Modeling and Control of Dynamic Spatially Distributed Systems

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. Zhong Yang obtained his PhD in engineering from Nanjing University of Aeronautics and Astronautics in 1996 and did a 2-year research postdoc at Southeast University in Automatic Control until 1997. He is the chief investigator of several academic and teaching reform research projects funded by the National Natural Science Foundation of China (NSFC) and the Natural Science Foundation of Jiangsu Province. He has also received several highly recognized prizes, including the Second Prize of Science and Technology in China Machinery Industry. Currently, Prof. Yang serves as the dean of the College of Intelligent Science and Control Engineering, Jinling Institute of Technology, as well as the research degree supervisor. His research interests now focus on intelligence science, control engineering, solar energy photovoltaic generation, and optical scattering.

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