Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

348 Pages·2009·11.151 MB·English

by  M. C. Nicoletti, L. C. Jain, R. C. Giordano (auth.), Maria do Carmo Nicoletti, Lakhmi C. Jain (eds.)

MariadoCarmoNicolettiandLakhmiC.Jain(Eds.)
ComputationalIntelligenceTechniquesforBioprocessModelling,Supervision
andControl
StudiesinComputationalIntelligence,Volume218
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Maria do Carmo Nicoletti and Lakhmi C.Jain (Eds.)
Computational Intelligence
Techniques for Bioprocess
Modelling, Supervision and
Control
123
Prof.Dr.MariadoCarmoNicoletti
CSDepartment
UniversidadeFederaldeS.Carlos
Rod.WashingtonLuiz,km235
CaixaPostal676
13565-905S.Carlos-SP
Brazil
E-mail:[email protected]
Prof.Dr.LakhmiJain
PhD,ME,BE(Hons),FellowIE(Aust)
ProfessorofKnowledge-BasedEngineering
SchoolofElectricalandInformationEngineering
UniversityofSouthAustralia
Adelaide
MawsonLakesCampusSA5095
Australia
E-mail:[email protected]
ISBN 978-3-642-01887-9 e-ISBN978-3-642-01888-6
DOI 10.1007/978-3-642-01888-6
Studiesin Computational Intelligence ISSN1860949X
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Preface

