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.
1. Chapter 1. Introduction - p. 3 2. Chapter 2. Modeling and optimal control framework for the solution purification process - p. 15 3. Part II: Modeling and optimal control of the copper removal process - p. 35 4. Chapter 3. Kinetic modeling of the competitive-consecutive reaction system - p. 39 5. Chapter 4. Additive requirement ratio estimation using trend distribution features - p. 63 6. Chapter 5. Real-time adjustment of zinc powder dosage based on fuzzy logic - p. 83 7. Part III: Modeling and optimal control of the cobalt removal process - p. 105 8. Chapter 6. Integrated modeling of the cobalt removal process - p. 109 9. Chapter 7. Intelligent optimal setting control of the cobalt removal process - p. 141 10. Chapter 8. Control of the cobalt removal process under multiple working conditions - p. 171 11. Abstract - p. 171 12. Contents - p. page171 13. 8.1 Problem analysis - p. page171 14. 8.2 Robust adaptive control under model–plant mismatch - p. 172 15. 8.3 Adaptive dynamic programming for working conditions with unknown model parameters - p. page183 16. References - p. 198 17. Part IV: System development and future research - p. 199 18. Chapter 9. Intelligent control system development - p. 203 19. Chapter 10. Conclusions and future research - p. 213 20. Index - p. 223
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.
1. Chapter 1. Introduction - p. 3 2. Chapter 2. Modeling and optimal control framework for the solution purification process - p. 15 3. Part II: Modeling and optimal control of the copper removal process - p. 35 4. Chapter 3. Kinetic modeling of the competitive-consecutive reaction system - p. 39 5. Chapter 4. Additive requirement ratio estimation using trend distribution features - p. 63 6. Chapter 5. Real-time adjustment of zinc powder dosage based on fuzzy logic - p. 83 7. Part III: Modeling and optimal control of the cobalt removal process - p. 105 8. Chapter 6. Integrated modeling of the cobalt removal process - p. 109 9. Chapter 7. Intelligent optimal setting control of the cobalt removal process - p. 141 10. Chapter 8. Control of the cobalt removal process under multiple working conditions - p. 171 11. Abstract - p. 171 12. Contents - p. page171 13. 8.1 Problem analysis - p. page171 14. 8.2 Robust adaptive control under model–plant mismatch - p. 172 15. 8.3 Adaptive dynamic programming for working conditions with unknown model parameters - p. page183 16. References - p. 198 17. Part IV: System development and future research - p. 199 18. Chapter 9. Intelligent control system development - p. 203 19. Chapter 10. Conclusions and future research - p. 213 20. Index - p. 223
