Applied AI Techniques in the Process Industry
From Molecular Design to Process Design and Optimization
Data-driven and first principles models for energy-relevant systems and processes approached through various in-depth case studies.
Chapter 1: Integrating Data-Driven Modeling with First-Principles Knowledge
Chapter 1: Integrating Data-Driven Modeling with First-Principles Knowledge
Chapter 2: Advanced algorithms for Hybrid Data-driven Modelling
Chapter 3: A computational Framework for Model-based Design and Optimization of Dynamic and Cyclic Membrane Processes
Chapter 4: AI-Aided Optimization and Design of MOF Materials for Gas Separation
Chapter 5: Machine Learning Aided Materials and Process Integration Design for High-Efficiency Gas Separation
Chapter 6: Data-driven Screening of High-performance Ionic Liquids
Chapter 7: Hunting for Aromatic Chemicals with AI Techniques
Chapter 8: AI-assisted Drug Design and Production
Chapter 9: Designing a Heat Exchanger by Combining Physics-Informed Deep Learning and Transfer Learning
Chapter 10: Catalyst Design Based on Machine Learning
Chapter 11: Surrogate Models for Sustainability Optimization of Complex Industrial System
Chapter 12: Advanced Machine Learning and Deep Learning Models for Chemical Process Control and Process Data Analytics
| ISBN | 9783527353392 |
|---|---|
| Artikelnummer | 9783527353392 |
| Medientyp | Buch |
| Auflage | 1. Auflage |
| Copyrightjahr | 2025 |
| Verlag | Wiley-VCH |
| Umfang | 336 Seiten |
| Abbildungen | 33 Tabellen |
| Sprache | Englisch |
