation and optimization of bioprocesses at the scales of technological operation.

Thermodynamics and kinetics of bioprocesses are the

central elements of any bioprocess development.


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Products of biotechnological production

Many so-called classical products of biotechnology are made at a scale of multihundred tons/year and have a market price of just a few Euro.

The optimization of each process step in their production is important for process economy.

Which substrate to use is a critical decision.


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Products of biotechnology (therapeutic proteins and enzymes)

• Recombinant DNA Tech-nologie is the basis.• Selection of a suitable host determines productivity but also product quality. • Many recombinant proteins are products of mass production. Making them economically therefore demands an optimized process technology. • Downstream processing (DSP) is a key cost factor.

Top bio-pharmaceutical products (2004)

1. EPO (about 11 bn. $)

2. Insulin

3. Remicade (monoclonal antibody against rheumatoid arthtritis, ...)

4. MAbThera (monoclonal antibody against lymphoma, ...)

5. Enbrel (chimeric fusion protein, comprising a monoclonal antibody against TNF a; used to treat psoriasis, different forms of arthritis, ...)

6. Neulasta (PEGylated Granulocyte colony stimulating factor, used during chemotherapy)


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Steps in the chain of bioprocess development

Abbildung. Parallel process development is a characteristic feature of the implementation of biotechnological processes in industry. This holds especially true for products of medicinal use. Time to market is usually several years for these products. Demonstration that the product can be made in principle is however done within 6 to 12 months.


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Abbildung. Schematic representation of the steps of bioprocess development, from gene to product.


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Situations frequently encountered during initial steps of process development

A new catalyst is neededScreening of microorganisms, genetic engineering and evolution to improve strain properties, enzymes, cell cultures, platform organisms and chassis strains

A new substrate needs to be processed or convertedNatural raw materials, biocatalysis in organic chemistry, waste and waste materials

Production of genuinely new product, or a genuinely new productionMetabolic engineering, synthetic biology, therapeutic proteins

Genuinely new process technologies High-performance reactors, miniaturisation and parallelisation, DSP, measurement and control in view of implementation of principles of Process Analytical Technology (PAT) und Quality-by-design (QbD)


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Metabolic Engineering

Definition: The directed improvement of product formation or cellular properties through the modification of specific biochemical reaction(s) or the introduction of new one(s) with the use of recombinant DNA technology

Implementation of new metabolic competences

Flux enhancement for a particular step or a series of steps in metabolism

Enhanced gene(s) expression

Improvement of kinetic properties of a certain enzyme

Deletion of competing steps in metabolism


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Fundamentals of quantification in bioprocesses

Figure: Schematic representation of a bioreaction catalyzed by microbial flocs (aggregated microorganisms) is shown. 1, Gas/liquid boundary; 2 Liquid phase; 3 Film at the liquid/solid interface; 4 Solid phase – cell mass; 5 individual cells.

Key variables: Biomass - X, (limiting) substrate - S, product - P, oxygen - O, carbon dioxide - C, respectively in g/L or M; heat of reaction- H, in kcal/L

Quantification: rate, stoichiometry, productivity (space-time yield), conversion and yield, costs


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Figure: Schematic workflow of systematic bioprocess development using mathematical modelling of process kinetics. Models are useful• to calculate the conversion/yield of the reactor under different operational conditions,• to calculate the reactor behavior under variation of conditions,• to generalize within certain process boundaries, • as basis for optimization, • to identify important process variables unknown or not yet identified to be important, and• to support clarification of reaction mechanisms.


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Quantitative process analysis

Relevant quantities and their physical dimensions Basic quantities (length, mass, time, temperature, current and light intensity);

derived quantities (z.B. acceleration, power, shear stress, shear rate, viscosity, torque, angular velocity, mass flux, yield coefficient, ....)

Dimensionless numbersExample 1: Reynolds number (Re) in fluid mechanics as measure of turbulence.

In stirred tank reactors: Re = NiDi2/, whereby: stirrer speed Ni [1/s]; stirrer diameter Di

[m]; fluid density [kg/m3]; fluid viscosity [kg/(m s)]

In tubular reactors : Re = uD/whereby: fluid velocity u [m/s]; tube diameter D [m]

Beispiel 2: Power number (Np) in bioreactors; Np = P g / (Ni3Di

5), whereby: power P [m2kg/s3]; gravitational force g [m/s2]

Dimensional correctness of mathematical equations usedP. M. Doran, Kapitel 2


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Reactor concepts of process engineering (1)

Ideally mixed stirred tank • Batch• Continuous operation • Cascade of stirred tanks• Fed batch

Tubular reactor with ideal plug flow • Continuous operation


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The fluid flow through the tubular reactor is often considered to be plug type, not allowing any dispersion or back mixing of fluid elements as these elements migrate through the reactor (in the axial dimension).

