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Giving an overview of the challenges in the control of bioprocesses, this comprehensive book presents key results in various fields, including: dynamic modeling; dynamic properties of bioprocess models; software sensors designed for the on-line estimation of parameters and state variables; control and supervision of bioprocesses.
Closes the gap between bioscience and mathematics-based process engineering This book presents the most commonly employed approaches in the control of bioprocesses. It discusses the role that control theory plays in understanding the mechanisms of cellular and metabolic processes, and presents key results in various fields such as dynamic modeling, dynamic properties of bioprocess models, software sensors designed for the online estimation of parameters and state variables, and control and supervision of bioprocesses Control in Bioengineering and Bioprocessing: Modeling, Estimation and the Use of Sensors is divided into three sections. Part I, Mathematical preliminaries and overview of the control and monitoring of bioprocess, provides a general overview of the control and monitoring of bioprocesses, and introduces the mathematical framework necessary for the analysis and characterization of bioprocess dynamics. Part II, Observability and control concepts, presents the observability concepts which form the basis of design online estimation algorithms (software sensor) for bioprocesses, and reviews controllability of these concepts, including automatic feedback control systems. Part III, Software sensors and observer-based control schemes for bioprocesses, features six application cases including dynamic behavior of 3-dimensional continuous bioreactors; observability analysis applied to 2D and 3D bioreactors with inhibitory and non-inhibitory models; and regulation of a continuously stirred bioreactor via modeling error compensation. Applicable across all areas of bioprocess engineering, including food and beverages, biofuels and renewable energy, pharmaceuticals and nutraceuticals, fermentation systems, product separation technologies, wastewater and solid-waste treatment technology, and bioremediation Provides a clear explanation of the mass-balance–based mathematical modelling of bioprocesses and the main tools for its dynamic analysis Offers industry-based applications on: myco-diesel for implementing "quality" of observability; developing a virtual sensor based on the Just-In-Time Model to monitor biological control systems; and virtual sensor design for state estimation in a photocatalytic bioreactor for hydrogen production Control in Bioengineering and Bioprocessing is intended as a foundational text for graduate level students in bioengineering, as well as a reference text for researchers, engineers, and other practitioners interested in the field of estimation and control of bioprocesses.
Bioreactors: Animal Cell Culture Control for Bioprocess Engineering presents the design, fabrication, and control of a new type of bioreactor meant especially for animal cell line culture. The new bioreactor, called the "see-saw bioreactor," is ideal for the growth of cells with a sensitive membrane. The see-saw bioreactor derives its name from its principle of operation in which liquid columns in either limb of the reactor alternately go up and down. The working volume of the reactor is small, to within 15 L. However, it can easily be scaled up for large production in volume of cell mass in the drug and pharmaceutical industries. The authors describe the principle of operation of the see-saw bioreactor and how to automatically control the bioprocess. They discuss different control strategies as well as the thorough experimental research they conducted on this prototype bioreactor in which they applied a time delay control for yield maximization. To give you a complete understanding of the design and development of the see-saw bioreactor, the authors cover the mathematical model they use to describe the kinetics of fermentation, the genetic algorithms used for deriving the optimal time trajectories of the bioprocess variables, and the corresponding control inputs for maximizing the product yield. One chapter is devoted to the application of time delay control. Following a description of the bioreactor’s working setup in the laboratory, the authors sum up their investigation and define the future scope of work in terms of design, control, and software sensors.
Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy, by Bernhard Sonnleitner Automatic Control of Bioprocesses, by Marc Stanke, Bernd Hitzmann An Advanced Monitoring Platform for Rational Design of Recombinant Processes, by G. Striedner, K. Bayer Modelling Approaches for Bio-Manufacturing Operations, by Sunil Chhatre Extreme Scale-Down Approaches for Rapid Chromatography Column Design and Scale-Up During Bioprocess Development, by Sunil Chhatre Applying Mechanistic Models in Bioprocess Development, by Rita Lencastre Fernandes, Vijaya Krishna Bodla, Magnus Carlquist, Anna-Lena Heins, Anna Eliasson Lantz, Gürkan Sin and Krist V. Gernaey Multivariate Data Analysis for Advancing the Interpretation of Bioprocess Measurement and Monitoring Data, by Jarka Glassey Design of Pathway-Level Bioprocess Monitoring and Control Strategies Supported by Metabolic Networks, by Inês A. Isidro, Ana R. Ferreira, João J. Clemente, António E. Cunha, João M. L. Dias, Rui Oliveira Knowledge Management and Process Monitoring of Pharmaceutical Processes in the Quality by Design Paradigm, by Anurag S Rathore, Anshuman Bansal, Jaspinder Hans The Choice of Suitable Online Analytical Techniques and Data Processing for Monitoring of Bioprocesses, by Ian Marison, Siobhán Hennessy, Róisín Foley, Moira Schuler, Senthilkumar Sivaprakasam, Brian Freeland
This is the first comprehensive volume on bioprocess automation and control. It addresses bioprocess engineers and biotechnologists seeking information about new devices and advanced control techniques, including those who are not be specialists in process control. It also offers guidance for control engineers who are used to classical problems in mechanical, electrical, or chemical engineering but who may not be familiar with the specifics of nonlinear, time-dependent bioprocesses and the instrumentation required to monitor them. The book begins with the development and analysis of control structures and describes available biosensors. Underlying soft sensor estimation techniques are outlined along with methods for using derived information. The coverage of industrial applications treats both low-level control loops (temperature, pH, etc.) and high-level control strategies (setpoint optimization, optimal trajectories, adaptive control, etc.). The international team of authors detail each topic in a thorough and complete manner, and provide an important source of information for both experienced users and those new to computer controlled fermentation systems. The intended readership includes chemical, control and bioprocess engineers; biochemists and biologists; and graduate students in biotechnology.
