Modeling, Optimization, and Control of Zinc Hydrometallurgical Purification Process: Emerging Methodologies and Applications in Modelling, Identification and Control
Autor Chunhua Yang, Bei Sunen Limba Engleză Paperback – 28 ian 2021
- 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
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Specificații
ISBN-13: 9780128195925
ISBN-10: 0128195924
Pagini: 244
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 x 15 mm
Greutate: 0.34 kg
Editura: ELSEVIER SCIENCE
Seria Emerging Methodologies and Applications in Modelling, Identification and Control
ISBN-10: 0128195924
Pagini: 244
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 x 15 mm
Greutate: 0.34 kg
Editura: ELSEVIER SCIENCE
Seria Emerging Methodologies and Applications in Modelling, Identification and Control
Public țintă
Graduate students majoring in process control and automation; Control engineers in the process industries; Academic researchers/lecturers for teaching and research referenceCuprins
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
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