The ideal concept is hardly met in reality, but used frequently in theoretical reactor design.

Ideal Re > 4100 Re < 2100

Re D v / , where D is the tube diameter, v is the fluid velocity, is the fluid density, and is the dynamic viscosity of the fluid.


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General mass balance of the reactors• Change = Massin - Massout + Production (or Consumption)

• d(VCA)/dt = FinCAin - FoutCAout + rAVwhere V is reactor volume (L, m3), CA is the concentration of substance A (M, mol m-3), F is volumentric flow (e.g. mL h-1; m3 s-1) and rA is the reaction rate of conversion of A (e.g. mol

m-3 s-1). Indices „in“ and „out“ stand for in-flow and out-flow. t is time.

• d(VCA)/dt is the (differential) change of mass A with time. Experimentally one measures differences: (VCA)/t.

• Usually we have: V = constant, because Fin = Fout. Therefore, mass balance can be written as a concentration balance.

• d(VCA)/dt = CAdV/dt + VdCA/dt (where dV/dt = 0 and thus)

• dCA/dt = D (CAin - CAout) + rA (where D = F/V)

• D is the dilution rate (s-1) and = D-1 is the mean residence time.



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iSTR in batch operation• Change = Production (or Consumption)

• dCA/dt = rA

• rA-1

dCA = dt

• Integration with CA0 at t = 0 and Cat at t = tb yields

• tb = rA-1

dCA Reaction time corresponds to the area under the curve of ƒ(CA)

• Example: for rA = - kCA, we have: tb = - k-1 CA-1

dCA = - k-1 ln (CA/CA0)

• An exponential decrease of A is implied, and tb is the time required to achieve a certain decrease in A.

• Other expressions of rA can be treated analogously.

• The Michaelis-Menten equation of enzyme kineticsdCA/dt = rA = - Vmax CA/(KMA + CA) yields analytically integrated:

Vmax tb = KMA ln(CA0/CA) + (CA0 - CA)



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iSTR in batch operation•Relevant questions for process optimization

Minimum duration of batch (tb)

Conversion U = (1 - CA/CA0) 100% at certain tb

Productivity P = (CA0 - CA)/tb

Enzyme concentration E to reach a certain productivity (Vmax = kcat E) where kcat (s-1) is the catalytic constant

Effect of KMA on conversion and productivity

(Specific) productivity of catalyst P/E (s-1)(Total) productivity of catalyst (P related to E/ tb) (dimensionless)

•Stoichiometry: CA0 - CA = CP P, Product

•tb (effective) = tb + tdown tdown (cleaning, preparation, ...)

•Stabilities of E, A, P



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iSTR in continuous operation (Fin = Fout)• Change = 0 (steady state) = Massin - Massout + Production (or Consumption)

• dCA/dt = D (CAin - CAout) + rA = 0

• D = - rA/(CAin - CAout) = (CAout - CAin)/ rA Residence time corresponds to area under a rectangle from

CAin – CAout

• STRs can be arranged in a cascade, whereby we have for each STR: j = Vj/F = (CAj - CAj+1)/ rA

• Relevant themes for process optimization: Minimum duration of reaction (); note the two variables F and VThe replaces tb in the calculation of the other reaction parameters

Advantage of continuous operation: tdown = 0 (or minimal).



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STR in fed batch (Fin (t) > 0; Fout = 0)• d(VCA)/dt = FinCAin + rAV

• Equation cannot be analytically integrated and solved for CA and t

Continuously operated PFR (Fin = Fout) • Change = 0 (steady state) = Massin - Massout + Production (or Consumption)

• dCA/dt = FinCAin - Fin(CAin - dCAin) + rA dV = 0

• dCAin/rA = dV/Fin

dCAin/rA = dV/Fin with boundaries of CA0 to CA and 0 to V

• V/Fin = = dCAin/rA Residence time corresponds to area under curve ƒ(CA)

• Note: in continuous PFR and tb in batch STR are synonymous variables; in other words, the change in CA dependent on (kPFR) and t (batch STR) is identical.



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Continuously operated PFR (Fin = Fout) • Change = 0 (steady state) = Massin - Massout + Production (or Consumption)

• dCA/dt = FinCAin - Fin(CAin - dCAin) + rA dV = 0

Reactor concepts of process engineering (5b)


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Abbildung: Operation modes and concentration changes with time and space for different ideal reactor types. STR, dc – discontinuous STR; STR, c – continuous STR; STR cas, c – continuously operated cascade of STRs; TR, c – continuous PFR. STR, stirred tank reactor; TR, tubular reactor, PFR.