Computational Intelligence (CI) and Bioprocess are well-established research areas
which have much to offer each other. Under the perspective of the CI area, Biopro-
cess can be considered a vast application area with a growing number of complex and
challenging tasks to be dealt with, whose solutions can contribute to boosting the
development of new intelligent techniques as well as to help the refinement and spe-
cialization of many of the already existing techniques. Under the perspective of the
Bioprocess area, CI can be considered a useful repertoire of theories, methods and
techniques that can contribute and offer interesting alternative approaches for solving
many of its problems, particularly those hard to solve using conventional techniques.
Although throughout the past years CI and Bioprocess areas have accumulated
substantial specific knowledge and progress has been quick and with a high degree of
success, we believe there is still a long way to go in order to use the potentialities of
the available CI techniques and knowledge at their full extent, as tools for supporting
problem solving in bioprocesses. One of the reasons is the fact that both areas have
progressed steadily and have been continuously accumulating and refining specific
knowledge; another reason is the high level of technical expertise demanded by each
of them.
The acquisition of technical skills, experience and good insights in either of the
two areas is very demanding and a hard task to be accomplished by any professional.
As often happens with interdisciplinary areas, it is difficult to find experts in one of
the two areas willing to get deeply involved in the other area, to the point of learning
its specialized vocabulary, becoming familiar with its terminology, acquiring suffi-
cient technical language to master communication as well as to understand its main
concepts and many of its specialized procedures, so to become autonomous and
knowledgeable in both and be able to devise efficient CI methods customised to bio-
processes. Fortunately, in spite of their own technical vocabulary and language, both
areas share a mathematical language, which can help to bridge the gap between the
specialized technical languages employed by each of them.
We believe that without a deep understanding of the problem, a good insight into
choosing the technique most suitable for solving the problem and a very good com-
mand of the chosen technique, its power and limitations, we will rarely find a reliable,
appropriate and satisfactory solution to the problem at hand.
The eleven chapters in this book as well as the Appendix intend to help those inter-
ested in both CI techniques and Bioprocess become familiarized with the vocabulary,
technical language and some of the main techniques and problems involved in both
areas. The book covers the use of CI techniques in bioprocess related tasks namely
modeling, supervision, monitoring and control, diagnostic, learning and optimization,
with applications in several areas. Aimed at researchers, practitioners and graduate
students, it may serve as a text for advanced courses in chemical engineering, bioin-
formatics and biotechnology and for computer scientists interested in bioprocesses.
Chapters are self-contained and many of them, besides their focus on theoretical
VI Preface
foundations, also include applications to real-world problems; they are briefly de-
scribed next.
(1) Computational Intelligence Techniques as Tools for Bioprocess Modelling,
Optimization, Supervision and Control
Presents an overview of recent and relevant works related to the use of CI
techniques in Bioprocesses.
(2) Software Sensors and their Applications in Bioprocess
Clearly describes the motivations, design, implementation and use of software sen-
sors and inferential estimation in bioprocesses, typically in fermentations. Research
work on software sensor is reviewed and the associated techniques are introduced
with examples and case studies.
(3) Monitoring of Bioprocesses: Mechanistic and Data-driven Approaches
Some state estimation techniques are theoretically discussed under two approaches,
mechanistic and data-driven and real-world applications are presented.
(4) Novel Computational Methods for Modeling and Control in Chemical and Bio-
chemical Process Systems
The chapter focuses on developing more efficient computational schemes for the
modeling and control of chemical and biochemical processes. Artificial neural net-
works are introduced and successfully used on a couple of benchmark problems.
(5) Computational Intelligence Techniques for Supervision and Diagnosis of
Biological Wastewater Treatment Systems
Surveys artificial intelligence as well as statistics techniques used for monitoring
and controlling wastewater treatment systems. The chapter covers knowledge based
systems, fuzzy logic, neural networks and multivariate statistical methods.
(6) Multiobjective Genetic Algorithms for the Optimization of Wastewater Treat-
ment Processes
This chapter presents a methodology for combining multiobjective genetic algo-
rithms with wastewater treatment plant (WWTP) models for the evaluation, optimiza-
tion and comparison of WWTP control laws. The use of the methodology on a case
study is described.
(7) Data Reconciliation Using Neural Networks for the Determination of K a
L
The problem of estimating the oxygen mass transfer coefficient (K a) in aerobic
L
fermentation using data reconciliation is described in the chapter. Data reconciliation
is implemented in two different ways: one by minimizing an objective function that
takes into account measurements and four estimation methods, and the other by using
a previously trained NN.
(8) A Computational Intelligent Based Approach for the Development of a Mini-
mal Defined Medium Application to Human Interleukin-3 Production by Streptomy-
ces lividans 66
The chapter describes an elaborated combination of different techniques aiming at
identifying the composition of the optimum minimal medium for the production of
rHuIL-3. The use of NN in combination with statistical techniques is part of the
process.
(9) Bioprocess Modelling for Learning Model Predictive Control (L-MPC)
The chapter describes a data-driven modeling methodology for batch and fed batch
processes. It also describes how developed models can be used for process monitor-
ing, for ensuring process reproducibility through control and for optimizing process
Preface VII
performance by enforcing learning from previous runs through a control methodology
named Learning Model Predictive Control (L-MPC).
(10) Performance Monitoring and Batch to Batch Control of Biotechnological
Processes
This chapter describes two approaches to ensuring the production quality of batch
biotechnological processes.
(11) Modelling of Biotechnological Processes - An Approach Based on Artificial
Neural Networks
The chapter describes a software tool named FerMoANN, suitable for modeling
fermentation processes. The use of the tool in two fermentation processes is presented
and discussed.

We wish to express our gratitude to the authors and reviewers for their contribu-
tion. We are also grateful to the editorial team of Springer-Verlag and SCI Data Proc-
essing Team for their assistance during the preparation of the manuscript.
M.C. Nicoletti wishes to extend her thanks to Leonie C. Pearson for kindly reviewing
many of her writings.

