SUSTAINABLE DEVELOPMENT
Innovate. Sustain. Transform.
Save up to 30% on top Physical Sciences & Engineering titles!
Modeling and Control of Dynamic Spatially Distributed Systems
Pharmaceutical Processes
- 1st Edition - November 8, 2024
- Authors: Yizhi Wang, Zhong Yang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 3 9 2 - 4
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 3 9 1 - 7
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 bioche… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteModeling 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.
- Provides an updated, supplemental knowledge to the body of distributed parameter systems
- Covers control and analysis framework based on a state-space approach for a non-standard model from industrial complex systems
- Presents a novel proposed fuzzy set and applies it to case studies to illustrate its feasibility
- Includes a control system design solution from perspective of medicine production
Graduate students of control engineering, automatic control, industrial engineering, Professional engineers working on pharmaceutical processes, especially quality control engineers and manufacture production engineers
- Modeling and Control of Dynamic Spatially Distributed Systems
- Cover image
- Title page
- Table of Contents
- Copyright
- About the authors
- Preface
- Background part
- Modeling and identification of dynamic spatially distributed systems part
- Control system design and analysis for dynamic spatially distributed systems part
- Part 1: Background
- Chapter 1 Dynamic spatially distributed systems: An overview
- Abstract
- Keywords
- 1.1 Introduction
- 1.2 Definition
- 1.3 Overview of the identification and modeling methods for DSDS
- 1.3.1 Parameter identification and system identification
- 1.3.2 Overview of modeling methods
- 1.4 Overview of parameterization methods for DSDS
- 1.4.1 Data fusion technology
- 1.4.2 Multisource data fusion
- 1.4.3 Processing methods for unstructured data
- 1.5 Control system design methods for DSDS
- 1.5.1 Traditional methods
- 1.5.2 Intelligent control methods
- 1.5.3 Intelligent optimization algorithms and applications
- 1.6 Opportunities and challenges
- References
- Chapter 2 Fundamental of fuzzy control theory
- Abstract
- Keywords
- 2.1 Overview
- 2.2 Fuzzy logic
- 2.2.1 Definition of fuzzy sets
- 2.2.2 Membership functions
- 2.2.3 Up-to-date fuzzy sets
- 2.3 Methods for determining membership functions
- 2.4 Important theorems of fuzzy sets
- 2.5 Fuzzy relationship and inference
- 2.5.1 Fuzzy relationship
- 2.5.2 Fuzzy relationship composition
- 2.5.3 Fuzzy inference
- 2.5.4 Defuzzifiction
- 2.6 Typical fuzzy logic systems
- 2.6.1 Basic structure of fuzzy logic system
- 2.6.2 Typical fuzzy logic systems
- 2.7 Type-2 fuzzy control system
- References
- Chapter 3 Pharmaceutical engineering and processes
- Abstract
- Keywords
- 3.1 QbD and GMP
- 3.1.1 Others
- 3.2 Pharmaceutical equipment and its automation
- 3.2.1 Typical production processes of common drugs
- 3.2.2 Drying process and equipment
- 3.2.3 Status quo of continuous production in pharmaceutical process and implementation difficulties
- 3.3 Guidance of QbD on the design and verification of pharmaceutical equipment
- 3.4 Conclusion
- References
- Part 2: Modeling of dynamic spatially distributed systems
- Chapter 4 Introduction to the depyrogenation tunnel
- Abstract
- Keywords
- 4.1 Qualification and validation
- 4.1.1 Validation
- 4.1.2 Qualification
- 4.2 Development of dry heat sterilization process and decision tree
- 4.3 Brief introduction to depyrogenation tunnel
- 4.4 Establishment of a mechanism model of depyrogenation tunnel
- 4.4.1 State space method
- 4.4.2 State space modeling
- References
- Chapter 5 Conventional methods in modeling and simulation of depyrogenation tunnel
- Abstract
- Keywords
- 5.1 Introduction
- 5.2 Literature review
- 5.2.1 Temperature field of sterilization process simulation study
- 5.2.2 Study on airflow uniformity
- 5.2.3 Analysis and research on energy consumption of the heating process
- 5.3 Computational modeling
