Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

This research book presents the use of computational intelligence paradigms in bioprocess related tasks namely modeling, supervision, monitoring and control, diagnostic, learning and optimization, with applications to several areas. Aimed at researchers, practitioners and graduate students, it will also serve as a text for advanced courses in chemical engineering, bioinformatics and biotechnology, and for computer scientists interested in bioprocesses. Chapters are self-contained and many of them include applications to real-world problems: - Computational Intelligence Techniques as Tools for Bioprocess Modelling, Optimization, Supervision and Control; - Software Sensors and their Applications in Bioprocess; - Monitoring of Bioprocesses: Mechanistic and Data-driven Approaches; - Novel Computational Methods for Modeling and Control in Chemical and Biochemical Process Systems; - Computational Intelligence Techniques for Supervision and Diagnosis of Biological Wastewater Treatment Systems; - Multiobjective Genetic Algorithms for the Optimisation of Wastewater Treatment Processes; - Data Reconciliation using Neural Networks for the Determination of KLa; - A Computational Intelligent Based Approach for the Development of a Minimal Defined Medium: Application to Human Interleukin-3 Production by treptomyces lividans 66; - Bioprocess Modelling for Learning Model Predictive Control (L-MPC); - Performance Monitoring and Batch to Batch Control of Biotechnological Processes; - Modelling of Biotechnological Processes - an Approach based on Artificial Neural Networks.

Klappentext

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.

Inhalt

Computational Intelligence Techniques as Tools for Bioprocess Modelling, Optimization, Supervision and Control.- Software Sensors and Their Applications in Bioprocess.- Monitoring of Bioprocesses: Mechanistic and Data-Driven Approaches.- Novel Computational Methods for Modeling and Control in Chemical and Biochemical Process Systems.- Computational Intelligence Techniques for Supervision and Diagnosis of Biological Wastewater Treatment Systems.- Multiobjective Genetic Algorithms for the Optimisation of Wastewater Treatment Processes.- Data Reconciliation Using Neural Networks for the Determination of KLa.- A Computational Intelligent Based Approach for the Development of a Minimal Defined Medium: Application to Human Interleukin-3 Production by Streptomyces lividans 66.- Bioprocess Modelling for Learning Model Predictive Control (L-MPC).- Performance Monitoring and Batch to Batch Control of Biotechnological Processes.- Modelling of Biotechnological Processes - An Approach Based on Artificial Neural Networks.