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Figure (left): Grafical comparison of cSTR und cTR on the basis of the residence time required to convert a substrate concentration S0 to S1 or S2. Shown are from top to bottom reactions having different dependence of the reaction rate (v; rS) on the substrate concentration. That is, the reactions show different reaction order. Top: 1. order; middle: Michaelis-Menten; bottom: Michaelis-Menten with substrate inhibition. Comparison of productivities is done in diagrams next to it, where the reciprocal rate is plotted against S. The residence times required are for the PFR the integral of the curve (area under the curve) and for the STR the area of the corresponding rectangle, as indicated in the figures. Except for the case of substrate inhibition (reaction order < 0), the PFR always outperforms the STR. Figure (right): Analogous analysis on the basis of the enzyme required (Vmax) in cTR und cSTR. Note the dependence on the substrate concentration.

KMA = 1 mM


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Reaction order• dCA/dt = k CA

1 1. order, k (s-1)

• dCA/dt = k CA0 = k 0. order, k (M s-1)

• dCA/dt = k CA2 2. order, k (M-1 s-1)

• dCA/dt = k CAn n-th order, k (M-n+1 s-1)

Reaction order (after integration)• 1. order: ln(CA/CA0) = k t or CA = CA0 exp (k t)

• 0. order: CA - CA0 = k t or CA = CA0 + k t

• 2. order: CA-1

- CA0-1

= k t or CA = 1/(CA0-1

+ k t)

Half life dependent on reaction order • 1. order: t1/2 = ln2/k (independent of CA0)

• 0. order: t1/2 = CA0/2k

• 2. order: t1/2 = 1/kCA0 (generally: 1/kCA0n-1)


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Reaction order for the Michaelis-Menten relationship• dCA/dt = VmaxCA

/ (KMA+ CA)

For CA0 » KMA we have approximately:

• dCA/dt = Vmax 0. order

For CA0 < KMA we have approximately:

• dCA/dt = VmaxCA / KMA 1. order

Conclusions from kinetic analysis

• cSTR not well suited for enzymes exhibiting high KMA in relation to CA0.

• Inhibition on KMA (competitive inhibition) augments the effect.

• Differences between cSTR and cPFR become large in particular at high

conversion of CA.Have a look to Chmiel (Kapitel 2 und 3) and Doran for refreshment of experimental determination of reaction rates and analysis of enzyme kinetics!


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Comparison of cSTR and cPFR kRKR = X [KMA+ CA0(1 - X)]/[Vmax(1 - X)]

where X is conversion, (CA0 - CA)/CA0.

kRR = (X CA0/Vmax) - (KMA/Vmax) ln(1 - X)Analytical derivations of equations for enzymatic transformations with competitive product inhibition and

substrate inhibition in Chmiel (Kapitel 12)

• Residence time in cPFRkRR = V/FkRR = L/v L is reactor length (m) and v is axial velocity (m s-1)

• Residence time in cPFR filled with solid (carrier) material (reactor porosity) = free reactor volume / total reactor volume = 1 – (total particle volume/total reactor volume)kRR = V/F or kRR = L/vi where vi = F/(V/L)


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1-dimensional dispersion model to describe STR and PFRIn the case of ideal plug flow, velocity of flow (vz) is constant along z-axis of the tubular reactor. It is also constant along the diameter (d) of the tube. Due to molecular diffusion, turbulent convection and wall friction () a parabolical velocity profile is formed and thus deviation from ideal plug flow results. Parameter to measure the combined effects of non-ideal flow is the longitudinal (effective) dispersion coefficient DL (Deff), that is defined as:

DL = f (v, D, d, , , ) where is kinematic viscosity.• The dimensionsless Bodenstein number (Bo) is used to characterize dispersion:

Bo = vz L / DL where L is the characteristic length of the reactor. • Note that:

Bo (iPFR) = ∞Bo (iSTR) = 0

• We have moreover, that for Bo = 7 an intermediate performance between STR and PFR is reached. For Bo ≥ 7 we usually speak of approximately ideal plug flow. • Relationship for the cascade of STRs is, N = 1 + 0.5 (Bo2 + 1)0.5. It can be used to estimate that for N ≥ 5 the cascade approximates the the PFR in performance. N is the number of STRs.


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Bioprocess kinetics

Figure: Strategy for process-kinetic analysis in biotechnology applying the principles of quantification, reduction of complexity, separation of process factors and mathematical modelling. “RDS”, rate-determining step; geschwindigkeitsbestimmender Schritt.Vereinfachung umfasst die Verdichtung der komplexen Strukturen des Prozesses auf die als signifikant erachteten bzw. messbaren Prozessäußerungen, wobei die experimentellen und theoretischen Untersuchungen auf einen erforderlichen Grad an Genauigkeit beschränkt bleiben können.

dCA/dt = kexpCACB

kexp = ƒ(Rk, TR)

kRk = ƒ(CA)


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Figure: Effects of relevant process factors (e.g. the substrate concentration) on the reaction rate in biological conversions. Deff is the effective (microscopic) transport coefficient for multiphase systems.

