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Bioprocess and Biosystems Engineering

, Volume 27, Issue 4, pp 255–262 | Cite as

Estimation of optimal feeding strategies for fed-batch bioprocesses

  • Ezequiel Franco-Lara
  • Dirk Weuster-Botz
Original papers

Abstract

A generic methodology for feeding strategy optimization is presented. This approach uses a genetic algorithm to search for optimal feeding profiles represented by means of artificial neural networks (ANN). Exemplified on a fed-batch hybridoma cell cultivation, the approach has proven to be able to cope with complex optimization tasks handling intricate constraints and objective functions. Furthermore, the performance of the method is compared with other previously reported standard techniques like: (1) optimal control theory, (2) first order conjugate gradient, (3) dynamical programming, (4) extended evolutionary strategies. The methodology presents no restrictions concerning the number or complexity of the state variables and therefore constitutes a remarkable alternative for process development and optimization.

Keywords

Feeding strategy Optimization Genetic algorithm Neural network 

Acronyms

ANN

Artificial neural network

DP

Dynamical programming

ES

Evolutionary strategy

FOCG

First order conjugate gradient

GA

Genetic algorithm

IDP+SQP

Iterative dynamical programming + sequential quadratic programming

NNMO

Neural network model optimization

OCT

Optimal control theory

List of Symbols

Amm

Ammonia concentration (mM)

F1

Volumetric feed rate of glucose (dm3 day−1)

F2

Volumetric feed rate of glutamine (dm3 day−1)

Glc

Glucose concentration (mM)

Glcin

Glucose concentration in the feed stream (mM)

Gln

Glutamine concentration (mM)

Glnin

Glutamine concentration in the feed stream (mM)

J

Optimization goal (mg Mab)

kd

First order death rate (day−1)

kμ

Kinetic constant (day−1)

Lac

Lactate concentration (mM)

Mab

Concentration of monoclonal antibodies (mM)

qglc

Specific use rate of glucose (mM cells−1 day−1)

qgln

Specific use rate of glutamine (mM cells−1 day−1)

qamm

Specific production rate of ammonia (mM cells−1 day−1)

qlac

Specific production rate of lactate (mM cells−1 day−1)

qMab

Specific production rate of monoclonal antibodies (mg cells−1 day−1)

t

Time (day)

tf

Time at the end of fermentation (day)

V

Culture reaction volume (dm3)

Vmax

Maximal culture reaction volume (dm3)

Xv

Concentration of viable cells (cells cm−3)

Yxv/glc

Cell yield coefficient for glucose (cells mM−1)

Yxv/gln

Cell yield coefficient for glutamine (cells mM−1)

Ylac/glc

Yield coefficient lactate/glucose (mM mM−1)

Yamm/gln

Yield coefficient ammonia/glutamine (mM mM−1)

Greek letters

α0

Maximal specific Mab production rate (mg cells−1 d−1)

β

Kinetic constant (mg cells−1 d−1)

μ

First order growth rate (d−1)

Parameters

μmax

1.09 day−1

kdmax

0.69 day−1

Yxv/glc

1.09·10−8 cells mM−1

Yxv/gln

3.8·10−8 cells mM−1

mglc

0.17 mM·10−8 cells−1 day−1

kmglc

19.0 mM

kglc

1.0 mM

kgln

0.3 mM

α0

2.57 mg ·10−8 cells−1 day−1

kμ

0.02 day−1

β

0.35 mg ·10−8 cells−1 day−1

kdlac

0.01 day−1 mM−1

kdamm

0.06 day−1 mM−1

kdgln

0.02 mM

Ylac/glc

1.8 mM mM−1

Yamm/gln

0.85 mM mM−1

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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Ezequiel Franco-Lara
    • 1
  • Dirk Weuster-Botz
    • 1
  1. 1.Biochemical EngineeringMunich University of TechnologyGarchingGermany

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