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Chapter 1. Dynamical Models of Bioreactors. Introduction. The basic dynamics of microbial growth in stirred tank reactors. Extensions to the basic dynamics. Models of the specific growth rate. The reaction scheme of a biotechnological process. General dynamical model of bioreactors. Examples of state space models. A basic structural property of the general dynamical model. Reduction of the general dynamical model. Stability analysis. Extending the general dynamical model. References and bibliography. Chapter 2. Kinetic Modelling, Estimation and Control in Bioreactors: An Overview. Introduction. Difficulties in modelling the reactor kinetics. Minimal modelling of reaction kinetics. Software sensors for bioreactors. Adaptive control of bioreactors. Conclusions and perspectives. References and bibliography. Chapter 3. State and Parameter Estimation with Known Yield coefficients. Introduction. On state observation in bioreactors. Extended Luenberger and Kalman observers. Asymptotic observers for state estimation when the reaction rates are unknown. On-line estimation of reaction rates. References and bibliography. Chapter 4. State and Parameter Estimation with unknown yield coefficients. Introduction. On-line estimation of the specific reaction rates. Joint estimation of yield coefficients and specific reaction rates. Adaptive observers. Estimation of yield coefficients. Other parameter estimation issues in bioreactors. References and bibliography. Chapter 5. Adaptive Control of Bioreactors. Introduction. Principle of linearizing control and remarks on closed loop stability. Singular perturbation design of linearizing controllers. Adaptive linearizing control (known yield coefficients). A general solution to the linearizing control problem for a class of CST bioreactors. Adaptive linearizing control (unknown yield coefficients). Practical aspects of implementation. Case study: Adaptive linearizing control of fed-batch reactors. Case study: Adaptive control of the gaseous production rate of a synthesis product. References and bibliography. Appendix 1. Models of the Specific Growth Rate. Appendix 2. Elements of Stability Theory. Appendix 3. Persistence of excitation. Convergence of Adaptive Estimators. Nomenclature. Index.
This book deals with monitoring and control of biotechnological processes. Different methods are proposed which are based on the nonlinear structure of the process and do not require any a priori knowledge of the fermentation parameters. The theoretical stability and convergence properties of the proposed algorithms are analysed and their performances are illustrated by simulation results and, in many instances, by real life experiments. The concept of software sensors is introduced; these are algorithms based on the nonlinear model of the process and designed for on-line estimation of the biological variables and/or the fermentation parameters. In order to deal with process nonstationarities and parameter uncertainties, reference is made to adaptive estimation and control techniques.
The book is the result of an intensive joint research effort by the authors during the last decade. It is intended as a graduate level text for students of bioengineering as well as a reference text for scientists and engineers involved in the design and optimization of bioprocesses.
- © Elsevier Science 1990
- 3rd July 1990
- Elsevier Science
- eBook ISBN:
@qu:The book is stimulating reading and gives a very attractive introduction to the field of modelling and control in biotechnology. Altogether a book that can be strongly recommended. @source:Chemometrics and Intelligent Laboratory Systems @qu:...one of the main interests of the book is that it is based on a fruitful experience of the authors faced with real problems in various fields of biotechnological activities. ...a significant contribution to the problem of modelling, estimation and adaptive control of bioreactors. It is a highly valuable document... @source:Automatica
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