Automatic Control of Bioprocesses - Measurement, Monitoring, Modelling, and Control of Bioprocesses - page 35

Automatic Control of Bioprocesses

Marc Stanke and Bernd Hitzmann

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 simula-
tions. 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) con-
troller. 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 pre-
dictive 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 meth-
odology. 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 mea-
surement system.

Keywords Automation Bioprocess Closed-loop control Fuzzy logic
Neural network

Abbreviations and Nomenclature
ANN Artificial neural network

CER Carbon dioxide evolution rate

C feed Substrate concentration in feed flow

CPR Carbon dioxide production rate

DO Dissolved oxygen

e(t) Control deviation at time t

M. Stanke B. Hitzmann ( )

Process Analytics and Cereal Technology, Institute of Food Science and Biotechnology,
University of Hohenheim, Garbenstr. 23, 70599 Stuttgart, Germany
e-mail: [e-mail]

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