Modeling, Optimization and Control of Zinc Hydrometallurgical Purification Process provides a clear picture on how to develop a mathematical model for complex industrial processes, how to design the optimization strategy, and how to apply control methods in order to achieve desired production target. This book shares the authors' recent ideas/methodologies/algorithms on the intelligent manufacturing of complex industry processes, e.g., how to develop a descriptive framework which could enable the digitalization and visualization of a process and how to develop the controller when the process model is not available.
- Presents an extended state-space descriptive framework for complex industrial processes
- Presents scientific problems extracted from real industrial process
- Proposes novel modeling and control tools for intelligent manufacturing of continuous industries
Chunhua Yang has served as a subject matter expert in Advanced Manufacturing Technology (863 Program), a member of Chinese Association of Automation (CAA), a member of the Process Control Technical Committee of CAA, a member of the Technical Committee of Component and Instrument of CAA, a member of the Technical Committee on Control Theory of CAA, Secretary-General of Computer Science Committee in nonferrous Metals Society of China, and Vice-Chair of the IFAC TC 6.2 Mining, Mineral and Metal Processing. She also serves as an associate editor for several journals including Acta Automatica Sinica, Control Theory & Applications, etc. She served as the Chair of the National Organizing Committee in the 5th IFAC workshop on Mining, Mineral and Metal Processing held in Shanghai, August 2018. Prof. Yang has won many prestigious awards and honours, including the Second Prize of National Science and Technology Progress for 3 times
Part I Background 1. Introduction 2. Modeling and optimal control framework for the solution purification process
Part II Modeling and optimal control of the copper removal process 3. Kinetic modeling of the competitive-consecutive reaction system 4. Additive requirement ratio estimation using trend distribution features 5. Real-time adjustment of zinc powder dosage based on fuzzy logic
Part III Modeling and optimal control of the cobalt removal process 6. Integrated modeling of the cobalt removal process 7. Intelligent optimal setting control of the cobalt removal process 8. Control of the cobalt removal process under multiple working conditions
Part IV System development and future research 9. Intelligent control system development 10. Conclusions and future research