M.C. Nicoletti, Brazil
L.C. Jain, Australia
Contents
1 Computational Intelligence Techniques as Tools for
Bioprocess Modelling, Optimization, Supervision and
Control..................................................... 1
M.C. Nicoletti, L.C. Jain, R.C. Giordano
2 Software Sensors and Their Applications in
Bioprocess.................................................. 25
Hongwei Zhang
3 Monitoring of Bioprocesses: Mechanistic and Data-Driven
Approaches................................................. 57
Laurent Dewasme, Philippe Bogaerts, Alain Vande Wouwer
4 Novel Computational Methods for Modeling and
Control in Chemical and Biochemical Process
Systems .................................................... 99
Petia Georgieva, Sebastia˜o Feyo de Azevedo
5 Computational Intelligence Techniques for Supervision
and Diagnosis of Biological Wastewater Treatment Systems. 127
Ana M.A. Dias, Eug´enio C. Ferreira
6 Multiobjective Genetic Algorithms for the Optimisation
of Wastewater Treatment Processes ........................ 163
Benoˆıt Beraud, Cyrille Lemoine, Jean-Philippe Steyer
7 Data Reconciliation Using Neural
Networks for the Determination of
K a ........................................................ 197
L
Nilesh Patel, Jules Thibault
X Contents
8 A Computational Intelligent Based Approach for the
Development of a Minimal Defined Medium: Application
to Human Interleukin-3 Production by Streptomyces
lividans 66 ................................................. 215
Keyvan Nowruzi, Ali Elkamel, Jeno M. Scharer,
Murray Moo-Young
9 Bioprocess Modelling for Learning Model Predictive
Control (L-MPC)........................................... 237
Mar´ıa Antonieta Alvarez, Stuart M. Stocks, S. Bay Jørgensen
10 Performance Monitoring and Batch to Batch Control of
Biotechnological Processes.................................. 281
Julian Morris, Jie Zhang
11 Modelling of Biotechnological Processes – An Approach
Based on Artificial Neural Networks........................ 311
Eduardo Valente, Miguel Rocha, Eug´enio C. Ferreira, Isabel Rocha
Appendix....................................................... 333
Author Index................................................... 343
1
Computational Intelligence Techniques as Tools
for Bioprocess Modelling, Optimization, Supervision
and Control
M.C. Nicoletti1, L.C. Jain2, and R.C. Giordano3
1 Computer Science Dept.,
3 Chemical Engineering Dept.,
Universidade Federal de S. Carlos −UFSCar
Rod. Washington Luiz, km 235
Caixa Postal 676
13565-905 S. Carlos – SP, Brazil
[email protected], [email protected]
2 School of Electrical and Information Engineering
University of South Australia
Adelaide, Australia
[email protected]
Abstract. This is an introductory chapter that presents a general review of some
Computational Intelligence (CI) techniques used today, both in the biotechnol-
ogy industry and in academic research. Various applications in bioprocess-
related tasks are presented and discussed. The aim of putting forth a surveying
view of the main tendencies in this field is to provide a broad panorama of the
research in the intersection between the two areas, to highlight the popularity of
a few CI techniques in Bioprocess applications and to discuss the potential
benefits that other not so explored CI techniques could offer.
1 Introduction
For the purpose of this introductory chapter we are calling Computational Intelligence
(CI) the research area whose main focus is the investigation and use of techniques
considered intelligent that can be automated by means of a computer program/system.
They typically refer to search/optimization algorithms in general (including genetic
algorithms, simulated annealing, particle swarm, etc.), neural networks, fuzzy logic
and fuzzy inference, symbolic learning algorithms, clustering, feature subset selection
and neuro-fuzzy systems. Although not conventionally considered computational intel-
ligence techniques, many statistical methods are closely related to CI techniques and,
as such, a few of them are dealt with in this book. It is also common to come across
hybrid systems that implement a conveniently balanced combination of different CI (or
CI/statistical) techniques aiming to compensate each other’s drawbacks and whose
design and development can be also considered as one of the goals of CI (see [1]).
Bioprocess modelling, optimization, supervision and control may be classified as a
branch of Chemical (or, more recently, Biological or Bioprocess) Engineering. These
M. do Carmo Nicoletti, L.C. Jain (Eds.): Comp. Intel. Tech. for Bio. Mod., SCI 218, pp. 1–23.
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