Modelling and Control of Dynamic Spatially Distributed Systems Pharmaceutical Processes Emerging Methodologies and Applications in Modelling, Identification and Control Series

Langue : Anglais

Auteurs : Wang Yizhi, Yang Wei-Zhong

Couverture de l’ouvrage Modelling and Control of Dynamic Spatially Distributed Systems

Résumé
Sommaire
Biographie
Commentaire

Modelling and Control of Dynamic Spatially Distributed Systems: Pharmaceutical Processes provides a balanced approach to help readers to get started quickly in the field of biochemical pharmaceuticals. From a theoretical perspective, dynamic spatially distributed systems are introduced to address their industrial applications. After identifying problems, the book provides readers with modeling and control system design techniques via a novel fuzzy set (class of objects with a continuum of grades of membership, to describe the grade of the object belonging to this fuzzy set) and intelligent computation methods. From an application perspective, the book provides a thorough understanding of Good Manufacture Practices (GMP) and the importance of identification, modelling, and intelligent control of such systems, reducing the test-and-error cost, and the R&D design time cycle of original drug development.

PART 1 Background 1. Dynamic Spatially Distributed Systems: Pharmaceutical Processes 2. Fundamentals of Fuzzy Control 3. Pharmaceutical Engineering And Processes PART 2 Modeling of Dynamic spatially Distributed systems 4. Introduction to Depyrogenation Tunnel 5. Conventional Methods in Modelling and Simulation of Depyrogenation Tunnel 6. Quasi-Guassian Fuzzy Sets and Approximation 7. Quasi-Guassian Fuzzy Systems and Applications PART 3 Intelligent Control Methods Of Dynamic Spatially Distributed Systems 8. General Control methods of Depyrogenation Tunnel 9. Hierarchical Fuzzy Control Of Depyrogenation Tunnel 10. Conclusions and Future Prospects

Yizhi Wang received her Ph.D. degree in Control Engineering from the University of West of England, received her M.Sc. degree in System Engineering from the University of South Australia and a B.Sc. degree in Chemical Engineering from Nanjing Forestry University. Yizhi served as a lecturer since 2018 in Discipline of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology. She is also served as an expert advisor of Nanjing Leechdom Biopharm Co. Ltd in pharmaceutical process and control area since 2019. Yizhi’s current research interests include Generative Adversarial Networks (GAN), advanced fuzzy theory and their applications in manufacturing systems and control. Yizhi has published about 10 refereed papers included in the Engineering Index (EI) and the Science Citation Index (SCI), and served as program committee of International Conference of Modelling, Identification and Control (IJMIC).
Dr. Wang also served as Project Assistant in Nanjing Kingfriend Biochemical Pharmaceutical Co.Ltd (http://www.nkf-pharma.com/en/index.asp, which is a listed company now) from 2011 to 2012, involved in whole generic drug (Heparin Sodium Injection) development. During that period, she acquired a thorough understanding of key index of pharmaceutical equipment and Chinese Pharmacopoeia and their compliance. Now she is undertaking three relevant research projects and promoting UIC (University-Industry Cooperation) research. Therefore, from practical perspective, she will provide real-life industrial solutions with pharmaceutical background.
Professor Zhong YANG obtained his Ph.D in Engineering in Nanjing University of Aeronautics and Astronautics in 1996 and did a 2-year research postdoc at Southeast University in Automatic Control until 1997. He has published over 100 refereed research papers indexed in Science Citation Index (SCI) and Engineering Index (EI). He has also contributed to over 100 patents including 5 PCT patents. He is

Provides an updated and supplement knowledge to the body of distributed parameter systems
Provides control and analysis framework based on state-space approach for a non-standard model from industrial complex systems
Presents a novel proposed fuzzy set and applied it to case studies to illustrate its feasibility
Presents a control system design solution from perspective of medicine production particularly