- 5.3.1 Geometric modeling
- 5.3.2 Mathematical modeling
- 5.4 Mesh generation and boundary conditions
- 5.5 Results and analysis
- 5.6 Conclusions and future work
- 5.6.1 Conclusions
- 5.6.2 Future work
- References
- Chapter 6 Quasi-Gaussian fuzzy sets and approximation
- Abstract
- Keywords
- 6.1 Introduction
- 6.2 Quasi-Gaussian fuzzy sets
- 6.2.1 Definition of one-dimensional quasi-Gaussian fuzzy membership function
- 6.2.2 Two-dimensional quasi-Gaussian fuzzy set
- 6.2.3 Type-2 quasi-Gaussian fuzzy set
- 6.3 Construction of quasi-Gaussian fuzzy system
- 6.3.1 Improvement of fuzzy rules
- 6.3.2 Development process of quasi-Gaussian fuzzy system
- 6.4 Design of quasi-Gaussian fuzzy systems
- 6.4.1 Definition of rectangular grid QGFS
- 6.4.2 The universal approximation of QGFS system
- 6.5 Approximation results and analysis
- 6.5.1 Approximation with one-dimensional QGFS
- 6.5.2 Approximation with two-dimensional QGFS
- 6.5.3 QGFS approaching with case study
- 6.6 Conclusions and future work
- 6.6.1 Conclusions
- 6.6.2 Future work
- References
- Chapter 7 Type-2 quasi-Gaussian fuzzy systems
- Abstract
- Keywords
- 7.1 Universal approximation of fuzzy systems
- 7.2 Type-2 fuzzy systems
- 7.3 Hierarchical fuzzy systems (HFS)
- 7.4 Quasi-Gaussian fuzzy systems
- 7.4.1 Quasi-Gaussian fuzzy set
- 7.4.2 Improvement of fuzzy rules
- 7.4.3 The construction process of quasi-Gaussian fuzzy system
- 7.4.4 Design of Quasi-Gaussian fuzzy system
- 7.4.5 Universal approximation of QGFS system
- 7.4.6 TS-type QGFS
- 7.5 State-space model based on QGFS
- 7.6 Summary
- References
- Part 3: Intelligent control methods of dynamic spatially distributed systems
- Chapter 8 General control methods of depyrogenation tunnel
- Abstract
- Keywords
- 8.1 Introduction
- 8.2 Pole placement control method
- 8.2.1 General introduction
- 8.2.2 Methodologies
- 8.2.3 Pole placement design
- 8.2.4 Modeling results and analysis
- 8.2.5 Conclusions and future work
- 8.3 Mamdani fuzzy control with decoupling method
- 8.3.1 Introduction
- 8.3.2 Background and methodology
- 8.3.3 Control system design
- 8.3.4 Performance analysis
- 8.3.5 Conclusions and future work
- 8.4 Fuzzy control of depyrogenation tunnel based on interval optimization of fuzzy sets
- 8.4.1 Introduction
- 8.4.2 Control system design and simulation
- 8.4.3 Results analysis
- 8.4.4 Conclusions and future work
- 8.5 Pharmaceutical equipment application and monohydrate s-nitrosocaptopril drying process in pilot plant experiments
- References
- Chapter 9 Hierarchical fuzzy control of the depyrogenation tunnel
- Abstract
- Keywords
- 9.1 Introduction
- 9.2 Two-dimensional fuzzy control system design and optimization
- 9.2.1 Designing a hybrid control system based on two-dimensional fuzzy logic
- 9.2.2 Design results and analysis
- 9.3 Type-2 hierarchical fuzzy control system design and simulation
- 9.3.1 Fuzzification of inputs and outputs
- 9.3.2 Analysis of control system results
- 9.4 Optimization and analysis of fuzzy subset intervals based on intelligence optimization algorithms
- 9.4.1 Introduction to the intelligent algorithms used
- 9.4.2 Optimization design methodology
- 9.4.3 Optimization results and analysis
- 9.5 Analysis of optimization results
- 9.6 Summary
- References
- Chapter 10 Conclusions and future prospects
- Abstract
- Keywords
- 10.1 Conclusions
- 10.2 Future prospects
- Index
- No. of pages: 316
- Language: English
- Edition: 1
- Published: November 8, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780323953924
- eBook ISBN: 9780323953917
YW
Yizhi Wang
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.
Affiliations and expertise
Associate Professor, College of Intelligent Science and Control Engineering, Jinling Institute of Technology, and Post Doc. Fellow, Nanjing Agricultural UniversityZY
Zhong Yang
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.
Affiliations and expertise
Former Dean, College of Intelligent Science and Control Engineering, Jinling Institute of TechnologyRead Modeling and Control of Dynamic Spatially Distributed Systems on ScienceDirect