Closes the gap between bioscience and mathematics-based process engineering This book presents the most commonly employed approaches in the control of bioprocesses. It discusses the role that control theory plays in understanding the mechanisms of cellular and metabolic processes, and presents key results in various fields such as dynamic modeling, dynamic properties of bioprocess models, software sensors designed for the online estimation of parameters and state variables, and control and supervision of bioprocesses Control in Bioengineering and Bioprocessing: Modeling, Estimation and the Use of Sensors is divided into three sections. Part I, Mathematical preliminaries and overview of the control and monitoring of bioprocess, provides a general overview of the control and monitoring of bioprocesses, and introduces the mathematical framework necessary for the analysis and characterization of bioprocess dynamics. Part II, Observability and control concepts, presents the observability concepts which form the basis of design online estimation algorithms (software sensor) for bioprocesses, and reviews controllability of these concepts, including automatic feedback control systems. Part III, Software sensors and observer-based control schemes for bioprocesses, features six application cases including dynamic behavior of 3-dimensional continuous bioreactors; observability analysis applied to 2D and 3D bioreactors with inhibitory and non-inhibitory models; and regulation of a continuously stirred bioreactor via modeling error compensation. Applicable across all areas of bioprocess engineering, including food and beverages, biofuels and renewable energy, pharmaceuticals and nutraceuticals, fermentation systems, product separation technologies, wastewater and solid-waste treatment technology, and bioremediation Provides a clear explanation of the mass-balance–based mathematical modelling of bioprocesses and the main tools for its dynamic analysis Offers industry-based applications on: myco-diesel for implementing "quality" of observability; developing a virtual sensor based on the Just-In-Time Model to monitor biological control systems; and virtual sensor design for state estimation in a photocatalytic bioreactor for hydrogen production Control in Bioengineering and Bioprocessing is intended as a foundational text for graduate level students in bioengineering, as well as a reference text for researchers, engineers, and other practitioners interested in the field of estimation and control of bioprocesses.
Giving an overview of the challenges in the control of bioprocesses, this comprehensive book presents key results in various fields, including: dynamic modeling; dynamic properties of bioprocess models; software sensors designed for the on-line estimation of parameters and state variables; control and supervision of bioprocesses.
Models offer benefits even before they are put on line. Based on years of experience, the authors reveal in New Directions in Bioprocess Modeling and Control that significant improvements can result from the process knowledge and insight that are gained when building experimental and first-principle models for process monitoring and control. Doing modeling in the process development and early commercialization phases is advantageous because it increases process efficiency and provides ongoing opportunities for improving process control. This technology is important for maximizing benefits from analyzers and control tool investments. If you are a process design, quality control, information systems, or automation engineer in the biopharmaceutical, brewing, or bio-fuel industry, this handy resource will help you define, develop, and apply a virtual plant, model predictive control, first-principle models, neural networks, and multivariate statistical process control. The synergistic knowledge discovery on bench top or pilot plant scale can be ported to industrial scale processes. This learning process is consistent with the intent in the Process Analyzer and Process Control Tools sections of the FDA_s Guidance for Industry PAT _ A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. It states in the Process Analyzer section of the FDA_s guidance: _For certain applications, sensor-based measurements can provide a useful process signature that may be related to the underlying process steps or transformations. Based on the level of process understanding these signatures may also be useful for the process monitoring, control, and end point determination when these patterns or signatures relate to product and process quality._
A comprehensive, contributed work concerned with bioprocess automation and control. Commences with generation and analysis of control structure, emphasizing biosensors with a penetrating look at available devices. Profiles underlying soft sensor estimation techniques as well as those for use in derived information. Illustrated applications treat either low level control loops or high level control strategies. Introduces new trends in process automation such as expert systems and neural networks, discussing their future in biotechnology.
Bioprocess engineering employs microorganisms to produce biological products for medical and industrial applications. The book covers engineering tasks around- the cultivation process in bioreactors including media design, feeding strategies, or cell harvesting. All aspects are described from the conceptual considerations to technical realization and provide insight by detailed explanations, drawings, formulas, and example processes.