Application of Neural Networks and Other Learning Technologies In Process Engineering.

This book is a follow-up to the IChemE symposium on "Neural Networks and Other Learning Technologies", held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been wr...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico eBook
Idioma:Inglés
Publicado: World Scientific 2001.
Temas:
Neural networks (Computer science)
Process control.
Manufacturing processes.
Neural Networks, Computer
Réseaux neuronaux (Informatique)
Fabrication > Contrôle.
Fabrication.
manufacturing.
Electronic resource.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Contents
  • Foreword
  • Acknowledgements
  • Part I: Modelling and Identification
  • 1. Simulation of Liquid-Liquid Extraction Data with Artificial Neural Networks
  • 2. RBFN Identification of an Industrial Polymerization Reactor Model
  • 3. Process Identification with Self-Organizing Networks
  • 4. Training Radial Basis Function Networks for Process Identification with an Emphasis on the Bayesian Evidence Approach
  • 5. Process Identification of a Fed-Batch Penicillin Production Process 8212; Training with the Extended Kalman Filter
  • Part II: Hybrid Schemes
  • 6. Combining Neural Networks and First Principle Models for Bioprocess Modeling
  • 7. Neural Networks in a Hybrid Scheme for Optimisation of Dynamic Processes: Application to Batch Distillation
  • 8. Hierarchical Neural Fuzzy Models as a Tool for Process Identification: A Bioprocess Application
  • Part III: Estimation and Control
  • 9. Adaptive Inverse Model Control of a Continuous Fermentation Process Using Neural Networks
  • 10. Set Point Tracking in Batch Reactors: Use of PID and Generic Model Control with Neural Network Techniques
  • 11. Inferential Estimation and Optimal Control of a Batch Polymerisation Reactor Using Stacked Neural Networks
  • Part IV: New Learning Technologies
  • 12. Reinforcement Learning in Batch Processes
  • 13. Knowledge Discovery through Mining Process Operational Data
  • Part V: Experimental and Industrial Applications
  • 14. Use of Neural Networks for Process Control. Experimental Applications
  • 15. Intelligent Modeling and Optimization of Process Operations Using Neural Networks and Genetic Algorithms: Recent Advances and Industrial Validation.

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