Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

1. Front Matter

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2. Computational Intelligence Techniques as Tools for Bioprocess Modelling, Optimization, Supervision and Control

   • M. C. Nicoletti, L. C. Jain, R. C. Giordano

   Pages 1-23

3. Software Sensors and Their Applications in Bioprocess

   • Hongwei Zhang

   Pages 25-56

4. Monitoring of Bioprocesses: Mechanistic and Data-Driven Approaches

   • Laurent Dewasme, Philippe Bogaerts, Alain Vande Wouwer

   Pages 57-97

5. Novel Computational Methods for Modeling and Control in Chemical and Biochemical Process Systems

   • Petia Georgieva, Sebastião Feyo de Azevedo

   Pages 99-125

6. Computational Intelligence Techniques for Supervision and Diagnosis of Biological Wastewater Treatment Systems

   • Ana M. A. Dias, Eugénio C. Ferreira

   Pages 127-162

7. Multiobjective Genetic Algorithms for the Optimisation of Wastewater Treatment Processes

   • Benoît Beraud, Cyrille Lemoine, Jean-Philippe Steyer

   Pages 163-195

8. Data Reconciliation Using Neural Networks for the Determination of KLa

   • Nilesh Patel, Jules Thibault

   Pages 197-214

9. A Computational Intelligent Based Approach for the Development of a Minimal Defined Medium: Application to Human Interleukin-3 Production by Streptomyces lividans 66

   • Keyvan Nowruzi, Ali Elkamel, Jeno M. Scharer, Murray Moo-Young

   Pages 215-236

10. Bioprocess Modelling for Learning Model Predictive Control (L-MPC)

    • María Antonieta Alvarez, Stuart M. Stocks, S. Bay Jørgensen

    Pages 237-280

11. Performance Monitoring and Batch to Batch Control of Biotechnological Processes

    • Julian Morris, Jie Zhang

    Pages 281-310

12. Modelling of Biotechnological Processes – An Approach Based on Artificial Neural Networks

    • Eduardo Valente, Miguel Rocha, Eugénio C. Ferreira, Isabel Rocha

    Pages 311-332

13. Back Matter

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Computational Intelligence (CI) and Bioprocess are well-established research areas which have much to offer each other. Under the perspective of the CI area, Biop- 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 s- 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.

• Monitor
• algorithm
• algorithms
• artificial neural network
• computational intelligence
• control
• genetic algorithms
• intelligence
• learning
• linear optimization
• model
• modeling
• neural networks
• optimization
• problem solving

• CS Department, Universidade Federal de S. Carlos, S. Carlos, Brazil

  Maria Carmo Nicoletti

• School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia

  Lakhmi C. Jain

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