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Automatic Control of Bioprocesses

  • Marc Stanke
  • Bernd Hitzmann
Chapter
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 132)

Abstract

In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional–integral–derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.

Graphical Abstract

Keywords

Automation Bioprocess Closed-loop control Fuzzy logic Neural network 

Abbreviations and Nomenclature

ANN

Artificial neural network

CER

Carbon dioxide evolution rate

Cfeed

Substrate concentration in feed flow

CPR

Carbon dioxide production rate

DO

Dissolved oxygen

e(t)

Control deviation at time t

EKF

Extended Kalman filter

FIA

Flow injection analysis

F0(t)

Feeding rate

Fb(t)

Feeding rate due to feedback part

GC

Gas chromatography

HCDC

High-cell-density cultivation

K

PID controller parameter matrix

Kd

PID controller parameter, derivative part

Ki

PID controller parameter, integral part

KLa

Oxygen transfer coefficient

Kp

PID controller parameter, proportional part

LLM

Local linear model

LoLiMoT

Local linear model tree

m

Substrate consumption due to cell maintenance

MIMO

Multiple-input multiple-output

MISO

Multiple-input single-output

MPC

Model predictive controller

NMPC

Nonlinear model predictive controller

ORP

Oxidation–reduction potential

OTR

Oxygen transfer rate

OUR

Oxygen uptake rate

PID

Proportional–integral–derivative

rDO

Specific oxygen consumption rate

rDOXt

Oxygen consumption rate

SISO

Single-input single-output

t

Time

u(t)

Control action at time t

V

Cultivation volume

V0

Initial volume

Vhead

Volume of head space

X

Biomass

x(t),x

Input values (measurement)

X0

Initial biomass

YXS

Yield factor for biomass formation

μ

Specific growth rate

μB(X)

Membership function of a fuzzy logic controller

μsp

Set-point of specific growth rate

τ

Integration variable

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Process Analytics and Cereal Technology, Institute of Food Science and BiotechnologyUniversity of HohenheimStuttgartGermany

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