Modeling and Control of Dynamic Spatially Distributed Systems

Modeling 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.

Yizhi Wang received her Ph.D. degree in Control Engineering from the University of the 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 an Associated Professor since 2024.8 and lecturer since 2018.7 in Discipline of Automation, College of Intelligent Science and Control Engineering, Jinling Institute of Technology and also a Post Doc. Fellow at Nanjing Agricultural University. She has served as Technical Vice President of Nanjing Leechdom Biopharm Co. Ltd in pharmaceutical process control and optimization since 2019.2. Her current research interest includes advanced fuzzy theory in evaluation, modeling, identification and control of intelligent manufacturing systems and unmanned agricultural equipment 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