Kinetics in bioprocesses (formal, phenomenological)


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Kinetics of microbial growthFigure: Characterization of microbial growth showing a time course (upper panel) and the specific growth rates in the chracteristic growth phases (lower panel).

How can one describe microbial growth using (phenomenological) kinetics and relatedly, how can substrate consumption and product formation be described?

What are characteristic parameters of the growth and how do they vary across different organisms? What are physiological implications of changes in growth parameters?

How does analysis of growth kinetics aid in (practical) bioprocess development? Can we calculate the doubling time of an organism? How long does it take to run a reaction (under batch conditions, for example)?


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Requirements of experimental studies in kinetics to characterize microbial physiology• Identification of relevant state variables from the culture parameters• Reduction of complexity• Elimination of physical effects, mostly that of mass transport

Figure: Interplay between the biological system (the cell population) used for biotechnological production and the physical and chemical parameters of the abiotic (external) environment (the bioreactor, the medium).


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Reduction of complexity

Abbildung: Concepts of segregation and structuring in the analysis of microbial kinetics. The simplest view of the microbial cell population is both unstructured and unsegregated.

„durchschnittliche Zelle“, average cell approximation„ausgewogenes Wachstum“, balanced growth approximation


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Reduction of complexity (2)Figure: Simplified view of the metabolism of glucose and/or ethanol in the yeast Saccharomyces cerevisiae. Aerobic and anoxic conversions of glucose are shown. qS and qO2 are specific consumption rates

of glucose and O2. These rates are

dependent on two key enzymes (e), pyruvate dehydrogenase and pyruvate decarboxylase, among others. For aerobic growth, respiration constitutes a kinetic bottleneck (respiratorischer Flaschenhals). This implies that growth and substrate consumption are overall limited at this point. Structured view of the cell: 1) respiratory metabolism;2) glycolysis; 3) „the remaining biomass“; 4) storage carbohydrates.


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Elimination of effects of mass transportFigure: Scheme of three stirred tank reactors explaining the

concepts of transport limitation and absence thereof. A term coined in German is „Pseudohomogenität“ which refers to a system that is heterogeneous in a sense that phase boundaries exist but homogeneous in the sense that mass transport effects are absent.

a) The case of a homogeneous solution lacking phase boundaries (e.g. liquid/solid); mass transport (rTR) occurs

through diffusion and is much faster than reaction (rRk).

Intrinsic kinetic analysis requires that rRk ≤ 0,1 rTR.

Transformations using soluble enzymes are examples.b) The case of „Pseudohomogenität“ where despite

existence of real phase boundaries we still have rRk ≤ 0,1

rTR.

c) The case of a truly heterogeous system where rRk ≥ 0,1

rTR.

Figure. Elimination of effects of mass transport (3) – mixing time in stirred tank reactors. Experimental ste-up for the characterization of the mixing behavior of a stirred tank reactor. A concentrated solution of marker substance (salt, dye, ...) is injected into the reactor. Mixing is monitored using an internal probe measuring conductivity or absorbance, for example. The figure shows a typical response curve from the probe. It enables the determination of circulation time (tc) and terminal mixing time (tm). To obtain tm it is necessary to defined a certain (minimum) degree of mixing („Mischgüte“, 90% as shown here). tc is dependent on how it is stirred and is lowered on stirring faster. Note: tm ≤ 0,1 tRk to fulfill the requirements of „homogeneity“. Also remember: The ideally mixed reactor has a mixing quality of 100% and is mixed infinitely fast.


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Process kinetic analysis• Integral data acquistion: CA = ƒ(t)

• Differential data acquisition: rA = dCA/tThe example of enzyme kinetics Differential rA (initial rate) determined at different values of CA0 (discontinuous or

continuous reaction system; F high, small, z-dim. small)Direct or linearized plotting of dataNon-linear or linear fitting (regression analysis) yields Vmax and KMA

IntegralTime course of CA

1. Graphical differentiation: rA ≈ CA/t und CA ≈ (CA1 - CA2)/2, and proceed as above, or

2. Fit of a suitable equation of the form CA = ƒ(t) to the data Henri equation for Michaelis-Menten equation:

(CA0 - CA)/t = - [KMA ln(CA0/CA)]/t + Vmax

Linearisation gives - KMA as slope and Vmax as ordinate intercept.

institute of biotechnology and biochemical engineering 1 bernd nidetzky vo bioprocess technology 1...

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